Quantum computing is an area of study focused on the development of computer based technologies centered around the principles ofquantum theory. Quantum theory explains the nature and behavior of energy and matter on thequantum(atomic and subatomic) level. Quantum computing uses a combination ofbitsto perform specific computational tasks. All at a much higher efficiency than their classical counterparts. Development ofquantum computersmark a leap forward in computing capability, with massive performance gains for specific use cases. For example quantum computing excels at like simulations.

The quantum computer gains much of its processing power through the ability for bits to be in multiple states at one time. They can perform tasks using a combination of 1s, 0s and both a 1 and 0 simultaneously. Current research centers in quantum computing include MIT, IBM, Oxford University, and the Los Alamos National Laboratory. In addition, developers have begun gaining access toquantum computers through cloud services.

Quantum computing began with finding its essential elements. In 1981, Paul Benioff at Argonne National Labs came up with the idea of a computer that operated with quantum mechanical principles. It is generally accepted that David Deutsch of Oxford University provided the critical idea behind quantum computing research. In 1984, he began to wonder about the possibility of designing a computer that was based exclusively on quantum rules, publishing a breakthrough paper a few months later.

Quantum Theory

Quantum theory's development began in 1900 with a presentation by Max Planck. The presentation was to the German Physical Society, in which Planck introduced the idea that energy and matter exists in individual units. Further developments by a number of scientists over the following thirty years led to the modern understanding of quantum theory.

Quantum Theory

Quantum theory's development began in 1900 with a presentation by Max Planck. The presentation was to the German Physical Society, in which Planck introduced the idea that energy and matter exists in individual units. Further developments by a number of scientists over the following thirty years led to the modern understanding of quantum theory.

The Essential Elements of Quantum Theory:

Further Developments of Quantum Theory

Niels Bohr proposed the Copenhagen interpretation of quantum theory. This theory asserts that a particle is whatever it is measured to be, but that it cannot be assumed to have specific properties, or even to exist, until it is measured. This relates to a principle called superposition. Superposition claims when we do not know what the state of a given object is, it is actually in all possible states simultaneously -- as long as we don't look to check.

To illustrate this theory, we can use the famous analogy of Schrodinger's Cat. First, we have a living cat and place it in a lead box. At this stage, there is no question that the cat is alive. Then throw in a vial of cyanide and seal the box. We do not know if the cat is alive or if it has broken the cyanide capsule and died. Since we do not know, the cat is both alive and dead, according to quantum law -- in a superposition of states. It is only when we break open the box and see what condition the cat is in that the superposition is lost, and the cat must be either alive or dead.

The principle that, in some way, one particle can exist in numerous states opens up profound implications for computing.

A Comparison of Classical and Quantum Computing

Classical computing relies on principles expressed by Boolean algebra; usually Operating with a 3 or 7-modelogic gateprinciple. Data must be processed in an exclusive binary state at any point in time; either 0 (off / false) or 1 (on / true). These values are binary digits, or bits. The millions of transistors and capacitors at the heart of computers can only be in one state at any point. In addition, there is still a limit as to how quickly these devices can be made to switch states. As we progress to smaller and faster circuits, we begin to reach the physical limits of materials and the threshold for classical laws of physics to apply.

The quantum computer operates with a two-mode logic gate:XORand a mode called QO1 (the ability to change 0 into a superposition of 0 and 1). In a quantum computer, a number of elemental particles such as electrons or photons can be used. Each particle is given a charge, or polarization, acting as a representation of 0 and/or 1. Each particle is called a quantum bit, or qubit. The nature and behavior of these particles form the basis of quantum computing and quantum supremacy. The two most relevant aspects of quantum physics are the principles of superposition andentanglement.

Superposition

Think of a qubit as an electron in a magnetic field. The electron's spin may be either in alignment with the field, which is known as aspin-upstate, or opposite to the field, which is known as aspin-downstate. Changing the electron's spin from one state to another is achieved by using a pulse of energy, such as from alaser. If only half a unit of laser energy is used, and the particle is isolated the particle from all external influences, the particle then enters a superposition of states. Behaving as if it were in both states simultaneously.

Each qubit utilized could take a superposition of both 0 and 1. Meaning, the number of computations a quantum computer could take is 2^n, where n is the number of qubits used. A quantum computer comprised of 500 qubits would have a potential to do 2^500 calculations in a single step. For reference, 2^500 is infinitely more atoms than there are in the known universe. These particles all interact with each other via quantum entanglement.

In comparison to classical, quantum computing counts as trueparallel processing. Classical computers today still only truly do one thing at a time. In classical computing, there are just two or more processors to constitute parallel processing.EntanglementParticles (like qubits) that have interacted at some point retain a type can be entangled with each other in pairs, in a process known ascorrelation. Knowing the spin state of one entangled particle - up or down -- gives away the spin of the other in the opposite direction. In addition, due to the superposition, the measured particle has no single spin direction before being measured. The spin state of the particle being measured is determined at the time of measurement and communicated to the correlated particle, which simultaneously assumes the opposite spin direction. The reason behind why is not yet explained.

Quantum entanglement allows qubits that are separated by large distances to interact with each other instantaneously (not limited to the speed of light). No matter how great the distance between the correlated particles, they will remain entangled as long as they are isolated.

Taken together, quantum superposition and entanglement create an enormously enhanced computing power. Where a 2-bit register in an ordinary computer can store only one of four binary configurations (00, 01, 10, or 11) at any given time, a 2-qubit register in a quantum computer can store all four numbers simultaneously. This is because each qubit represents two values. If more qubits are added, the increased capacity is expanded exponentially.

