Archive for the ‘Quantum Computing’ Category

The Quantum Computing Arms Race is not Just About Breaking Encryption Keys – Nextgov

Countries designate technologies as strategic for a variety of reasons. Some technologies are regarded as an engine for economic growth, others as a way to reduce dependence on foreign suppliers, a defensive measure, a path to gain economic or national security advantages, or even serve as leverage during times of conflict. Weve seen this play out with satellites, cellular networks, atomic energy, chip manufacturing and more.

Quantum computing is a new strategic technology with wide-reaching implications. The ability to solve problems and perform calculations that no existing classical computer can, or ever will be able to, opens a plethora of strategic opportunities and challenges.

Much attention has been focused on decryption using quantum computers. The worlds financial systems and many computer networks are protected by an encryption scheme that was once considered unbreakable. And indeed, it would take classical computers many years to break it. But a powerful-enough quantum computer could crack the code in a few hours. Suddenly, bank accounts, health records, and other sensitive information could be left exposed, with untold consequential damages. Though quantum computers that can break the code might not be available for another 5 to 10 years, bad actors are already recording sensitive encrypted information so theyre ready to decrypt it in the future. Even when considering blockchain, public-to-public-key and reused public-to-public-key-hash addresses are vulnerable to quantum attacks, raising concerns about bitcoin and contracts that are secured by the blockchain.

Those same quantum computing technologies can also act as a strong defensive measure. Many organizations are using quantum technology, and specifically, quantum key distribution, to create encryption schemes that are much more difficult to break or gain access to.

But while companies should indeed consider the positive and negative impact of quantum computers on their encryption and communication systems, they should also be aware that they can gain strategic leverage from superior quantum computing technology.

Quantum can be a game-changing differentiator when working with huge data sets, models that have numerous variables yet exhibit a high rate of change over time. This can apply to moonshot projectscuring cancer, decoding the human genebut also to everyday problems such as optimizing shipping routes or balancing personal stock portfolios.

Take, for instance, energy storage. Quantum computers excel at simulating chemical and pharmaceutical compounds. This is because chemical interaction is done at the quantum physics level, andas Noble Laureate Richard Feynman noted 40 years agoa quantum system is the best choice to simulate quantum phenomena. Powerful quantum computers, and the software that drives them, can be used to develop superior batteries with higher efficiency, lower weight, and higher capacity. Since batteries represent about 30% of the cost of an electric vehicle and play a critical role in its usefulness, leadership in battery technology could translate to leadership in the electrification of vehicles, energy storage for buildings and more.

Machine learning is another example. Whether to improve conversational AI, solve protein-folding problems or analyze images and videos, countries that develop leading ML capabilities gain strategic advantages. Quantum computing offers dramatic new ML opportunities. They stem from the ability of a quantum computer to load much more information than classical ones, execute numerous calculations simultaneously and use these capabilities to uncover new and meaningful data patterns.

That unique quantum ability to perform numerous calculations in parallel, as opposed to sequentially, comes in handy for better weather forecasting, more accurate assessment of financial risk and the ability to streamline the supply chain, optimize traffic and improve the dynamic allocation of shared resources, such as cellular spectrums.

Many countries understand this. Indeed, we are seeing a global quantum arms race, bearing similarities to the space race of decades ago. China, for instance, is reportedly investing $10 billion in a national quantum program. The European Union has pledged significant amounts in addition to what member-states are pledging individually. The US committed $1.2 billion as part of the National Quantum Initiative, followed by another $1 billion in National Science Foundation funding for AI and quantum centers. Many additional countries including Russia, Japan, India, Germany and France have created their own national quantum programs.

Given the strategic and wide-ranging consequences of superior quantum computing capacity, it is fair to ask what constitutes technical superiority. We look at two key components: hardware and software. Quantum computing hardware is about exploring new ways to create high-quality quantum bits or qubitsand integrating them into machines with larger capacity and higher computational accuracy. But this hardware will be useless without software that allows researchers to quickly translate their algorithms into the low-level instructions that quantum computers need to operate. Yet this quantum circuit creation is done nearly manually today, very close to the hardware itself. But as computers become larger and more powerful, it will become impossible for humans to cope with the scale and complexity of quantum circuitsunless they harness new breakthroughs in software development platforms.

Conventional computing capabilities are limited: you have to break the data into 1s and 0s. Quantum changes that and thus opens many opportunities that can look at multiple variables simultaneously.

Attaining and retaining strategic advantages requires long-term planning and focused execution. Analysts say that the U.S. lost the 5G war to China. Can the US afford to lose the quantum race as well? What if China or another nation unveiled tomorrow morning a scientifically-credible demonstration of a computer that cracks financial encryption or accurately simulates a complex molecule? Overnight, the world will feel completely different.

