Monthly Archives: January 2024

Invest in quantum leap in 2024, uncovering tech stocks driving the quantum computing industry's explosive growth

As we usher in 2024, quantum computing stocks are not just buzzwords but pivotal players in a technological revolution. Quantum computing, a field brewing for decades, currently stands at the forefront of innovation. Its a realm where the peculiarities of quantum mechanics converge to forge computing power, effectively dwarfing traditional methods.

Moreover, though quantum computing still dances mostly within the experimental stages in commercial settings, its promise remains undeniable. Functional quantum systems are no longer a fragment of science fiction they are a reality. The implications of this technology are vast and varied, stretching from societal advancements to inevitable security challenges. Yet, the promise held within these quantum computing stocks is palpable, a promise of a future where the benefits far surpass the risks.

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IonQ(NYSE:IONQ) stands out in the quantum computing space as a dedicated player, distinct from the sprawling tech giants which traditionally dominate the sector. This focus gives IonQ an edge, primarily considering its smaller market cap which hints at a robust upside potential for investors. As the first pure-play quantum computing company to go public, IonQ doesnt just participate in the quantum computing conversation; it leads it. With the industry still in its early stages, IonQs role in the sector is critical to the quantum computing narrative.

A significant draw for IonQ is its impressive collaborations with all three major cloud providers. Notably, its Aria quantum computer integrates seamlessly with Amazon (NASDAQ:AMZN), a platform enabling advanced tasks, including testing quantum circuits. This accessibility is a big leap forward for quantum computing applications. Financially, IonQs trajectory has been remarkable. Surpassing its $100 million cumulative bookings target since 2021 and accumulating $58.4 million in bookings in 2023 alone, IonQ demonstrates potent growth. Despite its unprofitability in terms of cash flow, the companys revenue for the third quarter surged by 122% year-over-year (YOY), a clear indicator of its mushrooming potential in a nascent yet rapidly evolving market.

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Nvidia(NASDAQ:NVDA) has established itself as a titan in the tech sphere, particularly in 2023, with its groundbreaking h100 chips leading the charge in artificial intelligence (AI) applications. The anticipation for 2024 is already high as Nvidia gears up to unveil the h200, the successor to the h100. The h200 is poised to elevate Nvidias status even further, reinforcing its position as a frontrunner in the tech world.

Beyond its AI prowess, Nvidia is making significant strides in quantum computing. Its cuQuantum project, aimed at stimulating quantum circuits, has broken new ground in the simulation of ideal and noisy qubits. Nvidias expertise in simulating quantum computing environments is another compelling reason for investors to take note. Moreover, Nvidias projections for a potential quadrupling by 2035 indicate a promising path for long-term investment.

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Alphabet(NASDAQ:GOOG, NASDAQ:GOOGL) is emerging as a powerhouse in the quantum computing sphere, achieving a pivotal breakthrough in February by reducing computational errors in its quantum bits. This advancement is a critical step towards making quantum computers not only usable but commercially viable. Alphabets dedication to overcoming one of the major hurdles in quantum computing commercialization highlights its commitment to leading in this innovative field.

Financially, Alphabet is on a strong footing, bolstered by its decision to efficiently reorganize its advertising business, which represents a staggering 80% of its total revenue. This reorganization comes on the heels of a remarkable $54.4 billion in ad sales in the recent quarter. Such strategic shifts could further enhance the companys robust financial performance. Additionally, Alphabets foray into AI with the launch of Gemini, an AI model designed to rival Microsofts OpenAI, showcases its ambition to convert technological prowess into tangible sales growth. The companys impressive top and bottom lines, with sales of $297.13 billion and net income of $66.73 billion, further solidify its position as a robust contender in the tech arena, poised for continued growth and innovation.

On the date of publication, Muslim Farooque did not have (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

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3 Quantum Computing Stocks to Tap into the Future in 2024 - InvestorPlace

Exploring the convergence of quantum computing, DNA data storage, and AI how these technologies could revolutionize computing power, memory, and information handling if challenges around implementation and ethics are overcome.

Check out these two books for a deeper dive and to stay ahead of the curve.

Computing technology has advanced in leaps and bounds since the early days of Charles Babbages Analytical Engine in the 1800s. The creation of the first programmable computer in the 1940s ushered in a digital revolution that has profoundly impacted communication, commerce, and scientific research. But the binary logic that underlies modern computing is nearing its limits. Exploring new frontiers in processing power, data storage, and information handling will enable us to tackle increasingly complex challenges.

The basic unit of binary computing is the bit either a 0 or 1. These bits can be manipulated using simple logic gates like AND, OR, and NOT. Combined together, these gates can perform any logical or mathematical operation. This binary code underpins everything from representing the notes in a musical composition to the pixels in a digital photograph. However, maintaining and expanding todays vast computational infrastructure requires massive amounts of energy and resources. And binary systems struggle to efficiently solve exponentially complex problems like modeling protein folding.

In the quest to surpass the boundaries of binary computing, quantum computing emerges as a groundbreaking solution. It leverages the enigmatic and powerful principles of quantum mechanics, fundamentally different from the classical world we experience daily.

Quantum Mechanics: The Core of Quantum Computing

Quantum computing is rooted in quantum mechanics, the physics of the very small. At this scale, particles like electrons and photons behave in ways that can seem almost magical. Two key properties leveraged in quantum computing are superposition and entanglement.

Superposition allows a quantum bit, or qubit, to exist in multiple states (0 and 1) simultaneously, unlike a binary bit which is either 0 or 1. This means a quantum computer can process a vast array of possibilities at once.

Entanglement is a phenomenon where qubits become interlinked in such a way that the state of one (whether its a 0, a 1, or both) can depend on the state of another, regardless of the distance between them. This allows for incredibly fast information processing and transfer.

Exponential Growth in Processing Power

A quantum computer with multiple qubits can perform many calculations at once. For example, 50 qubits can simultaneously exist in over a quadrillion possible states. This exponential growth in processing power could tackle problems that are currently unsolvable by conventional computers, such as simulating large molecules for drug discovery or optimizing complex systems like large-scale logistics.

Revolutionizing Fields: Cryptography and Beyond

Quantum computing holds the potential to revolutionize numerous fields. In cryptography, it could render current encryption methods obsolete, as algorithms like Shors could theoretically break them in mere seconds. This presents both a risk and an opportunity, prompting a new era of quantum-safe cryptography.

