Daily Archives: January 26, 2020

Study of a model of computation

Quantum Computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is used to perform such computation, which can be implemented theoretically or physically[1]:I-5 There are two main approaches to physically implementing a quantum computer currently, analog and digital. Analog approaches are further divided into quantum simulation, quantum annealing, and adiabatic quantum computation. Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits.[1]:2-13

Qubits are fundamental to quantum computing and are somewhat analogous to bits in a classical computer. Qubits can be in a 1 or 0 quantum state. But they can also be in a superposition of the 1 and 0 states. However, when qubits are measured the result is always either a 0 or a 1; the probabilities of the two outcomes depends on the quantum state they were in.

Quantum computing began in the early 1980s, when physicist Paul Benioff proposed a quantum mechanical model of the Turing machine.[2]Richard FeynmanandYuri Maninlater suggested that a quantum computer had the potential to simulate things that a classical computer could not.[3][4] In 1994, Peter Shor developed a quantum algorithm for factoring integers that had the potential to decrypt all secured communications.[5]

Despite ongoing experimental progress since the late 1990s, most researchers believe that "fault-tolerant quantum computing [is] still a rather distant dream".[6] On 23 October 2019, Google AI, in partnership with the U.S. National Aeronautics and Space Administration (NASA), published a paper in which they claimed to have achieved quantum supremacy. [7] While some have disputed this claim, it is still a significant milestone in the history of quantum computing.[8]

The field of quantum computing is a subfield of quantum information science, which includes quantum cryptography and quantum communication.

The prevailing model of quantum computation describes the computation in terms of a network of quantum logic gates. What follows is a brief treatment of the subject based upon Chapter 4 of Nielsen and Chuang.[9]

A memory consisting of n {textstyle n} bits of information has 2 n {textstyle 2^{n}} possible states. A vector representing all memory states has hence 2 n {textstyle 2^{n}} entries (one for each state). This vector should be viewed as a probability vector and represents the fact that the memory is to be found in a particular state.

In the classical view, one entry would have a value of 1 (i.e. a 100% probability of being in this state) and all other entries would be zero. In quantum mechanics, probability vectors are generalized to density operators. This is the technically rigorous mathematical foundation for quantum logic gates, but the intermediate quantum state vector formalism is usually introduced first because it is conceptually simpler. This article focuses on the quantum state vector formalism for simplicity.

We begin by considering a simple memory consisting of only one bit. This memory may be found in one of two states: the zero state or the one state. We may represent the state of this memory using Dirac notation so that

The state of this one-qubit quantum memory can be manipulated by applying quantum logic gates, analogous to how classical memory can be manipulated with classical logic gates. One important gate for both classical and quantum computation is the NOT gate, which can be represented by a matrix

The mathematics of single qubit gates can be extended to operate on multiqubit quantum memories in two important ways. One way is simply to select a qubit and apply that gate to the target qubit whilst leaving the remainder of the memory unaffected. Another way is to apply the gate to its target only if another part of the memory is in a desired state. These two choices can be illustrated using another example. The possible states of a two-qubit quantum memory are

In summary, a quantum computation can be described as a network of quantum logic gates and measurements. Any measurement can be deferred to the end of a quantum computation, though this deferment may come at a computational cost. Because of this possibility of deferring a measurement, most quantum circuits depict a network consisting only of quantum logic gates and no measurements. More information can be found in the following articles: universal quantum computer, Shor's algorithm, Grover's algorithm, DeutschJozsa algorithm, amplitude amplification, quantum Fourier transform, quantum gate, quantum adiabatic algorithm and quantum error correction.

Any quantum computation can be represented as a network of quantum logic gates from a fairly small family of gates. A choice of gate family that enables this construction is known as a universal gate set. One common such set includes all single-qubit gates as well as the CNOT gate from above. This means any quantum computation can be performed by executing a sequence of single-qubit gates together with CNOT gates. Though this gate set is infinite, it can be replaced with a finite gate set by appealing to the Solovay-Kitaev theorem.

