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Quantum computing

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Modular, scalable hardware architecture for a quantum computer

A new quantum-system-on-chip enables the efficient control of a large array of qubits, moving toward practical quantum computing.

May 29, 2024

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Physicists arrange atoms in extremely close proximity

The technique opens possibilities for exploring exotic states of matter and building new quantum materials.

May 2, 2024

MIT scientists tune the entanglement structure in an array of qubits

The advance offers a way to characterize a fundamental resource needed for quantum computing.

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A blueprint for making quantum computers easier to program

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Quantum Computing Advance Begins New Era, IBM Says

A quantum computer came up with better answers to a physics problem than a conventional supercomputer.

A model of the interior of a quantum computer at the IBM Thomas J. Watson Research Center in Yorktown Heights, N.Y. Credit... James Estrin/The New York Times

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By Kenneth Chang

• Published June 14, 2023 Updated June 19, 2023

Quantum computers today are small in computational scope — the chip inside your smartphone contains billions of transistors while the most powerful quantum computer contains a few hundred of the quantum equivalent of a transistor. They are also unreliable. If you run the same calculation over and over, they will most likely churn out different answers each time.

But with their intrinsic ability to consider many possibilities at once, quantum computers do not have to be very large to tackle certain prickly problems of computation, and on Wednesday, IBM researchers announced that they had devised a method to manage the unreliability in a way that would lead to reliable, useful answers.

“What IBM showed here is really an amazingly important step in that direction of making progress towards serious quantum algorithmic design,” said Dorit Aharonov, a professor of computer science at the Hebrew University of Jerusalem who was not involved with the research.

While researchers at Google in 2019 claimed that they had achieved “quantum supremacy” — a task performed much more quickly on a quantum computer than a conventional one — IBM’s researchers say they have achieved something new and more useful, albeit more modestly named.

“We’re entering this phase of quantum computing that I call utility,” said Jay Gambetta, a vice president of IBM Quantum. “The era of utility.”

A team of IBM scientists who work for Dr. Gambetta described their results in a paper published on Wednesday in the journal Nature .

Present-day computers are called digital, or classical, because they deal with bits of information that are either 1 or 0, on or off. A quantum computer performs calculations on quantum bits, or qubits, that capture a more complex state of information. Just as a thought experiment by the physicist Erwin Schrödinger postulated that a cat could be in a quantum state that is both dead and alive, a qubit can be both 1 and 0 simultaneously.

That allows quantum computers to make many calculations in one pass, while digital ones have to perform each calculation separately. By speeding up computation, quantum computers could potentially solve big, complex problems in fields like chemistry and materials science that are out of reach today. Quantum computers could also have a darker side by threatening privacy through algorithms that break the protections used for passwords and encrypted communications.

When Google researchers made their supremacy claim in 2019, they said their quantum computer performed a calculation in 3 minutes 20 seconds that would take about 10,000 years on a state-of-the-art conventional supercomputer.

But some other researchers, including those at IBM, discounted the claim, saying the problem was contrived. “Google’s experiment, as impressive it was, and it was really impressive, is doing something which is not interesting for any applications,” said Dr. Aharonov, who also works as the chief scientific officer of Qedma, a quantum computing company.

The Google computation also turned out to be less impressive than it first appeared. A team of Chinese researchers was able to perform the same calculation on a non-quantum supercomputer in just over five minutes , far quicker than the 10,000 years the Google team had estimated.

The IBM researchers in the new study performed a different task, one that interests physicists. They used a quantum processor with 127 qubits to simulate the behavior of 127 atom-scale bar magnets — tiny enough to be governed by the spooky rules of quantum mechanics — in a magnetic field. That is a simple system known as the Ising model, which is often used to study magnetism.

This problem is too complex for a precise answer to be calculated even on the largest, fastest supercomputers.

On the quantum computer, the calculation took less than a thousandth of a second to complete. Each quantum calculation was unreliable — fluctuations of quantum noise inevitably intrude and induce errors — but each calculation was quick, so it could be performed repeatedly.

Indeed, for many of the calculations, additional noise was deliberately added, making the answers even more unreliable. But by varying the amount of noise, the researchers could tease out the specific characteristics of the noise and its effects at each step of the calculation.

“We can amplify the noise very precisely, and then we can rerun that same circuit,” said Abhinav Kandala, the manager of quantum capabilities and demonstrations at IBM Quantum and an author of the Nature paper. “And once we have results of these different noise levels, we can extrapolate back to what the result would have been in the absence of noise.”