Beyond cryptography, quantum computing could advance materials science by accurately simulating molecular structures, aid in climate modeling by analyzing vast environmental data sets, and revolutionize financial modeling through complex optimization.

Key Quantum Algorithms

Research in quantum computing has already produced notable algorithms. Shors algorithm, for instance, can factor large numbers incredibly fast, a task thats time-consuming for classical computers. Grovers algorithm, on the other hand, can rapidly search unsorted databases, demonstrating a quadratic speedup over traditional methods.

The Road Ahead: Challenges and Promises

Despite its potential, quantum computing is still in its infancy. One of the major challenges is maintaining the stability of qubits. Known as quantum decoherence, this instability currently limits the practical use of quantum computers. Keeping qubits stable requires extremely low temperatures and isolated environments.

Additionally, error rates in quantum computations are higher than in classical computations. Quantum error correction, a field of study in its own right, is crucial for reliable quantum computing.

Quantum computing, though still in the developmental stage, stands at the forefront of a computational revolution. It promises to solve complex problems far beyond the reach of traditional computers, potentially reshaping entire industries and aspects of our daily lives. As research and technology advance, we may soon witness the unlocking of quantum computings full potential, heralding a new era of innovation and discovery.

DNA data storage emerges as a paradigm shift, harnessing the building blocks of life to revolutionize how we store information.

Unprecedented Storage Capabilities

DNAs storage density is unparalleled: one gram can store up to 215 petabytes of data. In contrast, traditional flash memory can hold only about 128 gigabytes per gram. This immense capacity could fundamentally change how we manage the worlds exponentially growing data.

Longevity and Reliability

DNA is not only dense but also incredibly durable. It can last thousands of years, far outstripping the lifespan of magnetic tapes and hard drives. Its natural error correction mechanisms, rooted in the double helix structure, ensure data integrity over millennia.

DNA for Computation and Beyond

Beyond storage, DNA holds potential for computation. Researchers are exploring DNA computing, where biological processes manipulate DNA strands to perform calculations. This could lead to breakthroughs in solving complex problems that are infeasible for conventional computers.

Challenges in Practical Implementation

Despite its promise, DNA data storage is not without challenges. Synthesizing and sequencing DNA is currently expensive and time-consuming. Researchers are working on methods to streamline these processes and reduce error rates, which are crucial for making DNA a practical medium for everyday data storage.

While quantum computing offers exponential speedups on specialized problems, its broader applicability and scalability remain uncertain. And both quantum and DNA computing currently require extremely low operating temperatures only possible with expensive equipment. They also consume large amounts of energy, though less than traditional data centers. However, both offer inherent data security advantages. Quantum computations cannot be copied, while DNA data storage is dense and hard to access. We may see hybrid deployments that apply these technologies to niche applications. For generalized workloads, traditional binary computing will likely dominate for the foreseeable future.

The integration of AI with quantum computing and DNA data storage represents a leap forward in computational capability.

AI and Quantum Computing: A Synergy for Complex Problems

AI algorithms can leverage the immense processing power of quantum computers to analyze large datasets more efficiently than ever before. This synergy could lead to breakthroughs in fields like drug discovery, where AI can analyze quantum-computed molecular simulations.

AI and DNA Data Storage: Managing Massive Databases

With DNAs vast storage capacity, AI becomes essential in managing and interpreting this wealth of information. AI algorithms can be designed to efficiently encode and decode DNA-stored data, making it accessible for practical use.

Ethical and Societal Implications

As highlighted in The Coming Wave by Mustafa Suleyman, the intersection of these technologies raises significant ethical questions. The use of genetic data in AI models, for instance, necessitates stringent privacy protections and considerations of genetic discrimination.

Looking Ahead: AI as the Conductor

The future sees AI not just as a tool but as a conductor, orchestrating the interplay between quantum computing and DNA data storage. This involves developing new algorithms tailored to the unique properties of quantum and DNA-based systems.

Google AI recently demonstrated a program that can autonomously detect and correct errors on a quantum processor, a major milestone. On the DNA computing front, researchers successfully stored a movie file and 100 books using DNA sequences. Ongoing studies also show promise in using DNA to manufacture nanoscale electronics for faster, denser computing. Quantum computing is enabling models of complex chemical reactions and biological processes. As costs decline, we could see exponential growth in synthesizing custom DNA and practical quantum computers.

Despite promising strides, there are still obstacles to realizing commercially viable DNA and quantum computing. Stability of quantum bits remains limited to milliseconds, far too short for practical applications. And while DNA sequencing costs have dropped, synthesis and assembly costs remain prohibitively high. There are also ethical pitfalls if without careful oversight, like insurers obtaining genetic data, or AI algorithms exhibiting biases. Job losses due to increasing automation present another societal challenge. Investments in retraining and social programs will be necessary to ensure shared prosperity.

Hybridized quantum-DNA computing could transform our relationship with information and usher in an era of highly personalized medicine and hyper-accurate simulations. It may even require overhauling information theory and rethinking how humans interact with advanced AI. But we must thoughtfully navigate disruptions to industries like finance and cryptography. Avoiding misuse will also require international cooperation to enact governance frameworks and design systems mindful of ethical dilemmas. With wise stewardship, hybrid computing could positively benefit humanity.

The convergence of quantum computing, DNA data storage, and AI represents an unprecedented phase change for processing power, memory, and information handling. To fully realize the potential, while mitigating risks, we must aggressively fund research and development at the intersection of these fields. The technical hurdles are surmountable through collaboration between the public and private sectors. But establishing governance and ethical frameworks ultimately requires a broad, multidisciplinary approach. If society rises to meet this challenge, we could enter an age of scientific wonders beyond our current imagination.

Check out these two books for a deeper dive:

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Beyond Binary: The Convergence of Quantum Computing, DNA Data Storage, and AI - Medium

Two groundbreaking studies have developed a method for controlling quantum entanglement in molecules, specifically calcium fluoride (CaF), using an optical tweezer array to create highly entangled Bell states. This advancement opens new avenues in quantum computing and sensing technologies.

Advancements in quantum entanglement with calcium fluoride molecules pave the way for new developments in quantum computing and sensing, utilizing controlled Bell state creation.

