The 13 Best Machine Learning Courses and Online Training for 2020 – Solutions Review

The editors at Solutions Review have compiled this list of the best machine learning courses and online training to consider for 2020.

Machine learning involves studying computer algorithms that improve automatically through experience. It is a sub-field of artificial intelligence where machine learning algorithms build models based on sample (or training) data. Once a predictive model is constructed it can be used to make predictions or decisions without being specifically commanded to do so. Machine learning is now a mainstream technology with a wide variety of uses and applications. It is especially prevalent in the fields of business intelligence and data management.

With this in mind, weve compiled this list of the best machine learning courses and online training to consider if youre looking to grow your AI or data science skills for work or play. This is not an exhaustive list, but one that features the best machine learning courses and training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well. Click Go to training to learn more and register.

Platform: Coursera

Description: This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

Related paths/tracks: Machine Learning with Python (IBM), Machine Learning Specialization (University of Washington),Mathematics for Machine Learning Specialization (Imperial College London), Machine Learning with TensorFlow on Google Cloud Platform Specialization (Google Cloud)

Platform: DataCamp

Description: In this non-technical course, youll learn everything youve been too afraid to ask about machine learning. Theres no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions. How does machine learning work, when can you use it, and what is the difference between AI and machine learning? Theyre all covered.

Related paths/tracks: Machine Learning for Business, Machine Learning with Tree-Based Models in Python, Machine Learning with caret in R

Platform: Edureka

Description: Edurekas Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Nave Bayes and Q-Learning. This training exposes you to concepts of statistics, time series and different classes of machine learning algorithms like supervised, unsupervised, and reinforcement algorithms. Throughout the course, youll be solving real-life case studies on media, healthcare, social media, aviation, and HR.

Related paths/tracks:Graphical Models Certification Training, Reinforcement Learning, Natural Language Processing with Python

Platform: edX

Description: Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

Related paths/tracks: Machine Learning for Data Science and Analytics (Columbia), Machine Learning Fundamentals (UC San Diego), Machine Learning with Python: from Linear Models to Deep Learning

Platform: Experfy

Description: As an introduction to machine learning, this course is presented at a level that is readily understood by all individuals interested in machine learning. This course provides a history of machine learning, defines data, and explains what is meant by big data; and classifies data in terms of computer programming. It covers the basic concept of numeral systems and the common numeral systems used by computer hardware to establish programming languages. Providing practical applications of machine learning.

Related paths/tracks: Machine Learning for Predictive Analytics, Feature Engineering for Machine Learning, Supervised Learning: Classification, Supervised Learning: Linear Regression, Unsupervised Learning: Clustering

Platform: Intellipaat

Description: This machine learning course will help you master the skills required to become an expert in this domain. Master skills such as Python, ML algorithms, statistics, supervised and unsupervised learning, etc. to become a successful professional in this popular technology. Intellipaats machine learning certification training comes with 24/7 support, multiple assignments, and project work to help you gain real-world exposure.

Related path/track: Artificial Intelligence Course and Training

Platform: LinkedIn Learning

Description: In this course, we review the definition and types of machine learning: supervised, unsupervised, and reinforcement. Then you can see how to use popular algorithms such as decision trees, clustering, and regression analysis to see patterns in your massive data sets. Finally, you can learn about some of the pitfalls when starting out with machine learning.

Related paths/tracks: Essential Math for Machine Learning: Python Edition, Applied Machine Learning: Algorithms, Applied Machine Learning Foundations

Platform: Mindmajix

Description: Mindmajix Machine Learning Training will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. You will gain in-depth knowledge of all the concepts of machine learning including supervised and unsupervised learning, algorithms, support vector machines, etc.,through real-time industry use cases, and this will help you in clearing the Machine Learning Certification Exam.

Related path/track: Machine Learning with Python Training

Platform: Pluralsight

Description: Have you ever wondered what machine learning is? Thats what this course is designed to teach you. Youll explore the open-source programming language R, learn about training and testing a model as well as using a model. By the time youre done, youll have a clear understanding of exactly what machine learning is all about.

Related paths/tracks: Understanding Machine Learning with Python, Understanding Machine Learning with R, Machine Learning: Executive Briefing, How Machine Learning Works, Deploying Machine Learning Solutions

Platform: Simplilearn

Description: This machine learning online course offers an in-depth overview of machine learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time-series modeling. Learn how to use Python in this machine learning certification training to draw predictions from data.

Platform: Skillshare

Description: If youve got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry and prepare you for a move into this hot career path. This comprehensive course includes68 lecturesspanning almost9 hours of video, and most topics includehands-on Python code examplesyou can use for reference and for practice.

Related paths/tracks:Demystifying Artificial Intelligence: Understanding Machine Learning, Goal-Driven Artificial Intelligence and Machine Learning

Platform: Udacity

Description: Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in the industry.

Related paths/tracks: Intro to Machine Learning with PyTorch,Intro to Machine Learning with TensorFlow

Platform: Udemy

Description: This course has been designed by two professional data scientists that can share their knowledge andhelp you learn complex theory, algorithms, and coding libraries in a simple way. The course will walk you step-by-step into the world of machine learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of data science.

Related paths/tracks:Python for Data Science and Machine Learning Bootcamp, Machine Learning, Data Science and Deep Learning with Python,Data Science and Machine Learning Bootcamp with R

Timothy is Solutions Review's Senior Editor. He is a recognized thought leader and influencer in enterprise BI and data analytics. Timothy has been named a top global business journalist by Richtopia. Scoop? First initial, last name at solutionsreview dot com.

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The 13 Best Machine Learning Courses and Online Training for 2020 - Solutions Review

Why organisations are poised to embrace machine learning – IT Brief Australia

Article by Snowflake senior sales engineer Rishu Saxena.

Once a technical novelty seen only in software development labs or enormous organisations, machine learning (ML) is poised to become an important tool for large numbers of Australian and New Zealand businesses.

Lured by promises of improved productivity and faster workflows, companies are investing in the technology in rising numbers. According to research firm Fortune Business Insights, the ML market will be worth US$117.19 billion by 2027.

Historically, ML was perceived to be an expensive undertaking that required massive upfront investment in people, as well as both storage and compute systems. Recently, many of the roadblocks that had been hindering adoption have now been removed.

One such roadblock was not having the right mindset or strategy when undertaking ML-related projects. Unlike more traditional software development, ML requires a flexible and open-ended approach. Sometimes it wont be possible to assess the result accurately, and this could well change during deployment and preliminary use.

A second roadblock was the lack of ML automation tools available on the market. Thanks to large investments and hard work by computer scientists, the latest generation of auto ML tools are feature-rich, intuitive and affordable.

Those wanting to put them to work no longer have to undertake extensive data science training or have a software development background. Dubbed citizen data scientists, these people can readily experiment with the tools and put their ideas into action.

The way data is stored and accessed by ML tools has also changed. Advances in areas such as cloud-based data warehouses and data lakes means an organisation can now have all its data in a single location. This means the ML tools can scan vast amounts of data relatively easily, potentially leading to insights that previously would have gone unnoticed.

The lowering of storage costs has further assisted this trend. Where an organisation may have opted to delete or archive data onto tape, that data can now continue to be stored in a production environment, making it accessible to the ML tools.

For those organisations looking to embrace ML and experience the business benefits it can deliver, there are a series of steps that should be followed:

When starting with ML, dont try to run before you walk. Begin with small, stand-alone projects that give citizen data scientists a chance to become familiar with the machine learning process, the tools, how they operate, and what can be achieved. Once this has been bedded down, its then easier to gradually increase the size and scope of activities.

To start your ML journey, lean on the vast number of auto ML tools available on the market instead of using open source notebook based IDEs that require high levels of skills and familiarity with ML.

There is an increasing number of ML tools on the market, so take time to evaluate options and select the ones best suited to your business goals. This will also give citizen data scientists required experience before any in-house development is undertaken.

ML is not something that has to be the exclusive domain of the IT department. Encourage the growth of a pool of citizen data scientists within the organisation who can undertake projects and share their growing knowledge.

To enable ML tools to do as much as possible, centralise the storage of all data in your organisation. One option is to make use of a cloud-based data platform that can be readily scaled as data volumes increase.