Quantum Programming

Quantum computing offers an ability to write programs in a completely new way. For example, a quantum computer could incorporate a programming sequence that would be along the lines of "take all the superpositions of all the prior computations." This would permit extremely fast ways of solving certain mathematical problems, such as factorization of large numbers.

The first quantum computing program appeared in 1994 by Peter Shor, who developed a quantum algorithm that could efficiently factorize large numbers.

The Problems - And Some Solutions

The benefits of quantum computing are promising, but there are huge obstacles to overcome still. Some problems with quantum computing are:

There are many problems to overcome, such as how to handle security and quantum cryptography. Long time quantum information storage has been a problem in the past too. However, breakthroughs in the last 15 years and in the recent past have made some form of quantum computing practical. There is still much debate as to whether this is less than a decade away or a hundred years into the future. However, the potential that this technology offers is attracting tremendous interest from both the government and the private sector. Military applications include the ability to break encryptions keys via brute force searches, while civilian applications range from DNA modeling to complex material science analysis.

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What is quantum computing? - TechTarget

Rigetti and Riverlane Receive a 500 Thousand ($613K USD) Grant to Work on Error Correction

Rigetti Computing and Riverlane have received this grant from Innovate UK, the UKs national innovation agency, to study syndrome extraction on superconducting quantum computers. This would be a critical step for providing error correction on the qubits in a fault tolerant quantum computer. Since a qubit cannot be measured directly without collapsing quantum error correction circuits have to be made more complex than their classical counterparts. A common method is to include additional qubits in the circuit called ancilla (or auxiliary) qubits that can be entangled with the data qubits and subsequently measured to form a syndrome pattern. This syndrome pattern can indicate if there is an error within the data qubits and what qubits are affected. Additional gates can then be applied to the data qubits based upon the syndrom to fix the data qubits and correct the errors. The beauty of this approach is that while the ancilla qubits are measured the data qubits are not measured so they remain in the quantum state and dont collapse. This research from Rigetti and Riverlane will explore ways of implementing this error correction process while minimizing any additional errors that could result from the syndrome extraction process itself. For more about this grant and research project, you can view a press release located here.

June 27, 2022

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Rigetti and Riverlane Receive a 500 Thousand ($613K USD) Grant to Work on Error Correction - Quantum Computing Report

Dates chiseled into an ancient tombstone have more in common with the data in your phone or laptop than you may realize. They both involve conventional, classical information, carried by hardware that is relatively immune to errors. The situation inside a quantum computer is far different: The information itself has its own idiosyncratic properties, and compared with standard digital microelectronics, state-of-the-art quantum-computer hardware is more than a billion trillion times as likely to suffer a fault. This tremendous susceptibility to errors is the single biggest problem holding back quantum computing from realizing its great promise.

Fortunately, an approach known as quantum error correction (QEC) can remedy this problem, at least in principle. A mature body of theory built up over the past quarter century now provides a solid theoretical foundation, and experimentalists have demonstrated dozens of proof-of-principle examples of QEC. But these experiments still have not reached the level of quality and sophistication needed to reduce the overall error rate in a system.

The two of us, along with many other researchers involved in quantum computing, are trying to move definitively beyond these preliminary demos of QEC so that it can be employed to build useful, large-scale quantum computers. But before describing how we think such error correction can be made practical, we need to first review what makes a quantum computer tick.

Information is physical. This was the mantra of the distinguished IBM researcher Rolf Landauer. Abstract though it may seem, information always involves a physical representation, and the physics matters.

Conventional digital information consists of bits, zeros and ones, which can be represented by classical states of matter, that is, states well described by classical physics. Quantum information, by contrast, involves qubitsquantum bitswhose properties follow the peculiar rules of quantum mechanics.

A classical bit has only two possible values: 0 or 1. A qubit, however, can occupy a superposition of these two information states, taking on characteristics of both. Polarized light provides intuitive examples of superpositions. You could use horizontally polarized light to represent 0 and vertically polarized light to represent 1, but light can also be polarized on an angle and then has both horizontal and vertical components at once. Indeed, one way to represent a qubit is by the polarization of a single photon of light.

These ideas generalize to groups of n bits or qubits: n bits can represent any one of 2n possible values at any moment, while n qubits can include components corresponding to all 2n classical states simultaneously in superposition. These superpositions provide a vast range of possible states for a quantum computer to work with, albeit with limitations on how they can be manipulated and accessed. Superposition of information is a central resource used in quantum processing and, along with other quantum rules, enables powerful new ways to compute.

Researchers are experimenting with many different physical systems to hold and process quantum information, including light, trapped atoms and ions, and solid-state devices based on semiconductors or superconductors. For the purpose of realizing qubits, all these systems follow the same underlying mathematical rules of quantum physics, and all of them are highly sensitive to environmental fluctuations that introduce errors. By contrast, the transistors that handle classical information in modern digital electronics can reliably perform a billion operations per second for decades with a vanishingly small chance of a hardware fault.

Of particular concern is the fact that qubit states can roam over a continuous range of superpositions. Polarized light again provides a good analogy: The angle of linear polarization can take any value from 0 to 180 degrees.