Integer factorization, which underpins the security of public key cryptographic systems, is believed to be computationally infeasible with an ordinary computer for large integers if they are the product of few prime numbers (e.g., products of two 300-digit primes).[10] By comparison, a quantum computer could efficiently solve this problem using Shor's algorithm to find its factors. This ability would allow a quantum computer to break many of the cryptographic systems in use today, in the sense that there would be a polynomial time (in the number of digits of the integer) algorithm for solving the problem. In particular, most of the popular public key ciphers are based on the difficulty of factoring integers or the discrete logarithm problem, both of which can be solved by Shor's algorithm. In particular, the RSA, DiffieHellman, and elliptic curve DiffieHellman algorithms could be broken. These are used to protect secure Web pages, encrypted email, and many other types of data. Breaking these would have significant ramifications for electronic privacy and security.

However, other cryptographic algorithms do not appear to be broken by those algorithms.[11][12] Some public-key algorithms are based on problems other than the integer factorization and discrete logarithm problems to which Shor's algorithm applies, like the McEliece cryptosystem based on a problem in coding theory.[11][13] Lattice-based cryptosystems are also not known to be broken by quantum computers, and finding a polynomial time algorithm for solving the dihedral hidden subgroup problem, which would break many lattice based cryptosystems, is a well-studied open problem.[14] It has been proven that applying Grover's algorithm to break a symmetric (secret key) algorithm by brute force requires time equal to roughly 2n/2 invocations of the underlying cryptographic algorithm, compared with roughly 2n in the classical case,[15] meaning that symmetric key lengths are effectively halved: AES-256 would have the same security against an attack using Grover's algorithm that AES-128 has against classical brute-force search (see Key size).

Quantum cryptography could potentially fulfill some of the functions of public key cryptography. Quantum-based cryptographic systems could, therefore, be more secure than traditional systems against quantum hacking.[16]

Besides factorization and discrete logarithms, quantum algorithms offering a more than polynomial speedup over the best known classical algorithm have been found for several problems,[17] including the simulation of quantum physical processes from chemistry and solid state physics, the approximation of Jones polynomials, and solving Pell's equation. No mathematical proof has been found that shows that an equally fast classical algorithm cannot be discovered, although this is considered unlikely.[18] However, quantum computers offer polynomial speedup for some problems. The most well-known example of this is quantum database search, which can be solved by Grover's algorithm using quadratically fewer queries to the database than that are required by classical algorithms. In this case, the advantage is not only provable but also optimal, it has been shown that Grover's algorithm gives the maximal possible probability of finding the desired element for any number of oracle lookups. Several other examples of provable quantum speedups for query problems have subsequently been discovered, such as for finding collisions in two-to-one functions and evaluating NAND trees.

Problems that can be addressed with Grover's algorithm have the following properties:

For problems with all these properties, the running time of Grover's algorithm on a quantum computer will scale as the square root of the number of inputs (or elements in the database), as opposed to the linear scaling of classical algorithms. A general class of problems to which Grover's algorithm can be applied[19] is Boolean satisfiability problem. In this instance, the database through which the algorithm is iterating is that of all possible answers. An example (and possible) application of this is a password cracker that attempts to guess the password or secret key for an encrypted file or system. Symmetric ciphers such as Triple DES and AES are particularly vulnerable to this kind of attack.[citation needed] This application of quantum computing is a major interest of government agencies.[20]

Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate in an efficient manner classically, many believe quantum simulation will be one of the most important applications of quantum computing.[21] Quantum simulation could also be used to simulate the behavior of atoms and particles at unusual conditions such as the reactions inside a collider.[22]

Quantum annealing or Adiabatic quantum computation relies on the adiabatic theorem to undertake calculations. A system is placed in the ground state for a simple Hamiltonian, which is slowly evolved to a more complicated Hamiltonian whose ground state represents the solution to the problem in question. The adiabatic theorem states that if the evolution is slow enough the system will stay in its ground state at all times through the process.