Once projects have been underway for some time, closely monitor the results being achieved. This will help to guide further investments and shape the types of projects that will be completed in the future.

Once knowledge and experience levels within the organisation have increased, consider tackling more complex projects. These will have the potential to add further value to the organisation and ensure that stored data is generating maximum value.

The potential for ML to support organisations, help them to achieve fresh insights, and streamline their operations is vast. By starting small and growing over time, its possible to keep costs under control while achieving benefits in a relatively short space of time.

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Why organisations are poised to embrace machine learning - IT Brief Australia

The Convergence of RPA and Automated Machine Learning – AiiA

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The future is now. We've been discussing the fact that RPA truly transforms the costs, accuracy, productivity, speed and efficiency of your enterprise. That transformation is all the more powerful with cognitive solutions baked-in.

Our old friends at Automation Anywhere combine forces with our new friends at DataRobot to discuss the integration and convergence of RPA & Automated ML and how that combination can hurdle your enterprise further through this fourth industrial revolution.

Watch the session on demand now.

The Convergence of RPA and Automated Machine LearningGreg van Rensburg, Director, Solutions Consulting,Automation AnywhereColin Priest, Vice President, AI Strategy,DataRobot

Robotic Process Automation (RPA) has disrupted repetitive business processes across a variety of industries. The combination of RPA, cognitive automation, and analytics is a game changer for unstructured data-processing and for gaining real-time insights. The next frontier? A truly complete, end-to-end process automation with AI-powered decision-making and predictive abilities. Join Automation Anywhere and DataRobot at this session to learn how organisations are using business logic and structured inputs, through a combination of RPA and Automated Machine Learning, to automate business processes, reduce customer churn and transform to digital operating models.

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The Convergence of RPA and Automated Machine Learning - AiiA

Machine Learning Operationalization Software Market Size, Trends, Analysis, Demand, Outlook And Forecast 2027 The Mathworks, Inc, Sas Institute Inc,…

Machine Learning Operationalization Software market research report bestows clients with the best results and for the same, it has been produced by using integrated approaches and the latest technology. With this market report, it becomes easier to establish and optimize each stage in the lifecycle of an industrial process that includes engagement, acquisition, retention, and monetization. This market report gives a wide-ranging analysis of the market structure and the evaluations of the various segments and sub-segments of this industry. Not to mention, several charts and graphs have been used effectively in the Machine Learning Operationalization Software Market report to represent the facts and figures in a proper way.

In this winning Machine Learning Operationalization Software market research report, industry trends are plotted on macro-level which helps clients and the businesses comprehend the market place and possible future issues. In this business report, market drivers and market restraints are studied carefully along with the analysis of the market structure. In no doubt, businesses are significantly relying on the different segments covered in the market research report hence Machine Learning Operationalization Software market document presents them with better insights to drive the business into the right direction. The report also offers great inspiration to seek new business ventures and evolve better.

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Major Industry Competitors:Machine Learning Operationalization Software Market

The Major Players Covered In The Machine Learning Operationalization Software Report Are The Mathworks, Inc, Sas Institute Inc, Microsoft, Parallelm, Inc, Algorithmia Inc, Tibco Software Inc, Sap, Ibm Corporation, Seldon Technologies Ltd, Actico Gmbh, Rapidminer, Inc And Knime Ag Among Other Domestic And Global Players. Market Share Data Is Available For Global, North America, Europe, Asia-Pacific, Middle East And Africa And South America Separately. Dbmr Analysts Understand Competitive Strengths And Provide Competitive Analysis For Each Competitor Separately.

Market Analysis:Machine Learning Operationalization Software Market

Machine Learning Operationalization Software Market Is Expected To Gain Market Growth In The Forecast Period Of 2020 To 2027. Data Bridge Market Research Analyses The Market Growing At A Cagr Of 44.2% In The Above-Mentioned Forecast Period.

The 2020 Annual Machine Learning Operationalization Software Market offers:

=> 100+ charts exploring and analysing the Machine Learning Operationalization Software market from critical angles including retail forecasts, consumer demand, production and more=> 10+ profiles of top Machine Learning Operationalization Software producing states, with highlights of market conditions and retail trends=> Regulatory outlook, best practices, and future considerations for manufacturers and industry players seeking to meet consumer demand=> Benchmark wholesale prices, market position, plus prices for raw materials involved in Machine Learning Operationalization Software type

Some extract from Table of Contents

Overview of Global Machine Learning Operationalization Software Market

Machine Learning Operationalization Software Size (Sales Volume) Comparison by Type

Machine Learning Operationalization Software Size (Consumption) and Market Share Comparison by Application

Machine Learning Operationalization Software Size (Value) Comparison by Region

Machine Learning Operationalization Software Sales, Revenue and Growth Rate

Machine Learning Operationalization Software Competitive Situation and Trends

Strategic proposal for estimating availability of core business segments

Players/Suppliers, Sales Area

Analyse competitors, including all important parameters of Machine Learning Operationalization Software

Global Machine Learning Operationalization Software Manufacturing Cost Analysis

The most recent innovative headway and supply chain pattern mapping

Get Detailed TOC with Tables and Figures @ https://www.databridgemarketresearch.com/toc/?dbmr=global-machine-learning-operationalization-software-market&skp

Rapid Business Growth Factors

In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor thats helping the market grow faster than usual is the tough competition.

Key Points of this Report:

The depth industry chain includes analysis value chain analysis, porter five forces model analysis and cost structure analysis

It describes present situation, historical background and future forecast

Comprehensive data showing Machine Learning Operationalization Software capacities, production, consumption, trade statistics, and prices in the recent years are provided

The report indicates a wealth of information on Machine Learning Operationalization Software manufacturers

Machine Learning Operationalization Software market forecasts for next five years, including market volumes and prices is also provided

Raw Material Supply and Downstream Consumer Information is also included

Any other users requirements which is feasible for us

What Porters Five Forces of Competitive Analysis Provides?

Supplier power: An assessment of how easy it is for suppliers to drive up prices. This is driven by the: number of suppliers of each essential input; uniqueness of their product or service; relative size and strength of the supplier; and cost of switching from one supplier to another.

Buyer power: An assessment of how easy it is for buyers to drive prices down. This is driven by the: number of buyers in the market; importance of each individual buyer to the organisation; and cost to the buyer of switching from one supplier to another. If a business has just a few powerful buyers, they are often able to dictate terms.

Competitive rivalry: The main driver is the number and capability of competitors in the market. Many competitors, offering undifferentiated products and services, will reduce market attractiveness.

Threat of substitution: Where close substitute products exist in a market; it increases the likelihood of customers switching to alternatives in response to price increases. This reduces both the power of suppliers and the attractiveness of the market.

Threat of new entry: Profitable markets attract new entrants, which erodes profitability. Unless incumbents have strong and durable barriers to entry, for example, patents, economies of scale, capital requirements or government policies, then profitability will decline to a competitive rate.

Five forces analysis helps organizations to understand the factors affecting profitability in a specific industry, and can help to inform decisions relating to: whether to enter a specific industry; whether to increase capacity in a specific industry; and developing competitive strategies.

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Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe, MEA or Asia Pacific.

Why Is Data TriangulationImportantin Qualitative Research?

This involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, other data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Top to Bottom Analysis and Vendor Share Analysis. Triangulation is one method used while reviewing, synthesizing and interpreting field data. Data triangulation has been advocated as a methodological technique not only to enhance the validity of the research findings but also to achieve completeness and confirmation of data using multiple methods

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Machine Learning Operationalization Software Market Size, Trends, Analysis, Demand, Outlook And Forecast 2027 The Mathworks, Inc, Sas Institute Inc,...

What is Quantum Computing, and How does it Help Us? – Analytics Insight

The term quantum computing gained momentum in the late 20thcentury. These systems aim to utilize these capabilities to become highly-efficient. They use quantum bits or qubits instead of the simple manipulation of ones and zeros in existing binary-based computers. These qubits also have a third state called superposition that simultaneously represents a one or a zero. Instead of analyzing a one or a zero sequentially, superposition allows two qubits in superposition to represent four scenarios at the same time. So we are at the cusp of a computing revolution where future systems have capability beyond mathematical calculations and algorithms.

Quantum computers also follow the principle of entanglement, which Albert Einstein had referred to as spooky action at a distance. Entanglement refers to the observation that the state of particles from the same quantum system cannot be described independently of each other. Even when they are separated by great distances, they are still part of the same system.