Pictorially, a qubits state can be thought of as an arrow pointing to a location on the surface of a sphere. Known as a Bloch sphere, its north and south poles represent the binary states 0 and 1, respectively, and all other locations on its surface represent possible quantum superpositions of those two states. Noise causes the Bloch arrow to drift around the sphere over time. A conventional computer represents 0 and 1 with physical quantities, such as capacitor voltages, that can be locked near the correct values to suppress this kind of continuous wandering and unwanted bit flips. There is no comparable way to lock the qubits arrow to its correct location on the Bloch sphere.

Early in the 1990s, Landauer and others argued that this difficulty presented a fundamental obstacle to building useful quantum computers. The issue is known as scalability: Although a simple quantum processor performing a few operations on a handful of qubits might be possible, could you scale up the technology to systems that could run lengthy computations on large arrays of qubits? A type of classical computation called analog computing also uses continuous quantities and is suitable for some tasks, but the problem of continuous errors prevents the complexity of such systems from being scaled up. Continuous errors with qubits seemed to doom quantum computers to the same fate.

We now know better. Theoreticians have successfully adapted the theory of error correction for classical digital data to quantum settings. QEC makes scalable quantum processing possible in a way that is impossible for analog computers. To get a sense of how it works, its worthwhile to review how error correction is performed in classical settings.

Simple schemes can deal with errors in classical information. For instance, in the 19th century, ships routinely carried clocks for determining the ships longitude during voyages. A good clock that could keep track of the time in Greenwich, in combination with the suns position in the sky, provided the necessary data. A mistimed clock could lead to dangerous navigational errors, though, so ships often carried at least three of them. Two clocks reading different times could detect when one was at fault, but three were needed to identify which timepiece was faulty and correct it through a majority vote.

The use of multiple clocks is an example of a repetition code: Information is redundantly encoded in multiple physical devices such that a disturbance in one can be identified and corrected.

As you might expect, quantum mechanics adds some major complications when dealing with errors. Two problems in particular might seem to dash any hopes of using a quantum repetition code. The first problem is that measurements fundamentally disturb quantum systems. So if you encoded information on three qubits, for instance, observing them directly to check for errors would ruin them. Like Schrdingers cat when its box is opened, their quantum states would be irrevocably changed, spoiling the very quantum features your computer was intended to exploit.

The second issue is a fundamental result in quantum mechanics called the no-cloning theorem, which tells us it is impossible to make a perfect copy of an unknown quantum state. If you know the exact superposition state of your qubit, there is no problem producing any number of other qubits in the same state. But once a computation is running and you no longer know what state a qubit has evolved to, you cannot manufacture faithful copies of that qubit except by duplicating the entire process up to that point.

Fortunately, you can sidestep both of these obstacles. Well first describe how to evade the measurement problem using the example of a classical three-bit repetition code. You dont actually need to know the state of every individual code bit to identify which one, if any, has flipped. Instead, you ask two questions: Are bits 1 and 2 the same? and Are bits 2 and 3 the same? These are called parity-check questions because two identical bits are said to have even parity, and two unequal bits have odd parity.

The two answers to those questions identify which single bit has flipped, and you can then counterflip that bit to correct the error. You can do all this without ever determining what value each code bit holds. A similar strategy works to correct errors in a quantum system.

Learning the values of the parity checks still requires quantum measurement, but importantly, it does not reveal the underlying quantum information. Additional qubits can be used as disposable resources to obtain the parity values without revealing (and thus without disturbing) the encoded information itself.

Like Schrdingers cat when its box is opened, the quantum states of the qubits you measured would be irrevocably changed, spoiling the very quantum features your computer was intended to exploit.

What about no-cloning? It turns out it is possible to take a qubit whose state is unknown and encode that hidden state in a superposition across multiple qubits in a way that does not clone the original information. This process allows you to record what amounts to a single logical qubit of information across three physical qubits, and you can perform parity checks and corrective steps to protect the logical qubit against noise.

Quantum errors consist of more than just bit-flip errors, though, making this simple three-qubit repetition code unsuitable for protecting against all possible quantum errors. True QEC requires something more. That came in the mid-1990s when Peter Shor (then at AT&T Bell Laboratories, in Murray Hill, N.J.) described an elegant scheme to encode one logical qubit into nine physical qubits by embedding a repetition code inside another code. Shors scheme protects against an arbitrary quantum error on any one of the physical qubits.

Since then, the QEC community has developed many improved encoding schemes, which use fewer physical qubits per logical qubitthe most compact use fiveor enjoy other performance enhancements. Today, the workhorse of large-scale proposals for error correction in quantum computers is called the surface code, developed in the late 1990s by borrowing exotic mathematics from topology and high-energy physics.

It is convenient to think of a quantum computer as being made up of logical qubits and logical gates that sit atop an underlying foundation of physical devices. These physical devices are subject to noise, which creates physical errors that accumulate over time. Periodically, generalized parity measurements (called syndrome measurements) identify the physical errors, and corrections remove them before they cause damage at the logical level.

A quantum computation with QEC then consists of cycles of gates acting on qubits, syndrome measurements, error inference, and corrections. In terms more familiar to engineers, QEC is a form of feedback stabilization that uses indirect measurements to gain just the information needed to correct errors.

QEC is not foolproof, of course. The three-bit repetition code, for example, fails if more than one bit has been flipped. Whats more, the resources and mechanisms that create the encoded quantum states and perform the syndrome measurements are themselves prone to errors. How, then, can a quantum computer perform QEC when all these processes are themselves faulty?