The Quantum algorithm for linear systems of equations or "HHL Algorithm", named after its discoverers Harrow, Hassidim, and Lloyd, is expected to provide speedup over classical counterparts.[23]

John Preskill has introduced the term quantum supremacy to refer to the hypothetical speedup advantage that a quantum computer would have over a classical computer in a certain field.[24] Google announced in 2017 that it expected to achieve quantum supremacy by the end of the year though that did not happen. IBM said in 2018 that the best classical computers will be beaten on some practical task within about five years and views the quantum supremacy test only as a potential future benchmark.[25] Although skeptics like Gil Kalai doubt that quantum supremacy will ever be achieved,[26][27] in October 2019, a Sycamore processor created in conjunction with Google AI Quantum was reported to have achieved quantum supremacy,[28] with calculations more than 3,000,000 times as fast as those of Summit, generally considered the world's fastest computer.[29] Bill Unruh doubted the practicality of quantum computers in a paper published back in 1994.[30] Paul Davies argued that a 400-qubit computer would even come into conflict with the cosmological information bound implied by the holographic principle.[31]

There are a number of technical challenges in building a large-scale quantum computer,[32]. David DiVincenzo listed the following requirements for a practical quantum computer:[33]

Sourcing parts for quantum computers is very difficult: Quantum computers need Helium-3, a nuclear research byproduct, and special cables that are only made by a single company in Japan.[34]

One of the greatest challenges is controlling or removing quantum decoherence. This usually means isolating the system from its environment as interactions with the external world cause the system to decohere. However, other sources of decoherence also exist. Examples include the quantum gates, and the lattice vibrations and background thermonuclear spin of the physical system used to implement the qubits. Decoherence is irreversible, as it is effectively non-unitary, and is usually something that should be highly controlled, if not avoided. Decoherence times for candidate systems in particular, the transverse relaxation time T2 (for NMR and MRI technology, also called the dephasing time), typically range between nanoseconds and seconds at low temperature.[35] Currently, some quantum computers require their qubits to be cooled to 20 millikelvins in order to prevent significant decoherence.[36]

As a result, time-consuming tasks may render some quantum algorithms inoperable, as maintaining the state of qubits for a long enough duration will eventually corrupt the superpositions.[37]

These issues are more difficult for optical approaches as the timescales are orders of magnitude shorter and an often-cited approach to overcoming them is optical pulse shaping. Error rates are typically proportional to the ratio of operating time to decoherence time, hence any operation must be completed much more quickly than the decoherence time.

As described in the Quantum threshold theorem, if the error rate is small enough, it is thought to be possible to use quantum error correction to suppress errors and decoherence. This allows the total calculation time to be longer than the decoherence time if the error correction scheme can correct errors faster than decoherence introduces them. An often cited figure for the required error rate in each gate for fault-tolerant computation is 103, assuming the noise is depolarizing.

Meeting this scalability condition is possible for a wide range of systems. However, the use of error correction brings with it the cost of a greatly increased number of required qubits. The number required to factor integers using Shor's algorithm is still polynomial, and thought to be between L and L2, where L is the number of qubits in the number to be factored; error correction algorithms would inflate this figure by an additional factor of L. For a 1000-bit number, this implies a need for about 104 bits without error correction.[38] With error correction, the figure would rise to about 107 bits. Computation time is about L2 or about 107 steps and at 1MHz, about 10 seconds.

A very different approach to the stability-decoherence problem is to create a topological quantum computer with anyons, quasi-particles used as threads and relying on braid theory to form stable logic gates.[39][40]

Physicist Mikhail Dyakonov has expressed skepticism of quantum computing as follows:

There are a number of quantum computing models, distinguished by the basic elements in which the computation is decomposed. The four main models of practical importance are:

The quantum Turing machine is theoretically important but the direct implementation of this model is not pursued. All four models of computation have been shown to be equivalent; each can simulate the other with no more than polynomial overhead.

For physically implementing a quantum computer, many different candidates are being pursued, among them (distinguished by the physical system used to realize the qubits):

A large number of candidates demonstrates that the topic, in spite of rapid progress, is still in its infancy. There is also a vast amount of flexibility.

The class of problems that can be efficiently solved by quantum computers is called BQP, for "bounded error, quantum, polynomial time". Quantum computers only run probabilistic algorithms, so BQP on quantum computers is the counterpart of BPP ("bounded error, probabilistic, polynomial time") on classical computers. It is defined as the set of problems solvable with a polynomial-time algorithm, whose probability of error is bounded away from one half.[61] A quantum computer is said to "solve" a problem if, for every instance, its answer will be right with high probability. If that solution runs in polynomial time, then that problem is in BQP.