In essence, the researchers were able to subtract the effects of noise from the unreliable quantum calculations, a process they call error mitigation.

“You have to bypass that by inventing very clever ways to mitigate the noise,” Dr. Aharonov said. “And this is what they do.”

Altogether, the computer performed the calculation 600,000 times, converging on an answer for the overall magnetization produced by the 127 bar magnets.

But how good was the answer?

For help, the IBM team turned to physicists at the University of California, Berkeley. Although an Ising model with 127 bar magnets is too big, with far too many possible configurations, to fit in a conventional computer, classical algorithms can produce approximate answers, a technique similar to how compression in JPEG images throws away less crucial data to reduce the size of the file while preserving most of the image’s details.

Michael Zaletel, a physics professor at Berkeley and an author of the Nature paper, said that when he started working with IBM, he thought his classical algorithms would do better than the quantum ones.

“It turned out a little bit differently than I expected,” Dr. Zaletel said.

Certain configurations of the Ising model can be solved exactly, and both the classical and quantum algorithms agreed on the simpler examples. For more complex but solvable instances, the quantum and classical algorithms produced different answers, and it was the quantum one that was correct.

Thus, for other cases where the quantum and classical calculations diverged and no exact solutions are known, “there is reason to believe that the quantum result is more accurate,” said Sajant Anand, a graduate student at Berkeley who did much of the work on the classical approximations.

It is not clear that quantum computing is indisputably the winner over classical techniques for the Ising model.

Mr. Anand is currently trying to add a version of error mitigation for the classical algorithm, and it is possible that could match or surpass the performance of the quantum calculations.

“It’s not obvious that they’ve achieved quantum supremacy here,” Dr. Zaletel said.

In the long run, quantum scientists expect that a different approach, error correction, will be able to detect and correct calculation mistakes, and that will open the door for quantum computers to speed ahead for many uses.

Error correction is already used in conventional computers and data transmission to fix garbles. But for quantum computers, error correction is likely years away, requiring better processors able to process many more qubits.

Error mitigation, the IBM scientists believe, is an interim solution that can be used now for increasingly complex problems beyond the Ising model.

“This is one of the simplest natural science problems that exists,” Dr. Gambetta said. “So it’s a good one to start with. But now the question is, how do you generalize it and go to more interesting natural science problems?”

Those might include figuring out the properties of exotic materials, accelerating drug discovery and modeling fusion reactions.

Kenneth Chang has been at The Times since 2000, writing about physics, geology, chemistry, and the planets. Before becoming a science writer, he was a graduate student whose research involved the control of chaos. More about Kenneth Chang

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What’s next for quantum computing

Companies are moving away from setting qubit records in favor of practical hardware and long-term goals.

• Michael Brooks archive page

This story is a part of MIT Technology Review’s  What’s Next series , where we look across industries, trends, and technologies to give you a first look at the future

In 2023, progress in quantum computing will be defined less by big hardware announcements than by researchers consolidating years of hard work, getting chips to talk to one another, and shifting away from trying to make do with noise as the field gets ever more international in scope.

For years, quantum computing’s news cycle was dominated by headlines about record-setting systems. Researchers at Google and IBM have had spats over who achieved what—and whether it was worth the effort. But the time for arguing over who’s got the biggest processor seems to have passed: firms are heads-down and preparing for life in the real world. Suddenly, everyone is behaving like grown-ups.

As if to emphasize how much researchers want to get off the hype train, IBM is expected to announce a processor in 2023 that bucks the trend of putting ever more quantum bits, or “qubits,” into play. Qubits, the processing units of quantum computers, can be built from a variety of technologies, including superconducting circuitry, trapped ions, and photons, the quantum particles of light. 

IBM has long pursued superconducting qubits, and over the years the company has been making steady progress in increasing the number it can pack on a chip. In 2021, for example, IBM unveiled one with a record-breaking 127 of them. In November, it debuted  its 433-qubit Osprey processor , and the company aims to release a 1,121-qubit processor called Condor in 2023. 

But this year IBM is also expected to debut its Heron processor, which will have just 133 qubits. It might look like a backwards step, but as the company is keen to point out, Heron’s qubits will be of the highest quality. And, crucially, each chip will be able to connect directly to other Heron processors, heralding a shift from single quantum computing chips toward “modular” quantum computers built from multiple processors connected together—a move that is expected to help quantum computers scale up significantly. 