Remarkably, the error-correction cycle can be designed to tolerate errors and faults that occur at every stage, whether in the physical qubits, the physical gates, or even in the very measurements used to infer the existence of errors! Called a fault-tolerant architecture, such a design permits, in principle, error-robust quantum processing even when all the component parts are unreliable.

A long quantum computation will require many cycles of quantum error correction (QEC). Each cycle would consist of gates acting on encoded qubits (performing the computation), followed by syndrome measurements from which errors can be inferred, and corrections. The effectiveness of this QEC feedback loop can be greatly enhanced by including quantum-control techniques (represented by the thick blue outline) to stabilize and optimize each of these processes.

Even in a fault-tolerant architecture, the additional complexity introduces new avenues for failure. The effect of errors is therefore reduced at the logical level only if the underlying physical error rate is not too high. The maximum physical error rate that a specific fault-tolerant architecture can reliably handle is known as its break-even error threshold. If error rates are lower than this threshold, the QEC process tends to suppress errors over the entire cycle. But if error rates exceed the threshold, the added machinery just makes things worse overall.

The theory of fault-tolerant QEC is foundational to every effort to build useful quantum computers because it paves the way to building systems of any size. If QEC is implemented effectively on hardware exceeding certain performance requirements, the effect of errors can be reduced to arbitrarily low levels, enabling the execution of arbitrarily long computations.

At this point, you may be wondering how QEC has evaded the problem of continuous errors, which is fatal for scaling up analog computers. The answer lies in the nature of quantum measurements.

In a typical quantum measurement of a superposition, only a few discrete outcomes are possible, and the physical state changes to match the result that the measurement finds. With the parity-check measurements, this change helps.

Imagine you have a code block of three physical qubits, and one of these qubit states has wandered a little from its ideal state. If you perform a parity measurement, just two results are possible: Most often, the measurement will report the parity state that corresponds to no error, and after the measurement, all three qubits will be in the correct state, whatever it is. Occasionally the measurement will instead indicate the odd parity state, which means an errant qubit is now fully flipped. If so, you can flip that qubit back to restore the desired encoded logical state.

In other words, performing QEC transforms small, continuous errors into infrequent but discrete errors, similar to the errors that arise in digital computers.

Researchers have now demonstrated many of the principles of QEC in the laboratoryfrom the basics of the repetition code through to complex encodings, logical operations on code words, and repeated cycles of measurement and correction. Current estimates of the break-even threshold for quantum hardware place it at about 1 error in 1,000 operations. This level of performance hasnt yet been achieved across all the constituent parts of a QEC scheme, but researchers are getting ever closer, achieving multiqubit logic with rates of fewer than about 5 errors per 1,000 operations. Even so, passing that critical milestone will be the beginning of the story, not the end.

On a system with a physical error rate just below the threshold, QEC would require enormous redundancy to push the logical rate down very far. It becomes much less challenging with a physical rate further below the threshold. So just crossing the error threshold is not sufficientwe need to beat it by a wide margin. How can that be done?

If we take a step back, we can see that the challenge of dealing with errors in quantum computers is one of stabilizing a dynamic system against external disturbances. Although the mathematical rules differ for the quantum system, this is a familiar problem in the discipline of control engineering. And just as control theory can help engineers build robots capable of righting themselves when they stumble, quantum-control engineering can suggest the best ways to implement abstract QEC codes on real physical hardware. Quantum control can minimize the effects of noise and make QEC practical.

In essence, quantum control involves optimizing how you implement all the physical processes used in QECfrom individual logic operations to the way measurements are performed. For example, in a system based on superconducting qubits, a qubit is flipped by irradiating it with a microwave pulse. One approach uses a simple type of pulse to move the qubits state from one pole of the Bloch sphere, along the Greenwich meridian, to precisely the other pole. Errors arise if the pulse is distorted by noise. It turns out that a more complicated pulse, one that takes the qubit on a well-chosen meandering route from pole to pole, can result in less error in the qubits final state under the same noise conditions, even when the new pulse is imperfectly implemented.

One facet of quantum-control engineering involves careful analysis and design of the best pulses for such tasks in a particular imperfect instance of a given system. It is a form of open-loop (measurement-free) control, which complements the closed-loop feedback control used in QEC.

This kind of open-loop control can also change the statistics of the physical-layer errors to better comport with the assumptions of QEC. For example, QEC performance is limited by the worst-case error within a logical block, and individual devices can vary a lot. Reducing that variability is very beneficial. In an experiment our team performed using IBMs publicly accessible machines, we showed that careful pulse optimization reduced the difference between the best-case and worst-case error in a small group of qubits by more than a factor of 10.

Some error processes arise only while carrying out complex algorithms. For instance, crosstalk errors occur on qubits only when their neighbors are being manipulated. Our team has shown that embedding quantum-control techniques into an algorithm can improve its overall success by orders of magnitude. This technique makes QEC protocols much more likely to correctly identify an error in a physical qubit.

For 25 years, QEC researchers have largely focused on mathematical strategies for encoding qubits and efficiently detecting errors in the encoded sets. Only recently have investigators begun to address the thorny question of how best to implement the full QEC feedback loop in real hardware. And while many areas of QEC technology are ripe for improvement, there is also growing awareness in the community that radical new approaches might be possible by marrying QEC and control theory. One way or another, this approach will turn quantum computing into a realityand you can carve that in stone.

This article appears in the July 2022 print issue as Quantum Error Correction at the Threshold.