Several nations, giant tech firms, universities, and startups are currently exploring quantum computing and its range of potential applications. IBM, Google, Microsoft, Amazon, and other companies are investing heavilyin developing large-scale quantum computing hardware and software. Google and UCSB have a partnership to develop a 50 qubits computer, as it would represent 10,000,000,000,000,000 numbers that would take a modern computer petabyte-scale memory to store. A petabyte is the unit above a terabyte and represents 1,024 terabytes. It is also equivalent to 4,000 digital photos taken every day. Meanwhile, names like Rigetti Computing, D-Wave Systems, 1Qbit Information Technologies, Inc., Quantum Circuits, Inc., QC Ware, Zapata Computing, Inc. are emerging as bigger players in quantum computing.

IEEE Standards Association Quantum Computing Working Group is developing two technical standards for quantum computing. One is for quantum computing definitions and nomenclature, so we can all speak the same language. The other addresses performance metrics and performance benchmarking to measure quantum computers performance against classical computers and, ultimately, each other. If required, new standards will also be added with time.

The rapid growth in the quantum tech sector over the past five years has been exciting. This is because quantum computing presents immense potential. For instance, a quantum system can be useful for scientists for conducting virtual experiments and sifting through vast amounts of data. Quantum algorithms like quantum parallelism can perform a large number of computations simultaneously. In contrast, quantum interference will combine their results into something meaningful and can be measured according to quantum mechanics laws. Even Chinese scientists are looking to developquantum internet, which shall be a more secure communication system in which information is stored and transmitted withadvanced cryptography.

Researchers at Case Western Reserve University used quantum algorithms to transform MRI scans for cancer, allowing the scans to be performed three times faster and to improve their quality by 30%. In practice, this can mean patients wont need to be sedated to stay still for the length of an MRI, and physicians could track the success of chemotherapy at the earliest stages of treatment.

Laboratoire de Photonique Numrique et Nanosciences of France has built a hybrid device that pairs a quantum accelerometer with a classical one and uses a high-pass filter to subtract the classical data from the quantum data. This has the potential to offer an highly precise quantum compass that would eliminate the bias and scale factor drifts commonly associated with gyroscopic components. Meanwhile, the University of Bristolhas founded a quantum solution for increasing security threats. Researchers at the University of Virginia School of Medicine are working to uncover the potential quantum computers hold to help understand genetic diseases.Scientists are also using quantum computing to find a vaccine for COVID and other life-threatening diseases.

In July 2017, in collaboration with commercial photonics tools providerM Squared, QuantIC demonstrated how a quantum gravimeter detects the presence of deeply hidden objects by measuring disturbances in the gravitational field. If such a device becomes practical and portable, the team believes it could become invaluable in an early warning system for predicting seismic events and tsunamis.

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What is Quantum Computing, and How does it Help Us? - Analytics Insight

Quantum computing: Photon startup lights up the future of computers and cryptography – ZDNet

A fast-growing UK startup is quietly making strides in the promising field of quantum photonics. Cambridge-based company Nu Quantum is building devices that can emit and detect quantum particles of light, called single photons. With a freshly secured 2.1 million ($2.71 million) seed investment, these devices could one day underpin sophisticated quantum photonic systems, for applications ranging from quantum communications to quantum computing.

The company is developing high-performance light-emitting and light-detecting components, which operate at the single-photon level and at ambient temperature, and is building a business based on the combination of quantum optics, semiconductor photonics, and information theory, spun out of the University of Cambridge after eight years of research at the Cavendish Laboratory.

"Any quantum photonic system will start with a source of single photons, and end with a detector of single photons," Carmen Palacios-Berraquero, the CEO of Nu Quantum, tells ZDNet. "These technologies are different things, but we are bringing them together as two ends of a system. Being able to controllably do that is our main focus."

SEE: Hiring Kit: Computer Hardware Engineer (TechRepublic Premium)

As Palacios-Berraquero stresses, even generating single quantum particles of light is very technically demanding.

In fact, even the few quantum computers that exist today, which were designed by companies such as Google and IBM, rely on the quantum states of matter, rather than light. In other words, the superconducting qubits that can be found in those tech giants' devices rely on electrons, not photons.

Yet the superconducting qubits found in current quantum computers are, famously, very unstable. The devices have to operate in temperatures colder than those found in deep space to function, because thermal vibrations can cause qubits to fall from their quantum state. On top of impracticality, this also means that it is a huge challenge to scale up the number of qubits in the computer.

A photonic quantum computer could have huge advantages over its matter-based counterpart. Photons are much less prone to interact with their environment, which means they can retain their quantum state for much longer and over long distances. A photonic quantum computer could, in theory, operate at room temperature and as a result, scale up much faster.

The whole challenge comes from creating the first quantum photon, explains Palacios-Berraquero. "Being able to emit one photon at a time is a ground-breaking achievement. In fact, it has become the Holy Grail of quantum optics."

"But I worked on generating single photons for my PhD. That's the IP I brought to the table."

Carmen Palacios-Berraquero and the Nu Quantum team just secured a 2.1 million ($2.71 million) seed investment.

Combined with improved technologies in the fields of nanoscale semi-conductor fabrication, Palacios-Berraquero and her team set off to crack the single-photon generation problem.

Nu Quantum's products come in the form of two little boxes: the first one generates the single photons that can be used to build quantum systems for various applications, and the other measures the quantum signals emitted by the first one. The technology, maintains the startup CEO, is bringing quantum one step closer to commercialization and adoption.

"Between the source and the detector of single photons, many things can happen, from the simplest to the most complex," explains Palacios-Berraquero. "The most complex one being a photonic quantum computer, in which you have thousands of photons on one side and thousands of detectors on the other. And in the middle, of course, you have gates, and entanglement, and and, and and. But that's the most complex example."

A photonic quantum computer is still a very long-term ambition of the startup CEO. A simpler application, which Nu Quantum is already working on delivering commercially with the UK's National Physical Laboratory, is quantum random number generation a technology that can significantly boost the security of cryptographic keys that secure data.

The keys that are currently used to encrypt the data exchanged between two parties are generated thanks to classical algorithms. Classical computing is deterministic: a given input will always produce the same output, meaning that complete randomness is fundamentally impossible. As a result, classical algorithms are predictable to an extent. In cryptography, this means that security keys can be cracked fairly easily, given sufficient computing power.

Not so much with quantum. A fundamental property of quantum photons is that they behave randomly: for example, if a single photon is sent down a path that separates in two ways, there is no way of knowing deterministically which way the particle will choose to go through.

SEE: What is the quantum internet? Everything you need to know about the weird future of quantum networks

The technology that Nu Quantum is developing with the National Physical Laboratory, therefore, consists of a source of single photons, two detectors, and a two-way path linking the three devices. "If we say the right detector is a 1, and the left detector is a 0, you end up with a string of numbers that's totally random," says Palacios-Berraquero. "The more random, the more unpredictable the key is, and the more secure the encryption."

Nu Quantum is now focusing on commercializing quantum random number generation, but the objective is to build up systems that are increasingly complex as the technology improves. Palacios-Berraquero expects that in four or five years, the company will be able to start focusing on the next step.

One day, she hopes, Nu Quantum's devices could be used to connect quantum devices in a quantum internet a decade-long project contemplated by scientists in the US, the EU, and China, which would tap the laws of quantum mechanics to almost literally teleport some quantum information from one quantum device to the next. Doing so is likely to require single photons to be generated and distributed between senders and receivers, because of the light particles' capacity to travel longer distances.

In the shorter term, the startup will be focusing on investing the seed money it has just raised. On the radar, is a brand-new lab and headquarters in Cambridge, and tripling the size of the team with a recruitment drive for scientists, product team members and business functions.

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Quantum computing: Photon startup lights up the future of computers and cryptography - ZDNet

The Coding School, IBM Quantum Provide Free Quantum Education to 5,000 Students Around the World – PRNewswire

LOS ANGELES, Oct. 6, 2020 /PRNewswire/ --The Coding Schoolis collaborating with IBM Quantumto offer a first-of-its-kind quantum computing course for 5,000 high school students and above, designed to make quantum education globally accessible and to provide high-quality virtual STEM education. To ensure an equitable future quantum workforce, the course is free. Students can apply here.