Here are four ways countries can increase their chances of winning the race:

We are at a critical juncture. Lets not wait for the quantum equivalent of a Sputnik moment. Rarely does a new technology come along that provides those who can harness it with this level of power.

Now is the time to grab the quantum bull by the horns. Our children and grandchildren will thank us for it.

Adm. Mike Rogers is the former head of U.S. Cyber Command and the National Security Agency. Nir Minerbi is theCEO and co-founder at quantum software providerClassiq.

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The Quantum Computing Arms Race is not Just About Breaking Encryption Keys - Nextgov

IONQ: Wall Street Analysts Predict More Than 160% Upside in These Stocks – StockNews.com

Concerns over the 40-year high inflation and hawkish federal reserve have pushed the benchmark indices into the bear market territory. Bearish sentiment is still widespread, and the market is likely to continue to feel the pressure in the near term as well.

The overwhelming mentality remains gloomy, with most people just trying to avoid bear-market rallies, convinced the SPX has several hundred points of further downside over the coming months, wrote Adam Crisafulli of Vital Knowledge.

However, certain financially robust stocks possess solid upside potential and might perform well in the long run. And staying invested is important, or one can miss out on long-term returns.

Despite the market downturns, Wall Street analysts believe IonQ, Inc. (IONQ) and Rigetti Computing, Inc. (RGTI) could rally by more than 160% in the near term. Thus, these stocks could be worth adding to your watchlist.

IonQ, Inc. (IONQ)

IONQ engages in the development of general-purpose quantum computing systems. The company offers access to its quantum computers through cloud platforms, such as Amazon Web Services (AWS), Amazon Braket, Microsofts Azure Quantum, and Googles Cloud Marketplace.

On May 17, 2022, IONQ launched IonQ Forte, its latest generation of quantum systems. Forte features acousto-optic deflector (AOD) technology, which nullifies noise and overcomes variations in ion position, which is critical for scaling quantum computers. Given the growing quantum computing market, the company should benefit from this development.

Furthermore, on June 23, 2022, IONQ announced its partnership with GE Research to explore the benefits of quantum computing concerning risk management. This collaboration is expected to achieve record feats in quantum computing.

For the first quarter ended March 31, 2022, IONQs revenue came in at $1.95 million, up 1,462.4% year-over-year. Its net loss decreased 42.4% year-over-year to $4.23 million, while its loss per share came in at $0.02, down 66.7% year-over-year. Moreover, its cash and cash equivalents came in at $86.75 million, up 144.2% year-over-year.

IONQs revenue is expected to increase 406.9% year-over-year to $10.64 million in 2022. Its EPS is expected to grow 20% per annum for the next five years.

IONQ declined 26.1% over the past month, closing the last trading session at $4.38. However, Wall Street analysts expect the stock to hit $11.50 soon, indicating a potential upside of 162.6%.

Rigetti Computing, Inc. (RGTI)

RGTI operates as an integrated systems company. The company builds quantum computers and the superconducting quantum processors that power them. Its machines are integrated into various public, private, or hybrid clouds through its Quantum Cloud Services platform.

On June 21, 2022, Rigetti UK Limited, a wholly-owned subsidiary of RGTI, announced the launch of its 32-qubit Aspen-series quantum computer in the UK. Chad Rigetti, RGTIs founder and CEO, said, We believe deploying our first UK-based quantum computer is a major step towards our vision to integrate QPUs into the fabric of the cloud.

RGTIs net cash provided by financing activities came in at $213.44 million for the first quarter ended March 31, 2022, up 1,674.9% year-over-year. Its cash and restricted cash came in at $206.94 million, up 609.2% year-over-year.

RGTIs revenue is expected to grow 123.7% year-over-year to $12.40 million in 2022. Its EPS is estimated to grow 60.3% in 2022.

RGTI shares have slumped 45% over the past three months closing the last trading session at $3.67. However, Wall Street analysts expect the stock to hit $11.50 soon, indicating a potential upside of 213.4%.

IONQ shares were trading at $4.34 per share on Friday afternoon, down $0.04 (-0.91%). Year-to-date, IONQ has declined -74.01%, versus a -20.15% rise in the benchmark S&P 500 index during the same period.

Riddhima is a financial journalist with a passion for analyzing financial instruments. With a master's degree in economics, she helps investors make informed investment decisions through her insightful commentaries. More...

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IONQ: Wall Street Analysts Predict More Than 160% Upside in These Stocks - StockNews.com

Quantex establishes the first of its kind Quantum Resistant Exchange and a crypto wallet. – Digital Journal

Quantex is the first of its kind Quantum Resistant Exchange, Wallet & Blockchain using dual-layer post-quantum signatures and quantum-resistant algorithmic encryption technology. The platform launched Quantum-resistant exchange, wallet, and blockchain for all defi needs.