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Quantum Error Correction: Time to Make It Work - IEEE Spectrum

COLLEGE PARK, Md.--(BUSINESS WIRE)--IonQ (NYSE: IONQ), an industry leader in quantum computing, today announced promising early results with its partner, GE Research, to explore the benefits of quantum computing for modeling multi-variable distributions in risk management.

Leveraging a Quantum Circuit Born Machine-based framework on standardized, historical indexes, IonQ and GE Research, the central innovation hub for the General Electric Company (NYSE: GE), were able to effectively train quantum circuits to learn correlations among three and four indexes. The prediction derived from the quantum framework outperformed those of classical modeling approaches in some cases, confirming that quantum copulas can potentially lead to smarter data-driven analysis and decision-making across commercial applications. A blog post further explaining the research methodology and results is available here.

Together with GE Research, IonQ is pushing the boundaries of what is currently possible to achieve with quantum computing, said Peter Chapman, CEO and President, IonQ. While classical techniques face inefficiencies when multiple variables have to be modeled together with high precision, our joint effort has identified a new training strategy that may optimize quantum computing results even as systems scale. Tested on our industry-leading IonQ Aria system, were excited to apply these new methodologies when tackling real world scenarios that were once deemed too complex to solve.

While classical techniques to form copulas using mathematical approximations are a great way to build multi-variate risk models, they face limitations when scaling. IonQ and GE Research successfully trained quantum copula models with up to four variables on IonQs trapped ion systems by using data from four representative stock indexes with easily accessible and variating market environments.

By studying the historical dependence structure among the returns of the four indexes during this timeframe, the research group trained its model to understand the underlying dynamics. Additionally, the newly presented methodology includes optimization techniques that potentially allow models to scale by mitigating local minima and vanishing gradient problems common in quantum machine learning practices. Such improvements demonstrate a promising way to perform multi-variable analysis faster and more accurately, which GE researchers hope lead to new and better ways to assess risk with major manufacturing processes such as product design, factory operations, and supply chain management.

As we have seen from recent global supply chain volatility, the world needs more effective methods and tools to manage risks where conditions can be so highly variable and interconnected to one another, said David Vernooy, a Senior Executive and Digital Technologies Leader at GE Research. The early results we achieved in the financial use case with IonQ show the high potential of quantum computing to better understand and reduce the risks associated with these types of highly variable scenarios.

Todays results follow IonQs recent announcement of the companys new IonQ Forte quantum computing system. The system features novel, cutting-edge optics technology that enables increased accuracy and further enhances IonQs industry leading system performance. Partnerships with the likes of GE Research and Hyundai Motors illustrate the growing interest in our industry-leading systems and feeds into the continued success seen in Q1 2022.

About IonQ

IonQ, Inc. is a leader in quantum computing, with a proven track record of innovation and deployment. IonQ's current generation quantum computer, IonQ Forte, is the latest in a line of cutting-edge systems, including IonQ Aria, a system that boasts industry-leading 20 algorithmic qubits. Along with record performance, IonQ has defined what it believes is the best path forward to scale. IonQ is the only company with its quantum systems available through the cloud on Amazon Braket, Microsoft Azure, and Google Cloud, as well as through direct API access. IonQ was founded in 2015 by Christopher Monroe and Jungsang Kim based on 25 years of pioneering research. To learn more, visit http://www.ionq.com.

IonQ Forward-Looking Statements

This press release contains certain forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Some of the forward-looking statements can be identified by the use of forward-looking words. Statements that are not historical in nature, including the words anticipate, expect, suggests, plan, believe, intend, estimates, targets, projects, should, could, would, may, will, forecast and other similar expressions are intended to identify forward-looking statements. These statements include those related to IonQs ability to further develop and advance its quantum computers and achieve scale; IonQs ability to optimize quantum computing results even as systems scale; the expected launch of IonQ Forte for access by select developers, partners, and researchers in 2022 with broader customer access expected in 2023; IonQs market opportunity and anticipated growth; and the commercial benefits to customers of using quantum computing solutions. Forward-looking statements are predictions, projections and other statements about future events that are based on current expectations and assumptions and, as a result, are subject to risks and uncertainties. Many factors could cause actual future events to differ materially from the forward-looking statements in this press release, including but not limited to: market adoption of quantum computing solutions and IonQs products, services and solutions; the ability of IonQ to protect its intellectual property; changes in the competitive industries in which IonQ operates; changes in laws and regulations affecting IonQs business; IonQs ability to implement its business plans, forecasts and other expectations, and identify and realize additional partnerships and opportunities; and the risk of downturns in the market and the technology industry including, but not limited to, as a result of the COVID-19 pandemic. The foregoing list of factors is not exhaustive. You should carefully consider the foregoing factors and the other risks and uncertainties described in the Risk Factors section of IonQs Quarterly Report on Form 10-Q for the quarter ended March 31, 2022 and other documents filed by IonQ from time to time with the Securities and Exchange Commission. These filings identify and address other important risks and uncertainties that could cause actual events and results to differ materially from those contained in the forward-looking statements. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and IonQ assumes no obligation and does not intend to update or revise these forward-looking statements, whether as a result of new information, future events, or otherwise. IonQ does not give any assurance that it will achieve its expectations.

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IonQ and GE Research Demonstrate High Potential of Quantum Computing for Risk Aggregation - Business Wire

Israeli Team8 venture group officially opened this years Cyber Week with an event that took place in Tel Aviv on Sunday. The event, which included international guests and cybersecurity professionals, showcased the country and the industry as a powerhouse in relation to Startup Nation.