"While quantum computing will revolutionize the world, few opportunities exist to make quantum accessible to K-12 students or the general population today," notes Kiera Peltz, the founder and executive director of The Coding School. "We are proud to collaborate with IBM Quantum, a global leader in quantum computing, to ensure the next generation is equipped with the skills necessary for the future of work."

The course, Qubit by Qubit's Introduction to Quantum Computing, will run for a full academic year, from October 2020 to May 2021, and consists of weekly live lectures, labs, and problem sets. Students are eligible to receive high school course credit for this course. The course is University of California A-G accreditedand is in the process of WASC accreditation. In addition to students registering independently, TCS is working with high schools to offer this course during the school day, making it the first time quantum computing is widely available as a for-credit course at the high school level.

Taught live by MIT and Oxford University quantum scientists, the course has been developed for students with no prior quantum computing experience and introduces students to the foundational concepts of quantum computing, including quantum mechanics, quantum information and computation, and quantum algorithms. Students will work with Qiskit, an open-source quantum software development kit, and the IBM Quantum Experienceplatform to run quantum circuits on real quantum computers. Lead instructors are Francisca Vasconcelos, a Rhodes Scholar and MIT graduate, and Amir Karamlou, a Graduate Fellow in MIT's Engineering Quantum Systems group.

"This year, more than ever before, students and educators are moving beyond the traditional classroom setting to online platforms like The Coding School," said Liz Durst, Director, IBM Quantum & Qiskit Community. "While this is a great challenge, IBM Quantum is excited to sponsor 5,000 studentsfrom around the world who are curious about quantum computing to start learning as early as high school about the fundamentals of how to program real quantum processors. We're proud to be collaborating with the Qubit by Qubit initiative on this Introduction to Quantum Computing course, working together to deliver a community-based approach to learning with our own best educational experts, tools, and resources such as the Qiskit Textbook."

Beyond increasing accessibility to quantum education, TCS and IBM Quantum are dedicated to ensuring the future quantum workforce is diverse and inclusive. Prior quantum courses by TCS have had over 70 percent students from historically underrepresented backgrounds in STEM. For this year-long course, students have already registered from over 60 countries. Students from communities traditionally underrepresented in STEM are strongly encouraged to apply, and high school students will be prioritized.

"I am eager to share my appreciation of this nascent field with students, especially those at the high school level," said Vasconcelos. "Through this TCS and IBM Quantum collaboration, we are training a diverse global cohort of future quantum engineers, researchers, and business leaders."

Apply today:

The course starts on Oct. 18, 2020. Learn more about the program and apply here.

High schools interested in partnering with TCS to offer this program for free as a for-credit or after-school enrichment course should email [emailprotected].

About The Coding School:

About TCS: Qubit by Qubit (QxQ) is an initiative of The Coding School, a 501(c)(3) tech education nonprofit. Founded in 2014, TCS has taught over 15,000 students from 60+ countries how to code. To learn more, visit: http://www.codeconnects.org.

About IBM Quantum

IBM Quantum is an industry-first initiative to build quantum systems for business and science applications. For more information about IBM's quantum computing efforts, please visit ibm.com/quantum.

Media Contact:

Rachel Zuckerman424-310-8999[emailprotected]

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The Coding School, IBM Quantum Provide Free Quantum Education to 5,000 Students Around the World - PRNewswire

4 Reasons Why Now Is the Best Time to Start With Quantum Computing – Medium

Quantum computing is a rapidly developing field, with everyone trying to build the perfect hardware, find new applications for current algorithms, or even develop new algorithms. Because of that, the near-future demand for quantum programmers and researchers will increase shortly.

Many governmental and industrial institutions have set aside substantial funds to develop quantum technologies. The Quantum Daily (TQD) estimated the current market for quantum computing to be around $235 million. This number is predicted to grow substantially to $6.25 billion by 2025.

This incredible amount of funds leads to an increase in the number of academia, government, and industry positions. Almost all technology companies are changing their business model to adapt to when quantum technology makes an impact.

TQD also adds that the U.S. Bureau of Labor Statistics estimates that in 2020 so far, there are around 1.4 million more quantum software development jobs than applicants who can fill them.

In 2019, MIT published an article called Q&A: The talent shortage in quantum computing that addressed the different challenges the field faces right now. Afterward, it developed MIT xPRO, a group addressing the reality that students arent the only people interested in learning about the different aspects of quantum information.

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4 Reasons Why Now Is the Best Time to Start With Quantum Computing - Medium

Canadian quantum computing firms partner to spread the technology – IT World Canada

In a bid to accelerate this countrys efforts in quantum computing, 24 Canadian hardware and software companies specializing in the field are launching an association this week to help their work get commercialized.

Called Quantum Industry Canada, the group says they represent Canadas most commercial-ready technologies, covering applications in quantum computing, sensing, communications, and quantum-safe cryptography.

The group includes Burnaby, B.C., manufacturer D-Wave Systems, Vancouver software developer 1Qbit, Torontos photonic quantum computer maker Xanadu Quantum Technologies, the Canadian division of software maker Zapata Computing, Waterloo, Ont.,-based ISARA which makes quantum-safe solutions and others.

The quantum opportunity has been brewing for many years, association co-chair Michele Mosca of the University of Waterloos Institute for Quantum Computing and the co-founder of two quantum startups, said in an interview, explaining why the new group is starting now. Canadas been a global leader at building up the global opportunity, the science, the workforce, and we didnt want this chance to pass. Weve got over 24 innovative companies, and we wanted to work together to make these companies a commercial success globally.

Its also important to get Canada known as a leader in quantum-related products and services, he added. This will help assure a strong domestic quantum industry as we enter the final stages of quantum readiness.

And while quantum computing is a fundamental new tool, Mosca said, its also important for Canadian organizations to start planning for a quantum computing future, even if the real business value isnt obvious. We dont know exactly when youll get the real business advantage you want to be ready for when quantum computers can give you an advantage.

Adib Ghubril, research director at Toronto-based Info-Tech Research Group, said in an interview creation of such a group is needed. When you want to foster innovation you want to gain critical mass, a certain number of people working in different disciplines it will help motivate them, even maybe compete.

Researchers from startups and even giants like Google, Microsoft, Honeywell and IBM have been throwing billions at creating quantum computers. So are countries, especially China, but also Australia, the U.K., Germany and Switzerland. Many big-name firms are touting projects with experimental equipment, or hybrid hardware that does accelerated computations but dont meet the standard definition of a quantum computer.

True quantum computers may be a decade off, some suggest. Ghubril thinks were 15 years from what he calls reliable, effective quantum computing. Still, last December IDC predicted that by 2023, one-quarter of the Fortune Global 500 will gain a competitive advantage from emerging quantum computing solutions.

Among the recent signposts:

Briefly, quantum computers take the theory of quantum mechanics to change the world of traditional computation of bits represented by zeros and ones. Instead, a bit can be a zero or a one. In a quantum computer, such basic elements are called qubits. With their expected ability to do astonishing fast computations, quantum computers may be able to help pharmaceutical companies create new drugs and nation-states to break encryption protecting government secrets.

Companies are taking different approaches. D-Wave uses a quantum annealing process to make machines it says are suited to solving real-world computing problems today. Xanadu uses what Mosca calls a more circuit-type computing architecture. Theres certainly the potential that some of the nearer-term technologies will offer businesses advantage, especially as they scale.

We know the road towards a full-fledged quantum computer is long. But there are amazing milestones in that direction.

Ghubril says Canada is in the leading pack of countries working on quantum computing. The momentum out of China is enormous, he said, but it looks like the country will focus on using quantum for telecommunications and not business solutions.

From his point of view companies are taking two approaches to quantum computers. Some, like D-Wave, are trying to use quantum ideas to optimize solving modelling problems. The problem is not every problem is an optimization problem, he said. Other companies are trying for the Grand Poobah the real (quantum) computer. So the IBMs of the world are going for the gusto. They want the real deal. They want to solve the material chemistry and biosynthesis and so on. Theyve gone big, but by doing so theyve gone slower. You cant do much on the IBM platform. You can learn a lot, but you cant do much. You can do more on a D-Wave, but you can only do one thing.