Quantum entanglement with molecules has long been a complex challenge in quantum science. However, recent advancements have emerged from two new studies. These studies showcase a method to tailor the quantum states of individual molecules, achieving quantum entanglement on demand. This development offers a promising platform for advancing quantum technologies, including computation and sensing. Quantum entanglement, a fundamental aspect of quantum mechanics, is vital for various quantum applications.

Ultracold molecules, with their intricate internal structure and long-lived rotational states, are ideal candidates for qubits in quantum computing and quantum simulations. Despite success in creating entanglement in atomic, photonic, and superconducting systems, achieving controlled entanglement between molecules has been a challenge. Now, Yicheng Bao and colleagues, along with Conner Holland and colleagues, have developed a method for the controlled quantum entanglement of calcium fluoride (CaF) molecules.

These studies utilized the long-range dipolar interaction between laser-cooled CaF molecules in a reconfigurable optical tweezer array. They successfully demonstrated the creation of a Bell state, a key class of entangled quantum state characterized by maximum entanglement between two qubits. The Bell state is crucial for many quantum technologies.

Both studies show that two CaF molecules located in neighboring optical tweezers and placed close enough to sense their respective long-range electric dipolar interaction led to an interaction between tweezer pairs, which dynamically created a Bell state out of the two previously uncorrelated molecules.

The demonstrated manipulation and characterization of entanglement of individually tailored molecules by Baoet al.and Hollandet al.paves the way for developing new versatile platforms for quantum technologies, writes Augusto Smerzi in a related Perspective.

References:

Dipolar spin-exchange and entanglement between molecules in an optical tweezer array by Yicheng Bao, Scarlett S. Yu, Loc Anderegg, Eunmi Chae, Wolfgang Ketterle, Kang-Kuen Ni and John M. Doyle, 7 December 2023, Science. DOI: 10.1126/science.adf8999

Entanglement with tweezed molecules by Augusto Smerzi, 7 December 2023, Science. DOI: 10.1126/science.adl4179

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Breaking the Cold Barrier: The Cutting-Edge of Quantum Entanglement - SciTechDaily

To a lot of IT leaders, quantum computers sound closer to science fiction than something that can be implemented in their data centers. But it's on the way; IBM last month introduced System Two, the first quantum computer that connects three processors to work together.

Last year's small steps on the quantum roadmap are turning into this year's bigger leaps. IBM charged Scott Crowder, vice president of quantum adoption, with the task of helping customers discover new uses for quantum computing, as well as the development of the software to accomplish those tasks. We asked Crowder to give CIOs a progress report on where quantum computing technology has advanced, and what it will take to get it into the enterprise.

For those who have heard of quantum computing but don't quite grok it, how does it differ from the classical computing that powers our laptops, phones and desktops?

Scott Crowder: It fundamentally uses a different information science. It's not like classical in the sense that we were doing classical information science before we invented digital computers. This is different in the way it does computation. Therefore, it's better at certain kinds of math than the computers of today are bad at and vice versa.

You could theoretically run anything on a universal quantum computer, but you wouldn't want to. You only want to run through quantum the things that classical computers aren't great at and what quantum computers have been proven to be good at. They leverage quantum mechanics, so it is like sci-fi come to life. It can do certain kinds of computation that we might never ever, ever be able to do using a classroom.

When will we see more mainstream adoption of quantum computers, and what will that look like?

Crowder: Before this year, you could argue that anything you could do with a quantum computer could be simulated classically. There was no point of doing the computation on a computer other than learning about its information science. But that's changed. This year, for the first time, you actually could run something on a quantum computer that you can't run on a classical simulator. It doesn't mean you can run anything on a quantum computer. It's the first couple of kinds of computations that you can actually get value out of a quantum computer as opposed to trying to simulate it.

Over the next couple of years, the usefulness or utility will continue to expand. Right now, there are limitations of how big a problem we can run because of the quality of the systems. But we're past the point where there's value in running a quantum computer. It doesn't mean there's business value yet, because problems tend to get bigger, they need to be integrated into your workflows, etc. But we don't think it's going to take until 2033 for other people to get business value.

In the 1940s, we weren't carrying around classical computers in our pocket and doing whatever it is we're doing on our phones. They were the initial use cases in scheduling. I think that's going to be true this decade [for quantum computers]. In the next decade when the systems get bigger and bigger and bigger -- and better and better and better -- you're going to see more and more use cases.

What will be the first use cases?

There are three kinds of math that quantum computers are getting better at.

One of them is around simulating nature. Materials, properties, physics, chemistry -- think of all the industrial as well as healthcare and life sciences chemistry-related things.

The second kind of math that quantum computers will be better at is a certain kind of complex structure in the data. The most famous algorithm, Shor's algorithm -- which all the nation-states are interested in -- is that kind of math. It does factoring: A times B equals C. A times B; regular old computers are good at giving you C. But given C, your computer is not good at figuring out what A and B were. Classical computers are not good at that kind of math, which is a good thing. If we don't have cryptography, we don't have a digital economy.

This is part of the discussion about quantum. If it falls into the hands of bad actors, we are in deep trouble. But this kind of math is also used in machine learning -- things like classification. It can help find fraud, better trial sites for clinical trials and better treatments when it's given a patient's health record data. There's a lot of interest in the industry of leveraging quantum computers in the near term for those kinds of problems.

The last kind of math, which is also interesting -- but for the second phase of the journey late this decade or in the next decade -- is around optimization. What takes me N tries on a regular old computer will take the square root of N tries on a quantum computer. So N equals 100, squared equals a factor of 10. There might be breakthroughs in that space as well. Examples might be portfolio optimization in financial services, risk management and logistics -- a whole bunch of things that people struggle with using regular computers to document today.

Quantum computers run somewhere down near zero degrees Kelvin. How are we going to solve the freezer problem? Put them in space?

Crowder: Unfortunately, space isn't cold enough.

We need to isolate the computing part of it from the rest of the universe because you're programmatically entangling these qubits with each other in a specific way. It can't be perfectly isolated (at absolute zero) because if it is perfectly isolated, we can't get them to do anything. It needs to be just connected enough to the rest of the universe so you can program it, but the rest of the universe can't muck with it. That's why either you need to keep it very, very, very, very cold -- like we do with our technology -- or you need to shoot laser beams at it [using a light-based approach] to take the entropy out. It's complicated, and it's not room temperature, no matter how you do it.