Opening remarks were made by Niv Sultan, star of Apple TVs Tehran, who also moderated the event. She then welcomed Gili Drob-Heinstein, Executive Director at the Blavatnik Interdisciplinary Cyber Research Center (ICRC) at Tel Aviv University, and Nadav Zafrir, Co-founder of Team8 and Managing Partner of Team8 Platform to the stage.

I would like to thank the 100 CSOs who came to stay with us, Zafrir said on stage. Guests from around the world had flown into Israel and spent time connecting with one another ahead of the official start of Cyber Week on Monday. Team8 was also celebrating its 8th year as a VC, highlighting the work it has done in the cybersecurity arena.

The stage was then filled with Admiral Mike Rogers and Nir Minerbi, Co-founder and CEO of Classiq, who together discussed The Quantum Opportunity in computing. Classical computers are great, but for some of the most complex challenges humanity is facing, they are not suitable, said Minerbi. Quantum computing will revolutionize every large industry.

Classiq develops software for quantum algorithms. Founded in 2020, it has raised a total of $51 million and is funded by Team8 among other VC players in the space. Admiral Mike Rogers is the Former Director of American agency the NSA and is an Operating Partner at Team8.

We are in a race, Rogers told the large crowd. This is a technology believed to have advantages for our daily lives and national security. I told both presidents I worked under why they should invest billions into quantum, citing the ability to look at multiple qubits simultaneously thus speeding up the ability to process information. According to Rogers, governments have already publicly announced $29 billion of funding to help develop quantum computing.

Final remarks were made by Renee Wynn, former CIO at NASA, who discussed the potential of cyber in space. Space may be the final frontier, and if we do not do anything else than what we are doing now, it will be chaos 100 miles above your head, she warned. On stage, she spoke to the audience about the threats in space and how satellites could be hijacked for nefarious reasons.

Cybersecurity and satellites are so important, she concluded. Lets bring the space teams together with the cybersecurity teams and help save lives.

After the remarks, the stage was then transformed to host the evenings entertainment. Israeli-American puppet band Red Band performed a variety of songs and was then joined by Marina Maximilian, an Israeli singer-songwriter and actress, who shared the stage with the colorful puppets.

The event was sponsored by Meitar, Delloitte, LeumiTech, Valley, Palo Alto, FinSec Innovation Lab, and SentinelOne. It marked the beginning of Cyber Week, a three-day conference hosted by Tel Aviv University that will welcome a variety of cybersecurity professionals for workshops, networking opportunities, and panel discussions. It is understood that this year will have 9,000 attendees, 400 speakers, and host people from 80 different countries.

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Red Band performing 'Seven Nation Army'.

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Quantum computing will revolutionize every large industry - CTech

Covina, June 22, 2022 (GLOBE NEWSWIRE) -- The discovery of potential COVID-19 therapeutics has a bright future due toquantum computing. New approaches to drug discovery are being investigated with funding from the Penn State Institute for Computational and Data Sciences, coordinated through the Penn State Huck Institutes of the Life Sciences. For businesses in the quantum computing market, these tendencies are turning into lucrative opportunities during forecast period. Research initiatives that are assisting in the screening of billions of chemical compounds to uncover suitable medication candidates have been made possible by the convergence of machine learning and quantum physics. Stakeholders in the quantum computing business are expanding the availability of supercomputers and growing R&D in artificial intelligence to support these studies (AI). The energy and electricity sector offers lucrative potential for businesses in the quantum computing market. As regard to whole assets, work overs, and infrastructure, this technology is assisting players in the energy and power sector in making crucial investment decisions. Budgetary considerations, resource constraints, and contractual commitments may all be factors in these issues that quantum computing can help to resolve.

Region Analysis:

North America is predicted to hold a large market share for quantum computing due to its early adoption of cutting-edge technology. Additionally, the existence of a competitive market and end-user acceptance of cutting-edge technology may promote market growth. Sales are anticipated to increase throughout Europe as a result of the rise of multiple startups, favourable legislative conditions, and the growing use of cloud technology. In addition, it is anticipated that leading companies' company expansion will accelerate market growth. The market is anticipated to grow in Asia Pacific as a result of the growing need for quantum computing solutions for simulation, optimization, and machine learning.

Key Highlights:

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Key Market Insights from the report:

Global Quantum Computing Market size accounted for US$ 387.3 billion in 2020 and is estimated to be US$ 4531.04 billion by 2030 and is anticipated to register a CAGR of 28.2%.The Global Quantum Computing Market is segmented based on component, application, end-user industry and region.

Competitive Landscape & their strategies of Quantum Computing Market:

Key players in the global quantum computing market include Wave Systems Corp, 1QB Information Technologies Inc, QC Ware, Corp, Google Inc, QxBranch LLC, Microsoft Corporation, International Business Machines Corporation, Huawei Technologies Co., Ltd, ID Quantique SA, and Atos SE.

Scope of the Report:

Global Quantum Computing Market, By Component, 2019 2029, (US$ Mn)

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Global Quantum Computing Market is estimated to be US$ 4531.04 billion by 2030 with a CAGR of 28.2% during the forecast period - By PMI -...

Perhaps the most famously weird feature of quantum mechanics is nonlocality: Measure one particle in an entangled pair whose partner is miles away, and the measurement seems to rip through the intervening space to instantaneously affect its partner. This spooky action at a distance (as Albert Einstein called it) has been the main focus of tests of quantum theory.