Heron is a signal of larger shifts in the quantum computing industry. Thanks to some recent breakthroughs, aggressive roadmapping, and high levels of funding, we may see general-purpose quantum computers earlier than many would have anticipated just a few years ago, some experts suggest. “Overall, things are certainly progressing at a rapid pace,” says Michele Mosca, deputy director of the Institute for Quantum Computing at the University of Waterloo. 

Here are a few areas where experts expect to see progress.

Stringing quantum computers together

IBM’s Heron project is just a first step into the world of modular quantum computing. The chips will be connected with conventional electronics, so they will not be able to maintain the “quantumness” of information as it moves from processor to processor. But the hope is that such chips, ultimately linked together with quantum-friendly fiber-optic or microwave connections, will open the path toward distributed, large-scale quantum computers with as many as a million connected qubits. That may be how many are needed to run useful, error-corrected quantum algorithms. “We need technologies that scale both in size and in cost, so modularity is key,” says Jerry Chow, director at IBM Quantum Hardware System Development.

Other companies are beginning similar experiments. “Connecting stuff together is suddenly a big theme,” says Peter Shadbolt, chief scientific officer of PsiQuantum , which uses photons as its qubits. PsiQuantum is putting the finishing touches on a silicon-based modular chip. Shadbolt says the last piece it requires—an extremely fast, low-loss optical switch—will be fully demonstrated by the end of 2023. “That gives us a feature-complete chip,” he says. Then warehouse-scale construction can begin: “We’ll take all of the silicon chips that we’re making and assemble them together in what is going to be a building-scale, high-performance computer-like system.” 

The desire to shuttle qubits among processors means that a somewhat neglected quantum technology will come to the fore now, according to Jack Hidary , CEO of SandboxAQ, a quantum technology company that was spun out of Alphabet last year . Quantum communications, where coherent qubits are transferred over distances as large as hundreds of kilometers, will be an essential part of the quantum computing story in 2023, he says.

“The only pathway to scale quantum computing is to create modules of a few thousand qubits and start linking them to get coherent linkage,” Hidary told MIT Technology Review. “That could be in the same room, but it could also be across campus, or across cities. We know the power of distributed computing from the classical world, but for quantum, we have to have coherent links: either a fiber-optic network with quantum repeaters, or some fiber that goes to a ground station and a satellite network.”

Many of these communication components have been demonstrated in recent years. In 2017, for example, China’s Micius satellite showed that coherent quantum communications could be accomplished between nodes separated by 1,200 kilometers. And in March 2022, an international group of academic and industrial researchers demonstrated a quantum repeater that effectively relayed quantum information over 600 kilometers of fiber optics. 

Taking on the noise

At the same time that the industry is linking up qubits, it is also moving away from an idea that came into vogue in the last five years—that chips with just a few hundred qubits might be able to do useful computing, even though noise easily disrupts their operations. 

This notion, called “noisy intermediate-scale quantum” (NISQ), would have been a way to see some short-term benefits from quantum computing, potentially years before reaching the ideal of large-scale quantum computers with many hundreds of thousands of qubits devoted to correcting errors. But optimism about NISQ seems to be fading. “The hope was that these computers could be used well before you did any error correction, but the emphasis is shifting away from that,” says Joe Fitzsimons, CEO of Singapore-based Horizon Quantum Computing.

Some companies are taking aim at the classic form of error correction, using some qubits to correct errors in others. Last year, both Google Quantum AI and Quantinuum , a new company formed by Honeywell and Cambridge Quantum Computing, issued papers demonstrating that qubits can be assembled into error-correcting ensembles that outperform the underlying physical qubits.

Other teams are trying to see if they can find a way to make quantum computers “fault tolerant” without as much overhead. IBM, for example, has been exploring characterizing the error-inducing noise in its machines and then programming in a way to subtract it (similar to what noise-canceling headphones do). It’s far from a perfect system—the algorithm works from a prediction of the noise that is likely to occur, not what actually shows up. But it does a decent job, Chow says: “We can build an error-correcting code, with a much lower resource cost, that makes error correction approachable in the near term.”

Maryland-based IonQ , which is building trapped-ion quantum computers, is doing something similar. “The majority of our errors are imposed by us as we poke at the ions and run programs,” says Chris Monroe, chief scientist at IonQ. “That noise is knowable, and different types of mitigation have allowed us to really push our numbers."

Getting serious about software

For all the hardware progress, many researchers feel that more attention needs to be given to programming. “Our toolbox is definitely limited, compared to what we need to have 10 years down the road,” says Michal Stechly of Zapata Computing , a quantum software company based in Boston. 