The good news is that there are commercial refrigeration techniques that are stable. They're low cost, and they're low energy compared to regular old computers -- like compared to a rack of electronics. These things seem extremely efficient. The refrigeration action is not that big of a problem. There are other problems in scaling them and getting the cost down, but the underlying technology is there.

Do you think that quantum computers will ever make it into the average enterprise data center? Or will it be reserved for specialized use only large enterprises will be able to afford?

Crowder: The infrastructure around quantum computers, I know, seems weird and different right now. But we've deployed them at Cleveland Clinic; we've deployed them in Germany, Japan and Canada. We have large data centers. I think in the near-term, like the next several years, the technology is so rapidly advancing that it probably doesn't make sense plopping them in enterprise data centers, because you're going to want the latest technology.

Cloud delivery has definite advantages because the software stack is evolving quickly and allows us to get new capabilities out to everybody at the same time and because the underlying hardware improves year by year by year. You're going to have quantum computers in enterprise data centers, whether that be [via] cloud provider or on premises. It's going to happen. It just doesn't make sense in the next several years.

Explain how quantum computing will intersect with AI. We have heard that quantum is not a match for generative AI.

Crowder: It's a mix. People usually use the word AI to mean the latest trend in AI.

Thinking of AI in a broader sense [than just generative AI], yes, there is a direct connection in terms of finding data patterns and complex structure problems, through machine learning or other means. Quantum will automatically do a better job of classification, as an example. That's not generative AI.

Generative AI is the latest stage in AI, and that is now the definition of AI for the next year or two until we come up with something else -- the next definition of AI. Generative AI has just a tenuous connection to quantum computing. There are people who are doing research and looking at leveraging quantum on neural networks as opposed to deep neural networks. I don't think anything has proven that quantum is going to be better in that space. But some researchers think that it might. Over the next couple of years, we'll find out the answer. But at this point I haven't seen any data that says definitively "yes." But I haven't seen any data that says definitively "no" either.

Don Fluckinger covers digital experience management, end-user computing, CPUs and assorted other topics for TechTarget Editorial. Got a tip? Email him here.

Originally posted here:

Where AI and quantum computing meet - TechTarget

With the technology machinery running at full speed, forward-looking investors should consider the hottest quantum computing stocks for the prospect of robust profitability. To be sure, the pure-play ecosystem presents high risks. At the same time, the potential paradigm shift involved in quantum computers makes the bullish case incredibly enticing.

Essentially, the innovation empowers simultaneous multi-tiered data transmissions at a level that classical computers could never hope to achieve. Thats because the latter category is effectively a binary proposition. To solve a multi-tiered problem, it must first tackle the earlier iteration before resolving the subsequent challenges.

Quantum computers? They can address multiple problem sets, in part because the underlying qubit can exist in two physical states owing to the straight-up weird phenomenon of quantum mechanics at one time. Bottom line, it opens doors previously considered permanently closed, thus undergirding hot quantum stocks.

For full disclosure, the sector is very much young and there will almost certainly be growing pains. So, dont play in this sandbox if you cant handle volatility. Still, if you can accept the risk-reward profile, these hottest quantum computing stocks deserve a closer look.

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While internet and tech innovation juggernaut Alphabet (NASDAQ:GOOG, NASDAQ:GOOGL) may be a mature and thus boring idea, GOOG should help you on the road to the hottest quantum computing stocks that are proven viable. Analysts agree, rating shares a consensus strong buy with a $155.60 average price target. The high-side target lands at $160.

Through its Google Quantum AI team, Alphabet represents a major player in the research and development of superconducting quantum processors and software tools. So far, the company has achieved significant milestones in demonstrating its quantum supremacy. Enticingly, the quantum computing market could be worth $6.5 billion by 2028, representing a CAGR of 48.1% from 2023.

Although GOOG is unquestionably one of the hot quantum computing stocks, the main difference between parent Alphabet and the competition centers on the primary focus. Alphabet is more research oriented and it can afford to do so thanks to its robust financials.

In other words, its not one of the hottest quantum computing stocks based on upward mobility potential. However, you can trust Alphabet to be around in the next hundred years.

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Based in Canada, D-Wave Quantum (NYSE:QBTS) claims to be the worlds first company to sell computers that exploit quantum effects in their operation. It carries serious clout, with D-Waves early customers stemming from esteemed names like Alphabets Google and the University of Southern California. Analysts anticipate great things from QBTS, pegging it a unanimous strong buy with a $2.17 price target. That makes it one of the hottest quantum computing stocks based on implied growth.

Fundamentally, D-Wave is relevant to the discussion thanks to its specialty in building annealing quantum computers. These devices are specifically designed for solving so-called optimization problems, such as logistics, scheduling and financial modeling. Further, D-Waves processors are based on superconducting qubits arranged in a chimera topology. This distinct profile enables the company to tackle specific challenges at a much faster clip than classical computers.

Financially, the risk for QBTS is that its largely a narrative play. For example, shares trade at a trailing-year revenue multiple of 12.62X. Needless to say, thats sky high. However, with the projected growth of the underlying field, QBTS might be an opportunity based on where it might go.

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Headquartered in Berkeley, California, Rigetti Computing (NASDAQ:RGTI) develops quantum integrated circuits (ICs). An IC is basically a canvas of semiconductor wafers with millions of tiny resistors, capacitors and other components. Rigetti also develops a cloud platform called Forest that enables programmers to write with quantum algorithms. Analysts love RGTI, rating shares a unanimous strong buy with a $3. That also makes it one of the hottest quantum computing stocks based on implied shareholder profit.

As mentioned above, Rigetti lays a stake in the quantum field through its software platform. Beyond providing tools for algorithm design, Forest enables quantum circuit development and error correction. Therefore, it caters to both researchers and developers who may be working on different quantum applications. Further, the company aims to introduce practical applications in areas like materials science, chemistry and even financial modeling.

According to investment data aggregator Gurufocus, Rigettis projected future three-to-five-year revenue growth rate clocks in at 45.29%. That would be impressive if it comes true. However, investors should realize that right now, RGTI trades at 7.93X trailing-year sales, which is significantly overpriced. Still, if you anticipate a bright future, Rigetti could be one of the hot quantum computing stocks to consider.