Ghburil encourages companies to dabble in the emerging technology.

Thats Infotechs recommendation: Just learn about it. Join a forum, open an account, try a few things. Nobody is going to gain a (financial) competitive advantage. Its a learning advantage.

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Canadian quantum computing firms partner to spread the technology - IT World Canada

Google’s Billion Dollar News, Commercial Quantum Computers And More In This Week’s Top News – Analytics India Magazine

The Dutch and the Finnish are doing their part in shedding the dystopian sci-fi rep that AI gets usually. These European nations often show up on the top when it comes to initiatives that take the human aspect seriously. Now they are at it again. Amsterdam and Helsinki are making moves to make sure that transparency of AI applications is established. Not only that but these cities want their citizens to play an active role going forward. In what can be a more sci-fi sounding announcement, quantum computing industry leader DWave opens up their tech for business applications making it the first to do so. There is more to news, thanks to Google and find out why in this weeks top news brought to you by Analytics India Magazine.

VMware and NVIDIA are coming together to offer an end-to-end enterprise platform for AI along with a new architecture for data center, cloud and edge; services that use NVIDIAs DPUs. We are partnering with NVIDIA to bring AI to every enterprise; a true democratization of one of the most powerful technologies, said Pat Gelsinger, CEO of VMware.

The full stack of AI software available on the NVIDIA NGCTM hub will be integrated into VMware vSphere, VMware Cloud Foundation and VMware Tanzu. This in turn will help accelerate AI adoption across the industru and allows enterprises to deploy AI-ready infrastructure across the data centers, cloud and edge.

On Thursday, Googles CEO Sundar Pichai announced that they would be sparing $1 billion for enabling high quality journalism. In a blog post penned by Pichai, underlined Googles mission to organize the worlds information and make it universally accessible and useful. Googles News Showcase features the editorial curation of award-winning newsrooms to give readers more insight on the stories that matter, and in the process, helps publishers develop deeper relationships with their audiences. Google has already signed partnerships for News Showcase with nearly 200 leading publications across Germany, Brazil, Argentina, Canada, the U.K. and Australia and will soon be expanding to India, Belgium and the Netherlands.

On Tuesday, D-Wave Systems, the Canadian quantum computing company announced the general availability of its next-gen quantum computing platform that flaunt new hardware, software, and tools to enable and accelerate the delivery of in-production quantum computing applications. The company stated that the platform is available in the Leap quantum cloud service and includes the Advantage quantum system, with more than 5000 qubits and 15-way qubit connectivity. In addition to this, there is an expanded hybrid solver service that can run problems with up to one million variables. Together, these services enables users to scale to address real-world problems with enabling businesses to run real-time quantum applications for the first time.

The PyTorch has announced that developers can leverage its libraries on Cloud TPUs. The XLA library, SAID pYtoRCH, has reached general availability (GA) on Google Cloud and supports a broad set of entry points for developers. It has a fast-growing community of researchers from MIT, Salesforce Research, Allen AI and elsewhere who train a wide range of models accelerated with Cloud TPUs and Cloud TPU Pods.

According to PyTorch, the aim of this project was to make it as easy as possible for the PyTorch community to leverage the high performance capabilities that Cloud TPUs offer while maintaining the dynamic PyTorch user experience. To enable this workflow, the team created PyTorch / XLA, a package that lets PyTorch connect to Cloud TPUs and use TPU cores as devices.

Github announced that the code scanning option, CodeQL is now generally available to all developers. With this new option developers get prompts It scans code as its created and surfaces actionable security reviews within pull requests and other GitHub experiences you use everyday, automating security as a part of your workflow. This helps ensure vulnerabilities never make it to production in the first place.Code scanning is powered by CodeQLthe worlds most powerful code analysis engine and will enable developers to use the 2,000+ CodeQL queries created by GitHub and the community, or create custom queries to easily find and prevent new security concerns.

No two palms are alike. Thats the idea behind Amazon One, a new service by the e commerce giant which allows customers to pay with their palm. Contactless payments were all the rage this pandemic and Amazon wants to step up their technology at one of their stores. All you need is a credit card, your mobile number, and of course, your palm. Once youre signed up, you can use your palm to enter, identify, and pay where Amazon One is available. Governments around the world started to ease the restrictions for public spaces like malls and stadiums and services like Amazon One might see a huge rise in demand because touching surfaces is so 2019!

On Monday, Amsterdam and Helsinki launched AI registries to detail how the respective governments use algorithms to deliver services. AI Register is a window into the artificial intelligence systems used by these cities through the register, citizens can get acquainted with the quick overviews of the citys artificial intelligence systems or examine their more detailed information based on your own interests. They can also give feedback and thus participate in building human-centred AI.

I have a master's degree in Robotics and I write about machine learning advancements.email:ram.sagar@analyticsindiamag.com

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Google's Billion Dollar News, Commercial Quantum Computers And More In This Week's Top News - Analytics India Magazine

All together now: Experiments with twisted 2D materials catch electrons behaving collectively – UW News

Engineering | News releases | Research | Science

October 6, 2020

Aerial shot of the University of Washingtons Seattle campus.Mark Stone/University of Washington

Scientists can have ambitious goals: curing disease, exploring distant worlds, clean-energy revolutions. In physics and materials research, some of these ambitious goals are to make ordinary-sounding objects with extraordinary properties: wires that can transport power without any energy loss, or quantum computers that can perform complex calculations that todays computers cannot achieve. And the emerging workbenches for the experiments that gradually move us toward these goals are 2D materials sheets of material that are a single layer of atoms thick.

In a paper published Sept. 14 in the journal Nature Physics, a team led by the University of Washington reports that carefully constructed stacks of graphene a 2D form of carbon can exhibit highly correlated electron properties. The team also found evidence that this type of collective behavior likely relates to the emergence of exotic magnetic states.

Weve created an experimental setup that allows us to manipulate electrons in the graphene layers in a number of exciting new ways, said co-senior author Matthew Yankowitz, a UW assistant professor of physics and of materials science and engineering, as well as a faculty researcher at the UWClean Energy Institute.

Yankowitz led the team with co-senior author Xiaodong Xu, a UW professor of physics and of materials science and engineering. Xu is also a faculty researcher with the UW Molecular Engineering and Sciences Institute, the UW Institute for Nano-Engineered Systems and the Clean Energy Institute.

Since 2D materials are one layer of atoms thick, bonds between atoms only form in two dimensions and particles like electrons can only move like pieces on a board game: side-to-side, front-to-back or diagonally, but not up or down. These restrictions can imbue 2D materials with properties that their 3D counterparts lack, and scientists have been probing 2D sheets of different materials to characterize and understand these potentially useful qualities.

But over the past decade, scientists like Yankowitz have also started layering 2D materials like a stack of pancakes and have discovered that, if stacked and rotated in a particular configuration and exposed to extremely low temperatures, these layers can exhibit exotic and unexpected properties.

Illustration of a moir pattern that emerges upon stacking and rotating two sheets of bilayer graphene. Correlated electronic states with magnetic ordering emerge in twisted double bilayer graphene over a small range of twist angles, and can be tuned with gating and electric field.Matthew Yankowitz

The UW team worked with building blocks of bilayer graphene: two sheets of graphene naturally layered together. They stacked one bilayer on top of another for a total of four graphene layers and twisted them so that the layout of carbon atoms between the two bilayers were slightly out of alignment. Past research has shown that introducing these small twist angles between single layers or bilayers of graphene can have big consequences for the behavior of their electrons. With specific configurations of the electric field and charge distribution across the stacked bilayers, electrons display highly correlated behaviors. In other words, they all start doing the same thing or displaying the same properties at the same time.

In these instances, it no longer makes sense to describe what an individual electron is doing, but what all electrons are doing at once, said Yankowitz.

Its like having a room full of people in which a change in any one persons behavior will cause everyone else to react similarly, said lead author Minhao He, a UW doctoral student in physics and a former Clean Energy Institute fellow.

Quantum mechanics underlies these correlated properties, and since the stacked graphene bilayers have a density of more than 1012, or one trillion, electrons per square centimeter, a lot of electrons are behaving collectively.