The way code runs on a cloud-accessible quantum computer is generally “circuit-based,” which means the data is put through a specific, predefined series of quantum operations before a final quantum measurement is made, giving the output. That’s problematic for algorithm designers, Fitzsimons says. Conventional programming routines tend to involve looping some steps until a desired output is reached, and then moving into another subroutine. In circuit-based quantum computing, getting an output generally ends the computation: there is no option for going round again.

Horizon Quantum Computing is one of the companies that have been building programming tools to allow these flexible computation routines. “That gets you to a different regime in terms of the kinds of things you’re able to run, and we’ll start rolling out early access in the coming year,” Fitzsimons says.

Helsinki-based Algorithmiq is also innovating in the programming space. “We need nonstandard frameworks to program current quantum devices,” says CEO Sabrina Maniscalco. Algorithmiq’s newly launched drug discovery platform, Aurora, combines the results of a quantum computation with classical algorithms. Such “hybrid” quantum computing is a growing area, and it’s widely acknowledged as the way the field is likely to function in the long term. The company says it expects to achieve a useful quantum advantage—a demonstration that a quantum system can outperform a classical computer on real-world, relevant calculations—in 2023. 

Competition around the world

Change is likely coming on the policy front as well. Government representatives including Alan Estevez, US undersecretary of commerce for industry and security, have hinted that trade restrictions surrounding quantum technologies are coming. 

Tony Uttley, COO of Quantinuum, says that he is in active dialogue with the US government about making sure this doesn’t adversely affect what is still a young industry. “About 80% of our system is components or subsystems that we buy from outside the US,” he says. “Putting a control on them doesn’t help, and we don’t want to put ourselves at a disadvantage when competing with other companies in other countries around the world.”

And there are plenty of competitors. Last year, the Chinese search company Baidu opened access to a 10-superconducting-qubit processor that it hopes will help researchers make forays into applying quantum computing to fields such as materials design and pharmaceutical development. The company says it has recently completed the design of a 36-qubit superconducting quantum chip. “Baidu will continue to make breakthroughs in integrating quantum software and hardware and facilitate the industrialization of quantum computing,” a spokesman for the company told MIT Technology Review. The tech giant Alibaba also has researchers working on quantum computing with superconducting qubits.

In Japan, Fujitsu is working with the Riken research institute to offer companies access to the country’s first home-grown quantum computer in the fiscal year starting April 2023. It will have 64 superconducting qubits. “The initial focus will be on applications for materials development, drug discovery, and finance,” says Shintaro Sato, head of the quantum laboratory at Fujitsu Research.

Not everyone is following the well-trodden superconducting path, however. In 2020, the Indian government pledged to spend 80 billion rupees ($1.12 billion when the announcement was made) on quantum technologies. A good chunk will go to photonics technologies—for satellite-based quantum communications, and for innovative “qudit” photonics computing.

Qudits expand the data encoding scope of qubits—they offer three, four, or more dimensions, as opposed to just the traditional binary 0 and 1, without necessarily increasing the scope for errors to arise. “This is the kind of work that will allow us to create a niche, rather than competing with what has already been going on for several decades elsewhere,” says Urbasi Sinha, who heads the quantum information and computing laboratory at the Raman Research Institute in Bangalore, India.

Though things are getting serious and internationally competitive, quantum technology remains largely collaborative—for now. “The nice thing about this field is that competition is fierce, but we all recognize that it’s necessary,” Monroe says. “We don’t have a zero-sum-game mentality: there are different technologies out there, at different levels of maturity, and we all play together right now. At some point there’s going to be some kind of consolidation, but not yet.”

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The current state of quantum computing: Between hype and revolution

February 19, 2021 By Lorenzo Pautasso, Anika Pflanzer, and Henning Soller

In recent years there has been a rising interest in quantum computing, fueled by several breakthroughs on the technology side and a significant increase in investments. To gain a fuller view of the current quantum-computing landscape, we conducted quantitative research as well as a comprehensive survey of more than 300 technologists across industries. Using our findings from both research components, we assessed quantum computing through four lenses—general temperature, 1 industry, private equity, and use cases—to understand its evolution from hype to game changer.

General temperature

Our Quantum Computing Monitor research helped us assemble a dashboard overview of where industries are in their quantum-computing maturity across five categories: bystanders, beginners, learners, professionals, and legends (Exhibit 1).

The pharma industry is at the learner level of quantum maturity, meaning it has already begun to hire quantum scientists to support possible use cases for quantum simulation in drug design, for instance. There are also massive implications for quantum technology in the financial-services industry—especially in security.