On the date of publication, Josh Enomoto did not have (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

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The 3 Hottest Quantum Computing Stocks to Watch in 2024 - InvestorPlace

Call it the Avengers of futuristic computing. Put together two of the buzziest terms in technology machine learning and quantum computers and you get quantum machine learning. Like the Avengers comic books and films, which bring together an all-star cast of superheroes to build a dream team, the result is likely to attract a lot of attention. But in technology, as in fiction, it is important to come up with a good plot.

If quantum computers can ever be built at large-enough scales, they promise to solve certain problems much more efficiently than can ordinary digital electronics, by harnessing the unique properties of the subatomic world. For years, researchers have wondered whether those problems might include machine learning, a form of artificial intelligence (AI) in which computers are used to spot patterns in data and learn rules that can be used to make inferences in unfamiliar situations.

Now, with the release of the high-profile AI system ChatGPT, which relies on machine learning to power its eerily human-like conversations by inferring relationships between words in text, and with the rapid growth in the size and power of quantum computers, both technologies are making big strides forwards. Will anything useful come of combining the two?

Many technology companies, including established corporations such as Google and IBM, as well as start-up firms such as Rigetti in Berkeley, California, and IonQ in College Park, Maryland, are investigating the potential of quantum machine learning. There is strong interest from academic scientists, too.

CERN, the European particle-physics laboratory outside Geneva, Switzerland, already uses machine learning to look for signs that certain subatomic particles have been produced in the data generated by the Large Hadron Collider. Scientists there are among the academics who are experimenting with quantum machine learning.

Our idea is to use quantum computers to speed up or improve classical machine-learning models, says physicist Sofia Vallecorsa, who leads a quantum-computing and machine-learning research group at CERN.

The big unanswered question is whether there are scenarios in which quantum machine learning offers an advantage over the classical variety. Theory shows that for specialized computing tasks, such as simulating molecules or finding the prime factors of large whole numbers, quantum computers will speed up calculations that could otherwise take longer than the age of the Universe. But researchers still lack sufficient evidence that this is the case for machine learning. Others say that quantum machine learning could spot patterns that classical computers miss even if it isnt faster.

Researchers attitudes towards quantum machine learning shift between two extremes, says Maria Schuld, a physicist based in Durban, South Africa. Interest in the approach is high, but researchers seem increasingly resigned about the lack of prospects for short-term applications, says Schuld, who works for quantum-computing firm Xanadu, headquartered in Toronto, Canada.

Some researchers are beginning to shift their focus to the idea of applying quantum machine-learning algorithms to phenomena that are inherently quantum. Of all the proposed applications of quantum machine learning, this is the area where theres been a pretty clear quantum advantage, says physicist Aram Harrow at the Massachusetts Institute of Technology (MIT) in Cambridge.

Over the past 20 years, quantum-computing researchers have developed a plethora of quantum algorithms that could, in theory, make machine learning more efficient. In a seminal result in 2008, Harrow, together with MIT physicists Seth Lloyd and Avinatan Hassidim (now at Bar-Ilan University in Ramat Gan, Israel) invented a quantum algorithm1 that is exponentially faster than a classical computer at solving large sets of linear equations, one of the challenges that lie at the heart of machine learning.

But in some cases, the promise of quantum algorithms has not panned out. One high-profile example occurred in 2018, when computer scientist Ewin Tang found a way to beat a quantum machine-learning algorithm2 devised in 2016. The quantum algorithm was designed to provide the type of suggestion that Internet shopping companies and services such as Netflix give to customers on the basis of their previous choices and it was exponentially faster at making such recommendations than any known classical algorithm.

Tang, who at the time was an 18-year-old undergraduate student at the University of Texas at Austin (UT), wrote an algorithm that was almost as fast, but could run on an ordinary computer. Quantum recommendation was a rare example of an algorithm that seemed to provide a significant speed boost in a practical problem, so her work put the goal of an exponential quantum speed-up for a practical machine-learning problem even further out of reach than it was before, says UT quantum-computing researcher Scott Aaronson, who was Tangs adviser. Tang, who is now at the University of California, Berkeley, says she continues to be pretty sceptical of any claims of a significant quantum speed-up in machine learning.

A potentially even bigger problem is that classical data and quantum computation dont always mix well. Roughly speaking, a typical quantum-computing application has three main steps. First, the quantum computer is initialized, which means that its individual memory units, called quantum bits or qubits, are placed in a collective entangled quantum state. Next, the computer performs a sequence of operations, the quantum analogue of the logical operations on classical bits. In the third step, the computer performs a read-out, for example by measuring the state of a single qubit that carries information about the result of the quantum operation. This could be whether a given electron inside the machine is spinning clockwise or anticlockwise, say.

Algorithms such as the one by Harrow, Hassidim and Lloyd promise to speed up the second step the quantum operations. But in many applications, the first and third steps could be extremely slow and negate those gains3. The initialization step requires loading classical data on to the quantum computer and translating it into a quantum state, often an inefficient process. And because quantum physics is inherently probabilistic, the read-out often has an element of randomness, in which case the computer has to repeat all three stages multiple times and average the results to get a final answer.

Once the quantumized data have been processed into a final quantum state, it could take a long time to get an answer out, too, according to Nathan Wiebe, a quantum-computing researcher at the University of Washington in Seattle. We only get to suck that information out of the thinnest of straws, Wiebe said at a quantum machine-learning workshop in October.

When you ask almost any researcher what applications quantum computers will be good at, the answer is, Probably, not classical data, says Schuld. So far, there is no real reason to believe that classical data needs quantum effects.

Vallecorsa and others say that speed is not the only metric by which a quantum algorithm should be judged. There are also hints that a quantum AI system powered by machine learning could learn to recognize patterns in the data that its classical counterparts would miss. That might be because quantum entanglement establishes correlations among quantum bits and therefore among data points, says Karl Jansen, a physicist at the DESY particle-physics lab in Zeuthen, Germany. The hope is that we can detect correlations in the data that would be very hard to detect with classical algorithms, he says.

Quantum machine learning could help to make sense of particle collisions at CERN, the European particle-physics laboratory near Geneva, Switzerland.Credit: CERN/CMS Collaboration; Thomas McCauley, Lucas Taylor (CC BY 4.0)

But Aaronson disagrees. Quantum computers follow well-known laws of physics, and therefore their workings and the outcome of a quantum algorithm are entirely predictable by an ordinary computer, given enough time. Thus, the only question of interest is whether the quantum computer is faster than a perfect classical simulation of it, says Aaronson.