BQP is contained in the complexity class #P (or more precisely in the associated class of decision problems P#P),[62] which is a subclass of PSPACE.

BQP is suspected to be disjoint from NP-complete and a strict superset of P, but that is not known. Both integer factorization and discrete log are in BQP. Both of these problems are NP problems suspected to be outside BPP, and hence outside P. Both are suspected to not be NP-complete. There is a common misconception that quantum computers can solve NP-complete problems in polynomial time. That is not known to be true, and is generally suspected to be false.[62]

The capacity of a quantum computer to accelerate classical algorithms has rigid limitsupper bounds of quantum computation's complexity. The overwhelming part of classical calculations cannot be accelerated on a quantum computer.[63] A similar fact prevails for particular computational tasks, like the search problem, for which Grover's algorithm is optimal.[64]

Bohmian Mechanics is a non-local hidden variable interpretation of quantum mechanics. It has been shown that a non-local hidden variable quantum computer could implement a search of an N-item database at most in O ( N 3 ) {displaystyle O({sqrt[{3}]{N}})} steps. This is slightly faster than the O ( N ) {displaystyle O({sqrt {N}})} steps taken by Grover's algorithm. Neither search method will allow quantum computers to solve NP-Complete problems in polynomial time.[65]

Although quantum computers may be faster than classical computers for some problem types, those described above cannot solve any problem that classical computers cannot already solve. A Turing machine can simulate these quantum computers, so such a quantum computer could never solve an undecidable problem like the halting problem. The existence of "standard" quantum computers does not disprove the ChurchTuring thesis.[66] It has been speculated that theories of quantum gravity, such as M-theory or loop quantum gravity, may allow even faster computers to be built. Currently, defining computation in such theories is an open problem due to the problem of time, i.e., there currently exists no obvious way to describe what it means for an observer to submit input to a computer and later receive output.[67][68]

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Quantum computing - Wikipedia

On Oct. 23, 2019, Google published a paper in the journal Nature entitled Quantum supremacy using a programmable superconducting processor. The tech giant announced its achievement of a much vaunted goal: quantum supremacy.

This perhaps ill-chosen term (coined by physicist John Preskill) is meant to convey the huge speedup that processors based on quantum-mechanical systems are predicted to exhibit, relative to even the fastest classical computers.

Googles benchmark was achieved on a new type of quantum processor, code-named Sycamore, consisting of 54 independently addressable superconducting junction devices (of which only 53 were working for the demonstration).

Each of these devices allows the storage of one bit of quantum information. In contrast to the bits in a classical computer, which can only store one of two states (0 or 1 in the digital language of binary code), a quantum bit qbit can store information in a coherent superposition state which can be considered to contain fractional amounts of both 0 and 1.

Sycamore uses technology developed by the superconductivity research group of physicist John Martinis at the University of California, Santa Barbara. The entire Sycamore system must be kept cold at cryogenic temperatures using special helium dilution refrigeration technology. Because of the immense challenge involved in keeping such a large system near the absolute zero of temperature, it is a technological tour de force.

The Google researchers demonstrated that the performance of their quantum processor in sampling the output of a pseudo-random quantum circuit was vastly better than a classical computer chip like the kind in our laptops could achieve. Just how vastly became a point of contention, and the story was not without intrigue.

An inadvertent leak of the Google groups paper on the NASA Technical Reports Server (NTRS) occurred a month prior to publication, during the blackout period when Nature prohibits discussion by the authors regarding as-yet-unpublished papers. The lapse was momentary, but long enough that The Financial Times, The Verge and other outlets picked up the story.

A well-known quantum computing blog by computer scientist Scott Aaronson contained some oblique references to the leak. The reason for this obliqueness became clear when the paper was finally published online and Aaronson could at last reveal himself to be one of the reviewers.

The story had a further controversial twist when the Google groups claims were immediately countered by IBMs quantum computing group. IBM shared a preprint posted on the ArXiv (an online repository for academic papers that have yet to go through peer review) and a blog post dated Oct. 21, 2019 (note the date!).

While the Google group had claimed that a classical (super)computer would require 10,000 years to simulate the same 53-qbit random quantum circuit sampling task that their Sycamore processor could do in 200 seconds, the IBM researchers showed a method that could reduce the classical computation time to a mere matter of days.