London, United Kingdom, 1st Jul 2022, King NewsWire, Quantex is a newly launched crypto platform in the industry. It functions as a licensed digital asset exchange and custodian, where holders can buy, sell, and store digital assets in a regulated, secure, and compliant manner.

In addition, Quantex is a quantum-resistant exchange, wallet, and blockchain for all the defi needs of crypto enthusiasts. It introduces a future-proof solution to hacking and vulnerabilities. The introduction of quantum computing constitutes a new paradigm shift for blockchain technology. It promises both problems and opportunities for the sector.

Quantex is a next-generation platform with security features against current cryptographic threats. It comes with a suite of solutions that provide safe digital asset custody. Quantex also allows for safe interactions with private and public post-quantum blockchains.

Visithttps://quantex.host/for further information.

Organization: Quantex

Contact Person: Media Relations

Email: Send Email

City: London

Country: United Kingdom

Website: https://quantex.host/

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Quantex establishes the first of its kind Quantum Resistant Exchange and a crypto wallet. - Digital Journal

A Huge Step Forward in Quantum Computing Was Just Announced: The First-Ever Quantum Circuit – ScienceAlert

Australian scientists have created the world's first-ever quantum computer circuit one that contains all the essential components found on a classical computer chip but at the quantum scale.

The landmark discovery, published in Nature today, was nine years in the making.

"This is the most exciting discovery of my career," senior author and quantum physicist Michelle Simmons, founder of Silicon Quantum Computing and director of the Center of Excellence for Quantum Computation and Communication Technology at UNSW told ScienceAlert.

Not only did Simmons and her team create what's essentially a functional quantum processor, they also successfully tested itby modeling a small molecule in which each atom has multiple quantum states something a traditional computer would struggle to achieve.

This suggests we're now a step closer to finally using quantum processing power to understand more about the world around us, even at the tiniest scale.

"In the 1950s, Richard Feynman said we're never going to understand how the world works how nature works unless we can actually start to make it at the same scale," Simmons told ScienceAlert.

"If we can start to understand materials at that level, we can design things that have never been made before.

"The question is: how do you actually control nature at that level?"

The latest invention follows the team's creation of the first ever quantum transistor in 2012.

(Atransistoris a small device that controls electronic signals and forms just one part of a computer circuit. An integrated circuit is more complex as it puts lots of transistors together.)

To make this leap in quantum computing, the researchers used a scanning tunneling microscope in an ultra-high vacuum to place quantum dots with sub-nanometer precision.

The placement of each quantum dot needed to be just right so the circuit could mimic how electrons hop along a string of single- and double-bonded carbons in a polyacetylene molecule.

The trickiest parts were figuring out: exactly how many atoms of phosphorus should be in each quantum dot; exactly how far apart each dot should be; and then engineering a machine that could place the tiny dots in exactly the right arrangement inside the silicon chip.

If the quantum dots are too big, the interaction between two dots becomes "too large to independently control them", the researchers say.

If the dots are too small, then it introduces randomness because each extra phosphorus atom can substantially change the amount of energy it takes to add another electron to the dot.

The final quantum chip contained 10 quantum dots, each made up of a small number of phosphorus atoms.

Double carbon bonds were simulated by putting less distance between the quantum dots than single carbon bonds.

Polyacetylene was chosen because it's a well-known model and could therefore be used to prove that the computer was correctly simulating the movement of electrons through the molecule.

Quantum computers are needed because classical computers cannot model large molecules; they are just too complex.

For example, to create a simulation of the penicillin molecule with 41 atoms, a classical computer would need 1086 transistors, which is "more transistors than there are atoms in the observable universe".

For a quantum computer, it would only require a processor with 286 qubits (quantum bits).

Because scientists currently have limited visibility as to how molecules function at the atomic scale, there's a lot of guess work in the creation of new materials.

"One of the holy grails has always been making a high temperature superconductor," says Simmons. "People just don't know the mechanism for how it works."

Another potential application for quantum computing is the study of artificial photosynthesis, and how light is converted to chemical energy through an organic chain of reactions.

Another big problem quantum computers could help solve is the creation of fertilizers. Triple nitrogen bonds are currently broken under high temperature and pressure conditions in the presence of an iron catalyst to create fixed nitrogen for fertilizer.

Finding a different catalyst that can make fertilizer more effectively could save a lot of money and energy.

Simmons says the achievement of moving from quantum transistor to circuit in just nine years is mimicking the roadmap set by the inventors of classical computers.

The first classical computer transistor was created in 1947. The first integrated circuit was built in 1958. Those two inventions were 11 years apart; Simmons' team made that leap two years ahead of schedule.

This article was published in Nature.

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A Huge Step Forward in Quantum Computing Was Just Announced: The First-Ever Quantum Circuit - ScienceAlert

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

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