Another possibility is to sidestep the hurdle of translating classical data altogether, by using quantum machine-learning algorithms on data that are already quantum.

Throughout the history of quantum physics, a measurement of a quantum phenomenon has been defined as taking a numerical reading using an instrument that lives in the macroscopic, classical world. But there is an emerging idea involving a nascent technique, known as quantum sensing, which allows the quantum properties of a system to be measured using purely quantum instrumentation. Load those quantum states on to a quantum computers qubits directly, and then quantum machine learning could be used to spot patterns without any interface with a classical system.

When it comes to machine learning, that could offer big advantages over systems that collect quantum measurements as classical data points, says Hsin-Yuan Huang, a physicist at MIT and a researcher at Google. Our world inherently is quantum-mechanical. If you want to have a quantum machine that can learn, it could be much more powerful, he says.

Huang and his collaborators have run a proof-of-principle experiment on one of Googles Sycamore quantum computers4. They devoted some of its qubits to simulating the behaviour of a kind of abstract material. Another section of the processor then took information from those qubits and analysed it using quantum machine learning. The researchers found the technique to be exponentially faster than classical measurement and data analysis.

Doing the collection and analysis of data fully in the quantum world could enable physicists to tackle questions that classical measurements can only answer indirectly, says Huang. One such question is whether a certain material is in a particular quantum state that makes it a superconductor able to conduct electricity with practically zero resistance. Classical experiments require physicists to prove superconductivity indirectly, for example by testing how the material responds to magnetic fields.

Particle physicists are also looking into using quantum sensing to handle data produced by future particle colliders, such as at LUXE, a DESY experiment that will smash electrons and photons together, says Jensen although the idea is still at least a decade away from being realized, he adds. Astronomical observatories far apart from each other might also use quantum sensors to collect data and transmit them by means of a future quantum internet to a central lab for processing on a quantum computer. The hope is that this could enable images to be captured with unparalleled sharpness.

If such quantum-sensing applications prove successful, quantum machine learning could then have a role in combining the measurements from these experiments and analysing the resulting quantum data.

Ultimately, whether quantum computers will offer advantages to machine learning will be decided by experimentation, rather than by giving mathematical proofs of their superiority or lack thereof. We cant expect everything to be proved in the way we do in theoretical computer science, says Harrow.

I certainly think quantum machine learning is still worth studying, says Aaronson, whether or not there ends up being a boost in efficiency. Schuld agrees. We need to do our research without the confinement of proving a speed-up, at least for a while.

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The AIquantum computing mash-up: will it revolutionize science? - Nature.com

Quantum machine learning (QML), a subfield of quantum computing that combines the principles of quantum mechanics with machine learning (ML) algorithms, hold transformative potential in banking and finance, offering opportunities to enhance decision-making processes, better mitigate risks, and uncover new business opportunities.

But despite these many promises and opportunities, there are still several challenges and risks that need to be addressed, including the complexity of quantum algorithms, the high costs associated with the development and implementation of quantum computing and QML, and regulatory and ethical challenges in integrating these technologies in the financial industry, a new report by the Institute of Financial Services Zug IFZ at the Lucerne School of Business says.

Quantum computing is a type of computing technology that harnesses the principles of quantum physics to perform computations. In contrast to classical computing where information is processed using bits represented as 0s and 1s, quantum computing uses quantum bits or qubits that can exist in a state of superposition, simultaneously representing both 0 and 1.

In addition to superposition, another unique principle of quantum computing is entanglement where the state of one qubit is directly influenced by the state of the other, even if they are physically separated.

These properties allow quantum computers to solve certain types of problems much more efficiently than classical computers.

On the other hand, AI technology relies on algorithms and data to create systems that emulate human intelligence and which are capable of performing tasks such as visual perception, speech recognition, decision-making, and language translation. ML is a subset of AI focusing on gives computer systems the ability to learn from data without explicit being programmed.

The Institute of Financial Services Zug IFZ report, titled Quantum Computing and Artificial Intelligence in Finance and released in December 2023, explores the relationship between quantum computing and ML in the financial sector, highlighting both opportunities and challenges in this technological convergence.

According to the report, when quantum computing and AI/ML are combined, these technologies can unlock unparalleled potential for financial services, a sector thats characterized by substantial data volumes, intricate problems, and critical decision-making. This integration promises to revolutionize how the financial services sector handles complex challenges and data-intensive processes, enhancing speed, precision, and intelligence, it says.

In the financial sector, QML, which refers to the use of algorithms run on quantum devices to process and analyze large volumes of data, is able to perform certain calculations exponentially faster than classical computers, potentially offering many benefits in use cases ranging from fraud prevention and creditworthiness calculations, to more efficient pricing strategies and optimized portfolio management strategies.

In fraud prevention, quantum algorithms can enhance fraud detection systems by efficiently and promptly analyzing large volumes of financial transaction data and identifying patterns indicative of fraudulent activities. In credit scoring, QML can aid in assessing credit-worthiness by analyzing diverse data sources to provide more accurate risk assessments for individuals and businesses.

Quantum algorithms can also be utilized to expedite the calculation of financial product prices, enabling more efficient pricing strategies and uncovering arbitrage opportunities. Finally, in portfolio management, trading and hedging, QML can be employed to develop advanced strategies and optimize portfolio management by processing market and financial data and identifying patterns that can inform decision-making processes as well as determining optimal investment opportunities.

But despite these opportunities, the report notes that quantum computing is still in its early stages of development and that several challenges are hampering the finance industry from fully harnessing the potential of quantum computing and QML.

These challenges primarily relate to scalability and reliability. High-performance quantum computers require hundreds of thousands of qubits for practical use, and while the industry is actively developing new and scalable hardware, it will still take a few years before a service is available in the required quantities, the report says.

Additionally, the use of quantum computing is still complicated and not user-friendly today, implying that further innovations in the area of quantum-related software are required.

Finally, the issue of reliability is a sticking point in the operation of a quantum computer thats associated with the issue of decoherence. Decoherence effects arise when a quantum system interacts with its environment and the superposition is lost. It can introduces errors and limits the depth and complexity of quantum computations that can be reliably performed.