However, the IBM classical computation would have to be carried out on the worlds fastest supercomputer the IBM-developed Summit OLCF-4 at Oak Ridge National Labs in Tennessee with clever use of secondary storage to achieve this benchmark.

While of great interest to researchers like myself working on hardware technologies related to quantum information, and important in terms of establishing academic bragging rights, the IBM-versus-Google aspect of the story is probably less relevant to the general public interested in all things quantum.

For the average citizen, the mere fact that a 53-qbit device could beat the worlds fastest supercomputer (containing more than 10,000 multi-core processors) is undoubtedly impressive. Now we must try to imagine what may come next.

The reality of quantum computing today is that very impressive strides have been made on the hardware front. A wide array of credible quantum computing hardware platforms now exist, including ion traps, superconducting device arrays similar to those in Googles Sycamore system and isolated electrons trapped in NV-centres in diamond.

These and other systems are all now in play, each with benefits and drawbacks. So far researchers and engineers have been making steady technological progress in developing these different hardware platforms for quantum computing.

What has lagged quite a bit behind are custom-designed algorithms (computer programs) designed to run on quantum computers and able to take full advantage of possible quantum speed-ups. While several notable quantum algorithms exist Shors algorithm for factorization, for example, which has applications in cryptography, and Grovers algorithm, which might prove useful in database search applications the total set of quantum algorithms remains rather small.

Much of the early interest (and funding) in quantum computing was spurred by the possibility of quantum-enabled advances in cryptography and code-breaking. A huge number of online interactions ranging from confidential communications to financial transactions require secure and encrypted messages, and modern cryptography relies on the difficulty of factoring large numbers to achieve this encryption.

Quantum computing could be very disruptive in this space, as Shors algorithm could make code-breaking much faster, while quantum-based encryption methods would allow detection of any eavesdroppers.

The interest various agencies have in unbreakable codes for secure military and financial communications has been a major driver of research in quantum computing. It is worth noting that all these code-making and code-breaking applications of quantum computing ignore to some extent the fact that no system is perfectly secure; there will always be a backdoor, because there will always be a non-quantum human element that can be compromised.

More appealing for the non-espionage and non-hacker communities in other words, the rest of us are the possible applications of quantum computation to solve very difficult problems that are effectively unsolvable using classical computers.

Ironically, many of these problems emerge when we try to use classical computers to solve quantum-mechanical problems, such as quantum chemistry problems that could be relevant for drug design and various challenges in condensed matter physics including a number related to high-temperature superconductivity.

So where are we in the wonderful and wild world of quantum computation?

In recent years, we have had many convincing demonstrations that qbits can be created, stored, manipulated and read using a number of futuristic-sounding quantum hardware platforms. But the algorithms lag. So while the prospect of quantum computing is fascinating, it will likely be a long time before we have quantum equivalents of the silicon chips that power our versatile modern computing devices.

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Google claims to have invented a quantum computer, but IBM begs to differ - The Conversation CA

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We are at an inflection point

Ever since Professor Alan Turing proposed the principle of the modern computer in 1936, computing has come a long way. While advancements to date have been promising, the future is even brighter, all thanks to quantum computing, which performs calculations based on the behaviour of particles at the sub-atomic level, writes Kalyan Kumar, CVP and CTO IT Services,HCL Technologies.

Quantum computing promises to unleash unimaginable computing power thats not only capable of addressing current computational limits, but unearthing new solutions to unsolved scientific and social mysteries. Whats more, thanks to increasing advancement since the 1980s, quantum computing can now drive some incredible social and business transformations.

Quantum computing holds immense promise in defining a positive, inclusive and human centric future, which is what theWEF Future Council on Quantum Computingenvisages. The most anticipated uses of quantum computing are driven by its potential to simulate quantum structures and behaviours across chemicals and materials. This promise is being seen guardedly by current scientists who claim quantum computing is still far from making a meaningful impact.

This said, quantum computing is expected to open amazing and much-needed possibilities in medical research. Drug development time, which usually takes more than 10 to 12 years with billions of dollars of investment, is expected to reduce considerably, alongside the potential to explore unique chemical compositions that may just be beyond the limits of current classical computing. Quantum computing can also help with more accurate weather forecasting, and provide accurate information that can help save tremendous amounts of agriculture production from damage.