Optical microscopy image of a twisted double bilayer graphene device.Matthew Yankowitz

The team sought to unravel some of the mysteries of the correlated states in their experimental setup. At temperatures of just a few degrees above absolute zero, the team discovered that they could tune the system into a type of correlated insulating state where it would conduct no electrical charge. Near these insulating states, the team found pockets of highly conducting states with features resembling superconductivity.

Though other teams have recently reported these states, the origins of these features remained a mystery. But the UW teams work has found evidence for a possible explanation. They found that these states appeared to be driven by a quantum mechanical property of electrons called spin a type of angular momentum. In regions near the correlated insulating states, they found evidence that all the electron spins spontaneously align. This may indicate that, near the regions showing correlated insulating states, a form of ferromagnetism is emerging not superconductivity. But additional experiments would need to verify this.

These discoveries are the latest example of the many surprises that are in store when conducting experiments with 2D materials.

Much of what were doing in this line of research is to try to create, understand and control emerging electronic states, which can be either correlated or topological, or possess both properties, said Xu. There could be a lot we can do with these states down the road a form of quantum computing, a new energy-harvesting device, or some new types of sensors, for example and frankly we wont know until we try.

In the meantime, expect stacks, bilayers and twist angles to keep making waves.

Co-authors are UW researchers Yuhao Li and Yang Liu; UW physics doctoral student and Clean Energy Institute fellow Jiaqi Cai; and K. Watanabe and T. Taniguchi with the National Institute for Materials Science in Japan. The research was funded by the UW Molecular Engineering Materials Center, a National Science Foundation Materials Research Science and Engineering Center; the China Scholarship Council; the Ministry of Education, Culture, Sports, Science and Technology of Japan; and the Japan Science and Technology Agency.

###

For more information, contact Xu at xuxd@uw.edu and Yankowitz at myank@uw.edu.

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All together now: Experiments with twisted 2D materials catch electrons behaving collectively - UW News

Quantum Software Market to Eyewitness Massive Growth by 2028: Origin Quantum Computing Technology, D Wave, IBM – The Daily Chronicle

GlobalQuantum SoftwareMarket Report is an objective and in-depth study of the current state aimed at the major drivers, market strategies, and key players growth. The study also involves the important Achievements of the market, Research & Development, new product launch, product responses and regional growth of the leading competitors operating in the market on a universal and local scale. The structured analysis contains graphical as well as a diagrammatic representation of worldwideQuantum SoftwareMarket with its specific geographical regions.

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Quantum Software Market to Eyewitness Massive Growth by 2028: Origin Quantum Computing Technology, D Wave, IBM - The Daily Chronicle

Quantum Computing in Aerospace and Defense Market:Revenue Gross, Demand, End-Users, Key Players, Top Competition, Growth & Forecast Insights till…

Quantum Computing in Aerospace and Defense Market Production Analysis and Geographical Market Performance Forecast

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The market study on the global Quantum Computing in Aerospace and Defense Market profiles some of the leading companies. It provides a brief account of companies operational structure and mentions their strategic initiatives. Analysts have also provided complete information about their existing products and the ones in the pipeline and overview of the research and development statuses of products in these companies.

The prominent players covered in this report: D-Wave Systems Inc, Qxbranch LLC, IBM Corporation, Cambridge Quantum Computing Ltd, 1qb Information Technologies Inc., QC Ware Corp., Magiq Technologies Inc., Station Q-Microsoft Corporation, and Rigetti Computing

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Quantum Computing in Aerospace and Defense Market:Revenue Gross, Demand, End-Users, Key Players, Top Competition, Growth & Forecast Insights till...

Quantum Computing Market : Advancements and Efficient Clinical Outcomes would Drive the Industry Growth with Top Key Player’s Analysis – The Daily…

Kenneth Research has published a detailed report on Quantum Computing Market which has been categorized by market size, growth indicators and encompasses detailed market analysis on macro trends and region-wise growth in North America, Latin America, Europe, Asia-Pacific and Middle East & Africa region. The report also includes the challenges that are affecting the growth of the industry and offers strategic evaluation that is required to boost the growth of the market over the period of 2019-2026.

The report covers the forecast and analysis of the Quantum Computing Market on a global and regional level. The study provides historical data from 2015 to 2019 along with a forecast from 2019-2026 based on revenue (USD Million). In 2018, the worldwide GDP stood at USD 84,740.3 Billion as compared to the GDP of USD 80,144.5 Billion in 2017, marked a growth of 5.73% in 2018 over previous year according to the data quoted by International Monetary Fund. This is likely to impel the growth of Quantum Computing Marketover the period 2019-2026.

The Final Report will cover the impact analysis of COVID-19 on this industry.

Request To Download Sample of This Strategic Report:https://www.kennethresearch.com/sample-request-10307113The report provides a unique tool for evaluating the Market, highlighting opportunities, and supporting strategic and tactical decision-making. This report recognizes that in this rapidly-evolving and competitive environment, up-to-date marketing information is essential to monitor performance and make critical decisions for growth and profitability. It provides information on trends and developments, and focuses on markets capacities and on the changing structure of the Quantum Computing.

The quantum annealing category held the largest share under the technology segment in 2019. This is attributed to successful overcoming of physical challenges to develop this technology and further incorporated in bigger systems. The BFSI category held the largest share in the quantum computing market in 2019. This is owing to the fact that the industry is growing positively across the globe, and large banks are focusing on investing in this potential technology that can enable them to streamline their business processes, along with unbeatable levels of security

Automotive to lead quantum computing market for consulting solutions during forecast periodAmong the end-user industries considered, space and defense is the largest contributor to the overall quantum computing market, and it is expected to account for a maximum share of the market in 2019. The need for secure communications and data transfer, with the demand in faster data operations, is expected to boost the demand for quantum computing consulting solutions in this industry. The market for the automotive industry is expected to grow at the highest CAGR

Quantum computing can best be defined as the use of the attributes and principles of quantum mechanics to perform calculations and solve problems. The global market for quantum computing is being driven largely by the desire to increase the capability of modeling and simulating complex data, improve the efficiency or optimization of systems or processes, and solve problems with more precision. A quantum system can process and analyze all data simultaneously and then return the best solution, along with thousands of close alternatives all within microseconds, according to a new report from Tractica.

2018 was a growth year for the market, as businesses from the BFSI sector showed tremendous interest in quantum computing and the trend is likely to continue in 2019 and beyond. Moreover, the public sector presents significant growth opportunity for the market. In the forthcoming years, the application opportunities for quantum computing is expected to expand further, which may lead to a higher commercial interest in the technology.

Market SegmentationThe report focuses on the following end-user sectors and applications for quantum computing:By Based on offering*Consulting solutions*Systems

By End-user sectors*Government.*Academic.*Healthcare.*Military.*Geology/energy.*Information technology.*Transport/logistics.*Finance/economics.*Meteorology.*Chemicals.

By Applications*Basic research.*Quantum simulation.*Optimization problems.*Sampling.

By Regional AnanlysisNorth America*U.S.*Canada

Europe*Germany*UK*France*Italy*Spain*Belgium*Russia*Netherlands*Rest of Europe

Asia-Pacific*China*India*Japan*Korea*Singapore*Malaysia*Indonesia*Thailand*Philippines*Rest of Asia-Pacific

Latin America*Brazil*Mexico*Argentina*Rest of LATAM

Middle East & Africa*UAE*Saudi Arabia*South Africa*Rest of MEA

The quantum computing market is highly competitive with high strategic stakes and product differentiation. Some of the key market players include International Business Machines (IBM) Corporation, Telstra Corporation Limited, IonQ Inc., Silicon Quantum Computing, Huawei Investment & Holding Co. Ltd., Alphabet Inc., Rigetti & Co Inc., Microsoft Corporation, D-Wave Systems Inc., Zapata Computing Inc., and Intel Corporation.

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Competitive Analysis:The Quantum Computing Market report examines competitive scenario by analyzing key players in the market. The company profiling of leading market players is included in this report with Porters five forces analysis and Value Chain analysis. Further, the strategies exercised by the companies for expansion of business through mergers, acquisitions, and other business development measures are discussed in the report. The financial parameters which are assessed include the sales, profits and the overall revenue generated by the key players of Market.