Nonlocality is spectacular. I mean, its like magic, said Adn Cabello, a physicist at the University of Seville in Spain.

But Cabello and others are interested in investigating a lesser-known but equally magical aspect of quantum mechanics: contextuality. Contextuality says that properties of particles, such as their position or polarization, exist only within the context of a measurement. Instead of thinking of particles properties as having fixed values, consider them more like words in language, whose meanings can change depending on the context: Timeflies likean arrow. Fruitflies likebananas.

Although contextuality has lived in nonlocalitys shadow for over 50 years, quantum physicists now consider it more of a hallmark feature of quantum systems than nonlocality is. A single particle, for instance, is a quantum system in which you cannot even think about nonlocality, since the particle is only in one location, said Brbara Amaral, a physicist at the University of So Paulo in Brazil. So [contextuality] is more general in some sense, and I think this is important to really understand the power of quantum systems and to go deeper into why quantum theory is the way it is.

Researchers have also found tantalizing links between contextuality and problems that quantum computers can efficiently solve that ordinary computers cannot; investigating these links could help guide researchers in developing new quantum computing approaches and algorithms.

And with renewed theoretical interest comes a renewed experimental effort to prove that our world is indeed contextual. In February, Cabello, in collaboration with Kihwan Kim at Tsinghua University in Beijing, China, published a paper in which they claimed to have performed the first loophole-free experimental test of contextuality.

The Northern Irish physicist John Stewart Bell is widely credited with showing that quantum systems can be nonlocal. By comparing the outcomes of measurements of two entangled particles, he showed with his eponymous theorem of 1965 that the high degree of correlations between the particles cant possibly be explained in terms of local hidden variables defining each ones separate properties. The information contained in the entangled pair must be shared nonlocally between the particles.

Bell also proved a similar theorem about contextuality. He and, separately, Simon Kochen and Ernst Specker showed that it is impossible for a quantum system to have hidden variables that define the values of all their properties in all possible contexts.

In Kochen and Speckers version of the proof, they considered a single particle with a quantum property called spin, which has both a magnitude and a direction. Measuring the spins magnitude along any direction always results in one of two outcomes: 1 or 0. The researchers then asked: Is it possible that the particle secretly knows what the result of every possible measurement will be before it is measured? In other words, could they assign a fixed value a hidden variable to all outcomes of all possible measurements at once?

Quantum theory says that the magnitudes of the spins along three perpendicular directions must obey the 101 rule: The outcomes of two of the measurements must be 1 and the other must be 0. Kochen and Specker used this rule to arrive at a contradiction. First, they assumed that each particle had a fixed, intrinsic value for each direction of spin. They then conducted a hypothetical spin measurement along some unique direction, assigning either 0 or 1 to the outcome. They then repeatedly rotated the direction of their hypothetical measurement and measured again, each time either freely assigning a value to the outcome or deducing what the value must be in order to satisfy the 101 rule together with directions they had previously considered.

They continued until, in the 117th direction, the contradiction cropped up. While they had previously assigned a value of 0 to the spin along this direction, the 101 rule was now dictating that the spin must be 1. The outcome of a measurement could not possibly return both 0 and 1. So the physicists concluded that there is no way a particle can have fixed hidden variables that remain the same regardless of context.

While the proof indicated that quantum theory demands contextuality, there was no way to actually demonstrate this through 117 simultaneous measurements of a single particle. Physicists have since devised more practical, experimentally implementable versions of the original Bell-Kochen-Specker theorem involving multiple entangled particles, where a particular measurement on one particle defines a context for the others.

In 2009, contextuality, a seemingly esoteric aspect of the underlying fabric of reality, got a direct application: One of the simplified versions of the original Bell-Kochen-Specker theorem was shown to be equivalent to a basic quantum computation.

The proof, named Mermins star after its originator, David Mermin, considered various combinations of contextual measurements that could be made on three entangled quantum bits, or qubits. The logic of how earlier measurements shape the outcomes of later measurements has become the basis for an approach called measurement-based quantum computing. The discovery suggested that contextuality might be key to why quantum computers can solve certain problems faster than classical computers an advantage that researchers have struggled mightily to understand.

Robert Raussendorf, a physicist at the University of British Columbia and a pioneer of measurement-based quantum computing, showed that contextuality is necessary for a quantum computer to beat a classical computer at some tasks, but he doesnt think its the whole story. Whether contextuality powers quantum computers is probably not exactly the right question to ask, he said. But we need to get there question by question. So we ask a question that we understand how to ask; we get an answer. We ask the next question.

Some researchers have suggested loopholes around Bell, Kochen and Speckers conclusion that the world is contextual. They argue that context-independent hidden variables havent been conclusively ruled out.

In February, Cabello and Kim announced that they had closed every plausible loophole by performing a loophole free Bell-Kochen-Specker experiment.

The experiment entailed measuring the spins of two entangled trapped ions in various directions, where the choice of measurement on one ion defined the context for the other ion. The physicists showed that, although making a measurement on one ion does not physically affect the other, it changes the context and hence the outcome of the second ions measurement.

Skeptics would ask: How can you be certain that the context created by the first measurement is what changed the second measurement outcome, rather than other conditions that might vary from experiment to experiment? Cabello and Kim closed this sharpness loophole by performing thousands of sets of measurements and showing that the outcomes dont change if the context doesnt. After ruling out this and other loopholes, they concluded that the only reasonable explanation for their results is contextuality.