Quantum computing promises a better and improved future, and while humans are poised to benefit greatly from this revolution, businesses too can expect unapparelled value.

When it comes to quantum computing, it can be said that much of the world is at the they dont know what they dont know stage. Proof points are appearing, and it is seemingly becoming clear that quantum computing solves problems that cannot be addressed by todays computers. Within transportation, for example, quantum computing is being used to develop battery and self-driving technologies, while Volkswagen has also been using quantum computing to match patterns and predict traffic conditions in advance, ensuring a smoother movement of traffic. In supply chains, logistics and trading are receiving a significant boost from the greater computing power and high-resolution modelling quantum computing provides, adding a huge amount of intelligence using new approaches to machine learning.

The possibilities for businesses are immense and go way beyond these examples mentioned above, in domains such as healthcare, financial services and IT. Yet a new approach is required. The companies that succeed in quantum computing will be those that create value chains to exploit the new insights, and form a management system to match the high-resolution view of the business that will emerge.

While there are some initial stage quantum devices already available, these are still far from what the world has been envisaging. Top multinational technology companies have been investing considerably in this field, but they still have some way to go. There has recently been talk of prototype quantum computers performing computations that would have previously taken 10,000 years in just 200 seconds. Though of course impressive, this is just one of the many steps needed to achieve the highest success in quantum computing.

It is vital to understand how and when we are going to adopt quantum computing, so we know the right time to act. The aforementioned prototype should be a wakeup call to early adopters who are seeking to find ways to create a durable competitive advantage. We even recently saw a business announcing its plans to make a prototype quantum computer available on its cloud, something we will all be able to buy or access some time from now. If organisations truly understand the value and applications of quantum computing, they will be able to create new products and services that nobody else has. However, productising and embedding quantum computing into products may take a little more time.

One important question arises from all this: are we witnessing the beginning of the end for classical computing? When looking at the facts, it seems not. With the advent of complete and practical quantum computers, were seeing a hybrid computing model emerging where digital binary computers will co-process and co-exist with quantum Qbit computers. The processing and resource sharing needs are expected to be optimised using real time analysis, where quantum takes over exponential computational tasks. To say the least, quantum computing is not about replacing digital computing, but about coexistence enabling composed computing that handles different tasks at the same time similar to humans having left and right brains for analytical and artistic dominance.

If one things for sure, its that we are at an inflection point, witnessing what could arguably be one of the most disruptive changes in human existence. Having a systematic and planned approach to adoption of quantum computing will not only take some of its mystery away, but reveal its true strategic value, helping us to know when and how to become part of this once in a lifetime revolution.

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What Is Quantum Computing, And How Can It Unlock Value For Businesses? - Computer Business Review

Among the healthcare technologies venture firms be looking at most closely at in 2020, various artificial intelligence and machine learning applications are atop this list, of course. But so are more nuts-and-bolts tools like administrative process automation and patient engagement platforms, VCs say.

Other, more leading-edge technologies genomics-focused data and analytics, and even quantum computing are among the areas attracting investor interest this year.

"We expect 2020 to mark the first year where health IT venture firms will start to look at quantum computing technology for upcoming solutions," Dr. Anis Uzzaman, CEO and general partner of Pegasus Tech Ventures, told Healthcare IT News.

"With the breakthrough supremacy announcement from Google validating the technology and the subsequent launch of the service Amazon Braket in 2019, there is sure to be a new wave of entrepreneurial activity starting in 2020."

He said quantum computing technology holds a lot of promise for the healthcare industry with potential breakthroughs possible throughout the health IT stack from operations and administration to security.

Among the promising companies, Uzzaman pointed to Palo Alto-based QC Ware, a startup pioneering a software solution that enables companies to use a variety of quantum hardware platforms such as Rigetti and IBM to solve a variety of enterprise problems, including those specifically related to healthcare.

He also predicted artificial intelligence would continue to be at the forefront for health IT venture firms in 2020 as it becomes more clear which startups may be winners in their initial target sectors.

"There has been consistent growth of investment activity over the past few years into healthcare startups using artificial intelligence to target a range of areas from imaging to diagnostics," he said.