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Kenneth Research is a reselling agency which focuses on multi-client market research database. The primary goal of the agency is to help industry professionals including various individuals and organizations gain an extra edge of competitiveness and help them identify the market trends and scope. The quality reports provided by the agency aims to make decision making easier for industry professionals and take firm decisions which helps them to form strategies after complete assessment of the market. Some of the industries under focus include healthcare & pharmaceuticals, ICT & Telecom, automotive and transportation, energy and power, chemicals, FMCG, food and beverages, aerospace and defense and others. Kenneth Research also focuses on strategic business consultancy services and offers a single platform for the best industry market research reports.

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Quantum Computing Market : Advancements and Efficient Clinical Outcomes would Drive the Industry Growth with Top Key Player's Analysis - The Daily...

How newly unemployed over-50s can start-up again – The Guardian

Max Wallace, a former professional boxer with an interest in art, wants to set up his own wellbeing centre. Sunil Jindal plans to set up a Mumsnet for older men while Sharon Thomas has a business model to monetise her mini artwork greetings cards.

But these entrepreneurs face one problem: theyre over 50 and although the state pension age rises to 66 on Tuesday, 54-year-old Jindal said they already risk being perceived to be over the hill by everyone from banks to, occasionally, themselves.

Ive had a very successful career in computer science, reaching director level, but when my last employer and I went our separate ways in July, I found it a real shock to be back in the job market, Jindal said.

Im applying for jobs but Im planning my own business too because I cant help asking myself: Will I find something at my age? Theres so much stigma around older people out there and I find I doubt myself too; can I reposition my USP at my age, deal with the technology, sustain the energy? he added.

Wallace has no shortage of energy, having set up a community interest company called Health Defence which offers fitness programmes, aimed at combatting ill health in the community. He now hopes to open a micro wellbeing centre in Hammersmith, west London, where people can access his boxing/kickboxing fitness programme, along with healthy eating workshops, massages and free health checks.

Around 377,000 older workers one in 10 male, and eight in 10 female workers in their 50s and 60s face a significant risk of losing their jobs as the governments furlough scheme is wound down this month, according to the Centre for Ageing Better and the Learning and Work Institute.

That is in addition to the recent doubling in the number of people over-50s already claiming unemployment-related benefits between March and May. According to an analysis of official data by Rest Less, a jobs, money and lifestyle site for the over-50s, numbers rose from 304,000 in March to 588,000 in June. This means that more over-50s are claiming universal credit than under-25s.

Additional analysis of Department for Work and Pensions data by the Centre for Ageing Better found that over-50s are less likely to bounce back from unemployment than any other age group: just 35% who lose their job return to work quickly, with 29% remaining unemployed for more than 12 months.

These statistics are causing concern among experts. Last week, the International Longevity Centre UK (ILC), the UKs specialist thinktank on the impact of longevity on society, urged the government to introduce a scheme akin to Kickstart but focused on the needs of older workers.

The long-term growth in employment of those aged over 50 has stalled, with too many people forced out of the workforce too early, said David Sinclair, ILC director. These older workers contribute to economic growth but are likely to find it much more difficult than other ages to get themselves another job.

Andy Briggs, group CEO at Phoenix Group and Government Business Champion for Older Workers, agreed. We know that if you become unemployed over the age of 50 you are less likely than any other group to get another job.

Suzanne Noble, who co-founded Advantages of Age with Rose Rouse, said this is a problem for which there are limited solutions. Noble, who has co-founded the Startup School for Seniors which launched on Monday, said a move into self-employment is one of the most positive and realistic choices available.

Lack of confidence is the key challenge for this age group, said Noble, who has also launched Silver Sharers, a website that matches homeowners aged 50-plus with lodgers of any age. We know, from previous work done with over-50s, that when an older person loses their job it can take a real toll on their mental health.

If youve been working for decades in a career, the expectation is that will deliver you into a comfortable retirement, she added. When that outcome is taken away from you, it can be crushing. Many feel that their work experience no longer has any value and that theres nowhere for them in the modern workplace.

But thats simply not the case, said Noble. Experience is one of the most important USPs of this group, so ignore it at your peril, she said. We have a more relaxed attitude to other people and their opinion about us: the No Fucks Given-movement stole the idea from their elders and betters.

The statistics show the value of that experience: businesses started by those aged over 45 are more likely to be successful than those created by those aged 18-25 years.

Mark Elliott, Nobles co-founder of the free, eight-week online Startup School for Seniors whose 60 participants are aged between 50 to 75 pointed out that modern technology is easier to use than it ever has been. If Covid-19 has taught us nothing else, its that many more older people have become digitally savvy through using Zoom and other video conferencing software, he said.

Theres no reason to assume that someone 50-plus hasnt got the aim or ambition of building a fast-scaling tech business but older entrepreneurs need to be realistic, he said.

Theyre not going to suddenly become Richard Branson but then, would they want to? he asked. On top of what opportunities there are, is the extra question, what sort of opportunities are people this age usually looking for?

With one in five people over 50 being informal carers, flexibility is key, said Elliott. Added to which, older people are often seeking a job that provides them with satisfaction as well as a pay cheque. At this stage in our lives, we need to build a business that we want to work in ourselves. Thats exciting.

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How newly unemployed over-50s can start-up again - The Guardian

How best to welcome your workforce back safely?

This coronavirus pandemic has made numerous changes in everyone’s life and has also affected our finances and business sector badly, although people had set up virtual systems and started completing their work from home, mostly the employers and employees have suffered loss, so here the good news is that now the condition is getting better and the situation has been controlled to a certain level which also means that it is the time to get back to your desk. If your workforce isn’t following the safety and protection rules, you may put your whole staff at risk and reduce your profitability.

In case, you have planned to start a new business in the lockdown period and now you see the things are going back to normal, you might see it as a chance to start your business, but for this matter, you need to have all the significant knowledge about how would you run the business, so you may have to buy a contractors book, the book will help you avoid anything that can put you in loss and also help you with all the legal matters. The contractor books are not only for the beginners, it can be even used by the people who’re doing business for a long time. Continue reading

Using Machine Learning To Predict Disease In Cattle Might Help Solve A Billion-Dollar Problem – Forbes

One of the challenges in scaling up meat production are issues of disease for the animals. Take bovine respiratory disease (BRD), for example. This contagious infection is responsible for nearly half of all feedlot deaths for cattle every year in North America. The industrys costs for managing the disease come close to $1 billion annually.

Preventative measures could significantly decrease these costs, and a small team comprising a data scientist, a college student and two entrepreneurs spent the past weekend at the Forbes Under 30 Agtech+ Hackathon figuring out a concept for better managing the disease.

Their solution? Tag-Ag, a conceptual set of predictive models that could take data already routinely gathered by cattle ranchers and tracked using ear tags to both identify cows at risk for BRD to focus prevention efforts; and to trace outbreaks of BRD to provide more focused treatment and management decisions.

By providing these insights, we can instill confidence in both big consumers such as McDonalds or Wal-Mart, and small consumers like you and me, that their meat is sourced from a healthy and sustainable operation, said team member Natalie McCaffrey, an 18 year-old undergraduate at Washington & Lee University at the Hackathons final presentations on Sunday evening.

McCaffrey was joined by Jacob Shields, 30, a senior research scientist at Elanco Animal Health; Marya Dzmiturk, 28, cofounder of TK startup Avanii and an alumnus of the 2020 Forbes Under 30 list in Manufacturing & Industry; and Shaina Steward, 29, founder of The Model Knowledge Group & Ekal Living.

They joined a larger group of hackathoners who brainstormed a variety of concepts related to animal health on Friday night before settling on three different ideas, at which point the group split into the smaller teams. The initial pitch for the Tag-Ag team was the use of AI & Big Data to help producers keep animals healthy.

As the Tag-Ag team began its research and development process on Saturday, one clear challenge was the scope of potential animal health issues, as well as a potentially intense labor process in collecting useful information. They settled on cattle because, McCaffrey says, big ranchers are already electronically collecting data on cattle, and because BRD by itself makes a huge impact on the industry.

Another advantage of using data already being collected, adds Shields, is that tools exist to build a model for the concepts predictive analytics based on whats out there. For supervised machine learning algorithms, the more inputs the better, he says. I dont believe well need additional studies to support this case, unless we knew of a handful of data points that werent being collected that really would help with the predictability.