Interest in quantum computing has risen sharply over the past year. In 2022, investors poured US$2.35 billion into quantum tech startups, surpassing 2021s record for the highest annual level of quantum tech startup investment, findings from a McKinsey analysis show.

Volume of raised investment in the indicated year, US$ million, Source: McKinsey and Company, April 2023

Deloitte expects the financial services industrys spending on quantum computing capabilities to grow 233x from just US$80 million in 2022 to US$19 billion in 2032, reflective of the sectors confidence in the technologys future commercial potential.

Spending on quantum computing capabilities from the financial services industry (US$ million), 2022-2032, Source: Deloitte Center for Financial Services analysis, July 2023

According to McKinsey, the financial services industry stands as one of the four sectors likely to see the earliest economic impact from quantum computing, potentially gaining up to US$700 billion in value by 2035 thanks to the technology.

Estimated value at stake for quantum computing in four key industries, Source: 2023 Quantum Technology Monitor, McKinsey and Company, April 2023

Switzerland welcomed in 2022 its first quantum hub. Called QuantumBasel, the center is located in the uptownBasel innovation campus and provides customers and researchers with workshops, training sessions, and access to quantum systems to further their understanding of quantum computing and drive progress towards commercial applications. Funded by the family of Dr. Thomas Staehelin and Monique Staehelin, QuantumBasel is set to house the countrys first commercially viable quantum computer starting in 2024.

Featured image credit: edited from freepik

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New IFZ Paper Explores the Opportunities and Challenges of Quantum Computing and AI in Finance - Fintech Schweiz ... - Fintechnews Switzerland

What will happen if we combine Quantum Computing with Artificial Intelligence?

What youre going to read in this blog might sound like the brainchild of a Sci-Fi novelist on a caffeine bench, but here is the kicker while these visions might seem farfetched to now, many leading experts are nodding along with the marriage of quantum computing and AI.

The lines between reality and fiction blur to the point where distinguishing one from the other could be our next big challenge.

Heres what will happen when we combine Quantum Computing with AI:

Tasks that take years will be done in seconds.

Think about the time it takes for your computer to start up. Remember dial-up internet that painful wait for a single web page to load?

Yep, that was top tech in its time.

Fast forward to todays supercomputers, which can process vast data in seconds. Impressive, right?

But what if I told you quantum computers scoff at these advanced machines? Classical computers work with bits. Think of them as light switches, either on or off.

Quantum computers, on the other hand, utilize qubits. Thanks to superposition, these qubits can be on, off, or both simultaneously.

A qubit, or quantum bit, is the basic unit of information in quantum computing. Its the quantum version of the classic binary bit, and its physically realized with a two-state device.

The power grows exponentially with each added qubit.

Nobel laureate Richard Feynman famously said,

If you think you understand quantum mechanics, you doesnt understand quantum mechanics.

True, its mind-boggling. But for a quick analogy, consider reading all the books in a library simultaneously instead of one by one.

Thats the potential speed of a quantum machine.

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Quantum Computing Meets AI What Happens Next | by Anshul Kummar | Jan, 2024 - Medium

AI and machine learning have undoubtedly captured the attention of the tech world and we are closer to AI-quantum computing than we thought.

The buzz around AI and machine learning isnt just hype anymore; its the soundtrack to a rapidly evolving landscape. From self-driving cars weaving through our streets to robots assisting in delicate surgeries, the applications are already changing our world. And amidst this exciting wave, another force is gathering momentum: the fusion of AI and quantum computing.

While the concept of AI-quantum computing might sound like science fiction, the reality is surprising. Were closer to achieving this groundbreaking synergy than many expected. Advancements in both fields are happening at a breakneck pace.

AI-quantum computing is the fusion of two of the buzziest terms in technology: machine learning and quantum computers.

In one corner, we have artificial intelligence (AI), the art of creating intelligent machines that can learn, reason, and understand the world around them. AI algorithms, powered by mountains of data, can decipher patterns, make predictions, and even generate creative content. Theyre behind the self-driving cars navigating our streets, the personalized recommendations filling our screens, and the medical insights revolutionizing healthcare.

In the other corner stands quantum computing, a technology that harnesses the counterintuitive principles of the quantum world. Unlike traditional computers that rely on bits (either 0 or 1), quantum computers employ qubits, which can exist in a superposition of both states simultaneously. This bizarre ability allows them to explore vast numbers of possibilities in parallel, tackling problems that would take classical computers eons to solve.

But what happens when these two giants collide? Thats where the excitement of AI-quantum computing takes center stage. This marriage of minds and mechanics holds the potential to:

Of course, this futuristic vision comes with its own set of challenges. Building and maintaining reliable AI-quantum computing is still a technological hurdle, and integrating them seamlessly with existing AI frameworks is no small feat. The very nature of quantum mechanics introduces noise and errors, demanding sophisticated error correction techniques.

Despite these obstacles, the field is progressing at breakneck speed. Advances in quantum hardware, software development, and AI algorithms are paving the way for practical applications. Research teams around the world are actively designing hybrid quantum-classical algorithms, testing them on real-world problems, and pushing the boundaries of whats possible.

While the success of AI-quantum computing remains to be seen, the potential rewards are undeniable. This collaborative venture could unleash a new era of scientific discovery, technological innovation, and human progress. Its a story still being written, but one that promises to rewrite the very notion of what computers can achieve.

So, the next time you hear about AI and quantum computing, remember this: its not just about bits and bytes, algorithms and circuits. Its about a powerful synergy, a fusion of minds and mechanics, with the potential to reshape the world we live in.

Its difficult to predict exactly when AI-quantum computing will become a reality, as its a complex field that requires significant advances in both AI and quantum computing. However, researchers are actively working on developing the necessary technologies and algorithms, and some experts believe that we could see the first practical applications of AI-quantum computing within the next 5-10 years.

There are several challenges that need to be overcome before AI-quantum computing can become a reality, including the development of reliable and scalable quantum computing hardware, the creation of quantum algorithms that can solve real-world problems, and the integration of quantum computing with classical AI systems.

Despite these challenges, many experts believe that AI-quantum computing has the potential to revolutionize many areas of research and industry, and there is significant investment and research being done in this field. For example, Google, IBM, and Microsoft are all actively working on developing quantum computing hardware and algorithms, NVIDIA has recently unveiled their superchips and there are several startups and research institutions working on AI-quantum computing applications.