However, Uzzaman also noted regulation and long enterprise sales cycles have largely slowed the ability for these companies to significantly scale their revenues.

"Therefore, we anticipate 2020 will be the year where it will become clearer to health IT venture firms who will be winners in applying artificial intelligence to imaging, pathology, genomics, operations, diagnostics, transcription, and more," he said. "We will also continue to see moderate growth in the overall investment amount in machine learning and AI companies, but will see a notable decrease in the number of companies receiving an investment.

Uzzaman explained there were already some signs in late 2019 that there could be late in a short-term innovation cycle for artificial intelligence with many companies, particularly those applying machine learning and AI to robotics, shutting down.

"However, we anticipate many companies will reach greater scale with their solutions and separate themselves from the competition, which will translate into more mega funding rounds," he said.

Ezra Mehlman, managing partner with Health Enterprise Partners, explained that at the beginning of each year, the firm conducts a market mapping exercise to determine which healthcare IT categories are rising to the top of the prioritization queue of its network of hospital and health plan limited partners.

"In the past year, we have seen budgets meaningfully open for automation solutions in administrative processing, genomics-focused data and analytics offerings, aging-in-place technologies and, in particular, patient engagement platforms rooted in proven clinical use cases," he said. "We are actively looking at all of these spaces."

He pointed out that in 2018, more than $2 billion was invested into artificial intelligence and machine learning healthcare IT companies, which represented a quarter of the total dollars invested into digital health companies that year.

"We view this as a recognition of two things: the meteoric aspirations that the market has assigned to AI and machine learning's potential, and a general sense that the underlying healthcare data infrastructure has reached the point of maturity, where it is possible to realize ROI from AI/machine learning initiatives," he said.

However, he said Health Enterprise Partners is still waiting for the "breakout" to occur in adoption.

"We believe we have now reached the point where category leaders will emerge in each major healthcare AI subsector and the usage will become more widespread we have made one such investment in the clinical AI space in the last year," Mehlman said.

Heading into 2020, Mehlman said companies that cannot deliver high-six-figure, year-one ROI in the form of increased revenue or reduced cost will struggle, and companies that cannot crisply answer the question, "Who is the buyer and what is the budget?" will be challenged.

"If one applies these tests to some of the areas that have attracted the most healthcare VC investment--social determinants of health, blockchain and digital therapeutics to name a few the number of viable companies sharply drops off," he said.

Mehlman noted that while these sound like simple principles, the current environment of rapidly consolidating, budget-constrained hospitals, vertically integrating health plans, and big tech companies making inroads into healthcare has raised the bar on what is required for a healthcare startup to gain meaningful market traction.

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Healthcare venture investment in 2020: Quantum computing gets a closer look - Healthcare IT News

Delta Air Lines is embarking on a multi-year collaborative effort with IBM including joining theIBM Q Networkto explore the potential capabilities of quantum computing to transform experiences for customers and employees.

"Partnering with innovative companies like IBM is one way Delta stays on the leading edge of tech to better serve our customers and our people, while drawing the blueprints for application across our industry," saidRahul Samant, Delta's CIO. "We've done this most recently with biometrics in our international terminals and we're excited to explore how quantum computing can be applied to address challenges across the day of travel."

TheIBM Q Network is a global community of Fortune 500 companies, startups, academic institutions and research labs working to advance quantum computing and explore practical applications.

Additionally, through theIBM Q Hub at NC State University, Delta will have access to the IBM Q Network's fleet of universal hardware quantum computersfor commercial use cases and fundamental research, including the recently-announced 53-qubit quantum computer, which, the company says, has the most qubits of a universal quantum computer available for external access in the industry, to date.

"We are very excited by the addition of Delta to our list of collaborators working with us on building practical quantum computing applications," said director of IBM ResearchDario Gil. "IBM's focus, since we put the very first quantum computer on the cloud in 2016, has been to move quantum computing beyond isolated lab experiments conducted by a handful of organizations, into the hands of tens of thousands of users. We believe a clear advantage will be awarded to early adopters in the era of quantum computing and with partners like Delta, we're already making significant progress on that mission."

For more information about the IBM Q Network, go to http://www.ibm.com/quantum-computing/network/overview

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Delta Partners with IBM to Explore Quantum Computing - Database Trends and Applications