Cabello and others think that these experiments could be used in the future to test the level of contextuality and hence, the power of quantum computing devices.

If you want to really understand how the world is working, said Cabello, you really need to go into the detail of quantum contextuality.

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The Spooky Quantum Phenomenon You've Never Heard Of - Quanta Magazine

An enigma machine on display outside the Alan Turing Institute entrance inside the British Library, London.

Credit: Shutterstock/William Barton

Suppose someone asked you to devise the most powerful computer possible. Alan Turing, whose reputation as a central figure in computer science and artificial intelligence has only grown since his untimely death in 1954, applied his genius to problems such as this one in an age before computers as we know them existed. His theoretical work on this problem and others remains a foundation of computing, AI and modern cryptographic standards, including those NIST recommends.

The road from devising the most powerful computer possible to cryptographic standards has a few twists and turns, as does Turings brief life.

Alan Turing

Credit: National Portrait Gallery, London

In Turings time, mathematicians debated whether it was possible to build a single, all-purpose machine that could solve all problems that are computable. For example, we can compute a cars most energy-efficient route to a destination, and (in principle) the most likely way in which a string of amino acids will fold into a three-dimensional protein. Another example of a computable problem, important to modern encryption, is whether or not bigger numbers can be expressed as the product of two smaller numbers. For example, 6 can be expressed as the product of 2 and 3, but 7 cannot be factored into smaller integers and is therefore a prime number.

Some prominent mathematicians proposed elaborate designs for universal computers that would operate by following very complicated mathematical rules. It seemed overwhelmingly difficult to build such machines. It took the genius of Turing to show that a very simple machine could in fact compute all that is computable.

His hypothetical device is now known as a Turing machine. The centerpiece of the machine is a strip of tape, divided into individual boxes. Each box contains a symbol (such as A,C,T, G for the letters of genetic code) or a blank space. The strip of tape is analogous to todays hard drives that store bits of data. Initially, the string of symbols on the tape corresponds to the input, containing the data for the problem to be solved. The string also serves as the memory of the computer. The Turing machine writes onto the tape data that it needs to access later in the computation.

Credit: NIST

The device reads an individual symbol on the tape and follows instructions on whether to change the symbol or leave it alone before moving to another symbol. The instructions depend on the current state of the machine. For example, if the machine needs to decide whether the tape contains the text string TC it can scan the tape in the forward direction while switching among the states previous letter was T and previous letter was not C. If while in state previous letter was T it reads a C, it goes to a state found it and halts. If it encounters the blank symbol at the end of the input, it goes to the state did not find it and halts. Nowadays we would recognize the set of instructions as the machines program.

It took some time, but eventually it became clear to everyone that Turing was right: The Turing machine could indeed compute all that seemed computable. No number of additions or extensions to this machine could extend its computing capability.

To understand what can be computed it is helpful to identify what cannot be computed. Ina previous life as a university professor I had to teach programming a few times. Students often encounter the following problem: My program has been running for a long time; is it stuck? This is called the Halting Problem, and students often wondered why we simply couldnt detect infinite loops without actually getting stuck in them. It turns out a program to do this is an impossibility. Turing showed that there does not exist a machine that detects whether or not another machine halts. From this seminal result followed many other impossibility results. For example, logicians and philosophers had to abandon the dream of an automated way of detecting whether an assertion (such as whether there are infinitely many prime numbers) is true or false, as that is uncomputable. If you could do this, then you could solve the Halting Problem simply by asking whether the statement this machine halts is true or false.

Turing went on to make fundamental contributions to AI, theoretical biology and cryptography. His involvement with this last subject brought him honor and fame during World War II, when he played a very important role in adapting and extending cryptanalytic techniques invented by Polish mathematicians. This work broke the German Enigma machine encryption, making a significant contribution to the war effort.

Turing was gay. After the war, in 1952, the British government convicted him for having sex with a man. He stayed out of jail only by submitting to what is now called chemical castration. He died in 1954 at age 41 by cyanide poisoning, which was initially ruled a suicide but may have been an accident according to subsequent analysis. More than 50 years would pass before the British government apologized and pardoned him (after years of campaigning by scientists around the world). Today, the highest honor in computer sciences is called the Turing Award.

Turings computability work provided the foundation for modern complexity theory. This theory tries to answer the question Among those problems that can be solved by a computer, which ones can be solved efficiently? Here, efficiently means not in billions of years but in milliseconds, seconds, hours or days, depending on the computational problem.

For example, much of the cryptography that currently safeguards our data and communications relies on the belief that certain problems, such as decomposing an integer number into its prime factors, cannot be solved before the Sun turns into a red giant and consumes the Earth (currently forecast for 4 billion to 5 billion years). NIST is responsible for cryptographic standards that are used throughout the world. We could not do this work without complexity theory.

Technology sometimes throws us a curve, such as the discovery that if a sufficiently big and reliable quantum computer is built it would be able to factor integers, thus breaking some of our cryptography. In this situation, NIST scientists must rely on the worlds experts (many of them in-house) in order to update our standards. There are deep reasons to believe that quantum computers will not be able to break the cryptography that NIST is about to roll out. Among these reasons is that Turings machine can simulate quantum computers. This implies that complexity theory gives us limits on what a powerful quantum computer can do.

But that is a topic for another day. For now, we can celebrate how Turing provided the keys to much of todays computing technology and even gave us hints on how to solve looming technological problems.

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Alan Turing's Everlasting Contributions to Computing, AI and Cryptography - NIST