For a business model, the Tag-Ag team suggests a subscription-based model, with a one-time implementation fee for any hardware needs. They believe that theres definitely room to raise capital, pointing to the size of the market loss theyre addressing plus the $500 million in venture capital invested in AgTech companies in 2019 alone.

Investors and institutions are recognizing opportunities in the AgTech space, McCaffrey says, and beyond that, she adds, our space of AI and data has space for additional players.

Team members: Natalie McCaffery, undergraduate, Washington & Lee University; Jacob Shields, senior research scientist, Elanco Animal Health; Marya Dzmiturk, cofounder, Avanii; Shaina Steward, 29, founder, The Model Knowledge Group and Ekal Living.

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Using Machine Learning To Predict Disease In Cattle Might Help Solve A Billion-Dollar Problem - Forbes

Machine Learning Is Cheaper But Worse Than Humans at Fund Analysis – Institutional Investor

Morningstar had a problem.

Or rather, its millions of users did: The star-rating system, which drives huge volumes of assets, is inherently backwards-looking. These make-or-break badges label how good (or bad) a fund has performed, not how it will perform.

Morningstars solution was analysts: humans who dig deep into the big and popular fund products, then assign them forward-looking ratings. For analyzing the lesser or niche products, Morningstar unleashed the algorithms.

But the humans still have an edge, academic researchers found except in productivity.

We find that the analyst report, which is usually 4 or 5 pages, provides very detailed information, and is better than a star rating, as it claims to be, said Si Cheng, an assistant finance professor at the Chinese University of Hong Kong, in an interview. She and her co-authors of a just-published study also found that the forward-looking algorithmic analysis doesnt do as much as an analyst rating. If we look at very similar funds rated by human and machine, theyre quite different even though you have two-forward looking ratings.

[II Deep Dive: AQRs Problem With Machine Learning: Cats Morph Into Dogs]

The most potent value in all of these Morningstar modes came from the tone of human-generated reports assessed using machine-driven textual analysis.

Tone is likely to come from soft information, such as what the analyst picks up from speaking to fund management and investors. That deeply human sense of enthusiasm or pessimism matters when it comes through in conflict with the actual rating, which the analysts and algos based on quantitative factors.

Most of Morningstars users are retail investors, but only professionals are tapping into this human-quant arbitrage, discovered Cheng and her Peking University co-authors Ruichang Lu and Xiajun Zhang.

We do find that only institutional investors are taking advantage of analysts reports, she told Institutional Investor Tuesday. They do withdraw from a fund if the fund gets a gold rating but a pessimistic tone.

Cheng, her coauthors, and other academic researchers working in the same vein highlight cost one major advantage of algorithmic analysis over the old-fashioned kind. After initial set up, they automatically generate all of the analysis at a frequency that a human cannot replicate, Cheng said.

As Anne Tucker, director of the legal analytics and innovation initiative at Georgia State University, cogently put it, machine learning is leveraging components of human judgement at scale. Its not a replacement; its a tool for increasing the scale and the speed. On the legal side, almost all of our data is locked in text: memos, regulatory filings, orders, court decisions, and the like.

Tucker has teamed up with GSU analytics professor Yusen Xia and associate law professor Susan Navarro Smelcer to gather the text of fund filings and turn machine-learning programs onto them, searching for patterns and indicators of future risk and performance. The project is underway, and detailed in a recent working paper.

We have complied all of the investment strategy and risk sections from 2010 onwards, and are using text mining, machine learning, a suite of other computational tools to understand the content, study compliance, and then to aggregate texts in order to model emerging risks, Tucker told II. If we listen to the most sophisticated investors collectively, what can we learn? If we would have had these tools before 2008, would we have been able to pick up tremors?

Maybe but they wouldnt have picked up the Covid-19 crisis, early findings suggest.

There were essentially no pandemic-related risk disclosures before this happened, Tucker said.

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Machine Learning Is Cheaper But Worse Than Humans at Fund Analysis - Institutional Investor

Is Quantum Machine Learning the next thing? | by Alessandro Crimi | ILLUMINATION-Curated | Oct, 2020 – Medium

In classical computers, bits are stored as either a 0 or a 1 in binary notation. Quantum computers use quantum bits or qubits which can be both 0 and 1, this is called superimposition. Last year Google and NASA claimed to have achieved quantum supremacy, raising some controversies though. Quantum supremacy means that a quantum computer can perform a single calculation that no conventional computer, even the biggest supercomputer can perform in a reasonable amount of time. Indeed, according to Google, the Sycamore is a computer with a 54-qubit processor, which is can perform fast computations.

Machines like Sycamore can speed up simulation of quantum mechanical systems, drug design, the creation of new materials through molecular and atomic maps, the Deutsch Oracle problem and machine learning.

When data points are projected in high dimensions during machine learning tasks, it is hard for classical computers to deal with such large computations (no matter the TensorFlow optimizations and so on). Even if the classical computer can handle it, an extensive amount of computational time is necessary.

In other words, the current computers we use can be sometime slow while doing certain machine learning application compared to quantum systems.

Indeed, superposition and entanglement can come in hand to train properly support vector machine or neural networks to behave similarly to a quantum system.

How we do this in practice can be summarized as

In practice, quantum computers can be used and trained like neural networks, or better neural networks comprises some aspects of quantum physics. More specifically, in photonic hardware, a trained circuit of quantum computer can be used to classify the content of images, by encoding the image into the physical state of the device and taking measurements. If it sounds weird, it is because this topic is weird and difficult to digest. Moreover, the story is bigger than just using quantum computers to solve machine learning problems. Quantum circuits are differentiable, and a quantum computer itself can compute the change (rewrite) in control parameters needed to become better at a given task, pushing further the concept of learning.

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Is Quantum Machine Learning the next thing? | by Alessandro Crimi | ILLUMINATION-Curated | Oct, 2020 - Medium

Requirements for the Use of Machine Learning in Cardiology Research – The Cardiology Advisor

Suggestions were formulated to reduce bias and error related to the use of machine learning (ML) approaches in cardiology research, and published in the Journal of American College of Cardiologists: Cardiovascular Imaging.

The use of ML approaches for cardiovascular research has recently increased, as the technology offers approaches to automatically discover relevant patterns among datasets. This review authored by members of the American College of Cardiology Healthcare Innovation Council, points to the fact that many studies using ML approaches may have uncertain real-world data sources, inconsistent outcomes, possible measurement inaccuracies, or lack of validation and reproducibility.

The authors provide here a framework to guide cardiovascular research in the form of a checklist.

When considering employing a ML approach for their research work, investigators should initially determine whether it would be applicable for the specific study aim. An important caveat of ML is that it requires large sample sizes. Therefore, if collecting and labeling fewer than hundreds of samples per class is not feasible, overfitting is likely be a relevant concern. When sufficient samples are available, ML approaches are best suited for unstructured data, exploratory study objectives, or for feature selection purposes.

Next, data should be standardized, if necessary. During this process, redundant features are normalized, duplicates are removed, outliers removed or corrected for, and missing data removed or imputed. As a general rule, the ratio of observations to measurements should be 5. In cases in which this ratio is too large, dimension reduction may be considered.

Many ML approaches are available to researchers, and the choice of which model to implement is critical. Some models are preferable for high-dimensional data (regression or instance-based learning) or imaging data (convolutional neural networks). The authors recommend selecting the simplest algorithm that is appropriate for ones dataset.

Several methods are available to assess and evaluate models. Model assessment should always be performed through random division of the data into training, testing, and validation sets. Cross-validation and bootstrapping methods are best suited for big data, and jack-knifing methods for smaller datasets. Model evaluation should include appropriate plots (Bland-Altman). In addition, inter-observational variability should be reported, and misclassification risk be made clear.

To maintain a level of reproducibility across studies, the authors encourage researchers to release the code and data used, when possible. All chosen variables and parameters, as well as specific versions of software and libraries should be clearly indicated.

The authors acknowledge that these methods are complex, and while they have the opportunity to advance the field of cardiology, especially personalized medicine, many concerns remain when translating these findings into clinical practice. This checklist should assist researchers in reducing bias or error when designing and carrying out future studies.

Reference

Sengupta P P, Shrestha S, Berthon B, et al. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist. JACC Cardiovasc Imaging. 2020;13(9):2017-2035.

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Requirements for the Use of Machine Learning in Cardiology Research - The Cardiology Advisor