The convergence of artificial intelligence (AI) and quantum computing holds immense potential to revolutionize industries and transform our lives. This potent combination could tackle previously intractable problems and drive unprecedented innovation across various fields.

Imagine personalized medicine tailoring treatments to individual genomes, materials science designing revolutionary substances with unheard-of properties, or finance predicting market fluctuations with uncanny accuracy. AI-quantum computing could unlock these possibilities, accelerating drug discovery, optimizing supply chains, and creating next-generation solar cells.

Education could be radically personalized, with AI-powered tutors adapting to each students needs and preferences. Climate change mitigation strategies could be vastly improved through accurate modeling and resource management. Even mundane tasks like traffic management and entertainment recommendations could be optimized, leading to smoother commutes and personalized content experiences.

This transformative potential comes with challenges. Automation through AI could lead to job losses, necessitating reskilling and adaptation programs. Ensuring fairness and mitigating bias in AI algorithms will be crucial to prevent discrimination in loan approvals or criminal justice. Robust data privacy and security regulations are needed to address potential breaches and protect individual information.

Achieving true AI-quantum computing will take time, significant research, and careful ethical considerations. But the potential benefits are immense, with the potential to solve some of humanitys most pressing challenges and improve our lives in unimaginable ways. Ultimately, the future of AI-quantum computing depends on how we choose to develop and utilize this powerful technology, ensuring it serves the betterment of humanity.

Who knows? Maybe Open AIs Q-star is the first small step we have taken for it.

Featured image credit: benzoix/Freepik.

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What Is AI-quantum Computing And How Will It Change The World? - Dataconomy

Quantum computing is an exciting yet complex space with much promise. Recent projections estimate the global quantum computing market will grow to$7.6 billionin 2027. This forecast considers factors like the methodical pace of quantum hardware development, competition from other advanced computing technologies, and current economic uncertainties.

Analysts expect a gradual expansion as the market matures. Progress will likely come through enhancements in infrastructure, computing platforms, and a wider range of suitable applications. Experts predict investments will continue accelerating over the next five years, even with measured hardware breakthroughs.Quantum computing stocksare positioned to capitalize on this advancement. Lets take a look at the three most promising ones.

Source: The Art of Pics / Shutterstock.com

The first quantum computing stock on our list is the global tech companyMicrosoft(NASDAQ:MSFT). It has been around for over48 yearsand is based in Redmond, Washington. Today, the company is valued at over $2.7 trillion, develops software and hardware, provides cloud computing, and now develops quantum computing technology. Main business segments include productivity, business processes, and LinkedIn; cloud computing platforms like Azure; Windows and other operating systems; and devices like Surface and Xbox.

While quantum computing may have a limited financial impact on Microsoft, like its investment in OpenAI, the companys innovative contributions solidify its role in shaping the future of computing technology.

MSFTrecently announceda partnership with the AFL-CIO to develop AI technology that benefits workers. Instead of treating labor as an input to be optimized by tech, they want workers themselves to guide the development process. The partnership allows workers on-the-ground expertise to shape how AI gets built and deployed. This collaboration can reduce burdens, enhance careers, unlock human potential, and increase company valuation.

Microsoft reported strong financial results in its recent quarter. Total revenue rose13%year-over-year to $56.5 billion, operating income jumped 25% to $26.9 billion, net income increased 27% to $22.3 billion, and diluted earnings per share was $2.99. The company saw double-digit growth across major financial metrics, and on top of that, analysts rated the stock aStrong Buy, citing over 60% upside potential with a high price target of $600. These factors show that Microsoft continues to fire on all cylinders and deliver shareholder value, making it one of the best quantum computing stocks to buy.

Source: JHVEPhoto / Shutterstock.com

Intel(NASDAQ:INTC) has been an innovative force since its founding over55 yearsago. The company develops and provides computing products and services, including quantum computing technologies. Intels market cap now exceeds $200 billion thanks to its multiple business segments, such as client computing platforms, data centers, and artificial intelligence solutions. The company is also known for driving progress in cloud infrastructure, networking, and vision capabilities.

Intelrecently announcedthe launch of AI products, including the Intel Core Ultra and 5th Gen Intel Xeon processors. The company can unlock operational value by deploying these solutions across its technology infrastructure to boost efficiency, reduce expenses, and open the door for modern applications.

Intel delivered third-quarter revenue of$14.2 billionwhich represents an 8% decline in revenue year-over-year. The company outlined fourth-quarter guidance indicating expected revenues between $14.6 billion and $15.6 billion and non-GAAP EPS of $0.44. Analysts are confident with the stock, giving it a Buy rating witha high estimate of $68, citing over 34% upside potential from its current prices, making it one of the great quantum computing stocks to pick up.

Source: josefkubes / Shutterstock.com

Honeywell(NASDAQ:HON) is a diversified technology and manufacturing company. The company was founded in1885and headquartered in Charlotte, North Carolina. As of late, the company has an enterprise value of$150 billionand operates four main business segments: aerospace, building technologies, performance materials and technologies, and safety and productivity solutions. Besides these segments, Honeywell also explores quantum computing through its Honeywell Quantum Solutions division. The division focuses on developing and commercializing quantum devices.

Honeywell recently boosted its market presence by integratingquantum-computing-hardened encryptionkeys into its smart utility meters. This solution generates keys through quantum-computing-enhanced randomness, significantly increasing data security for gas, water, and electric utilities. This initiative fortifies Honeywells commitment to innovation and positions the company at the forefront of cybersecurity.

Honeywell reported strong third-quarter results, with sales of$9.2 billion, up 3% over the prior year. Orders were up by 10%, the companys backlog grew 8% to reach a record level of $31.4 billion. Operating margins also went up by 20.9%. The Aerospace division performed well this quarter, with 18% sales growth. Honeywell also exceeded earnings expectations by a modest2.25%and analysts rate the stock as a Strong Buy with over 20% upside potential. Considering these factors, Honeywell is set for continued growth and makes for an excellent quantum computing stock to buy.

On the date of publication, Rick Orford held long positions in MSFT. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

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New Year, New Gains: The 3 Best Quantum Computing Stocks to Buy in 2024 - InvestorPlace