Although our survey results show that the adoption rate of quantum computing for industry in general is average (2.9 out of 5), technology, media, and telecom companies have made several breakthroughs over the past five years. Advances include achieving quantum supremacy, developing an industrial quantum computer, and setting up cloud-based quantum-computing services. These breakthroughs mark clear milestones on the path to much higher quantum-computing maturity going forward.

Private equity

Quantum computing’s potential is also underpinned by the significant increase in private-equity (PE) investments over the past five years (Exhibit 2), which signals an interest in new technologies and a belief in the long-term viability of quantum computing. Although a large share of PE investment has been in bigger companies, such as D-Wave, there is a wide variety of companies of different sizes with an array of solution patterns. Swiss company ID Quantique, for example, is small but has become profitable.

Currently, a multitude of quantum-computing use cases that could significantly change how industries operate are being investigated worldwide. Quantum machine learning, quantum simulation, and quantum-inspired computation are generating a lot of interest and now have industrial applications.

Quantum simulation was first used to study the behavior of molecules directly—a capability that isn’t currently possible in the lab. An example includes trying to understand large molecules in silico instead of trying to see their specific behavior in situ. Quantum cryptography is already being implemented in industries such as banking—for example, in Swiss private banks—to protect sensitive data. The development of these use cases has accelerated with the advent of several different leading and emerging technologies. Improved cooling technologies, including cryogenics, for example, support the construction of quantum computers, devices that operate most effectively when their environment’s temperature is close to absolute zero.

It is true that, thus far, the ambitions for quantum computing exceed its realized impact. Based on our research, however, we see clear signs that larger changes will come soon, possibly fueled by individual technology developers as well as technology advancements that could help potential use cases become a reality. Quantum computing is moving from hype to reality.

Lorenzo Pautasso is a consultant in McKinsey’s Munich office, where Anika Pflanzer is a partner; Henning Soller is a partner in the Frankfurt office.

1 “Temperature” describes the current maturity of this technology in an industry on a scale from 1 (not mature) to 5 (disruptive for the industry).

What’s Next in Quantum is   quantum-centric supercomputing

A key factor in classical supercomputing is the intersection of communication and computation. The same holds true for quantum. Quantum-centric supercomputing utilizes a modular architecture to enable scaling. It combines quantum communication and computation to increase system capacity, and uses a hybrid cloud middleware to seamlessly integrate quantum and classical workflows. To realize this next wave in quantum we are building a new system called Quantum System Two. See our quantum development roadmap

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Qiskit: Open-Source Quantum Development

Qiskit is an open-source SDK for working with quantum computers at the level of pulses, circuits, and application modules.

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Quantum Computing

Quantum Computing merges two great scientific revolutions of the 20th century: computer science and quantum physics. Quantum physics is the theoretical basis of the transistor, the laser, and other technologies which enabled the computing revolution. But on the algorithmic level, today's computing machinery still operates on ""classical"" Boolean logic. Quantum Computing is the design of hardware and software that replaces Boolean logic by quantum law at the algorithmic level. For certain computations such as optimization, sampling, search or quantum simulation this promises dramatic speedups. We are particularly interested in applying quantum computing to artificial intelligence and machine learning. This is because many tasks in these areas rely on solving hard optimization problems or performing efficient sampling.

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Quantum computers in 2023: how they work, what they do, and where they’re heading

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In June, an IBM computing executive claimed quantum computers were entering the “utility” phase , in which high-tech experimental devices become useful. In September, Australia’s Chief Scientist Cathy Foley went so far as to declare “ the dawn of the quantum era ”.

This week, Australian physicist Michelle Simmons won the nation’s top science award for her work on developing silicon-based quantum computers.

Obviously, quantum computers are having a moment. But – to step back a little – what exactly are they?

What is a quantum computer?

One way to think about computers is in terms of the kinds of numbers they work with.

The digital computers we use every day rely on whole numbers (or integers ), representing information as strings of zeroes and ones which they rearrange according to complicated rules. There are also analogue computers, which represent information as continuously varying numbers (or real numbers ), manipulated via electrical circuits or spinning rotors or moving fluids.

Read more: There's a way to turn almost any object into a computer – and it could cause shockwaves in AI

In the 16th century, the Italian mathematician Girolamo Cardano invented another kind of number called complex numbers to solve seemingly impossible tasks such as finding the square root of a negative number. In the 20th century, with the advent of quantum physics, it turned out complex numbers also naturally describe the fine details of light and matter.

In the 1990s, physics and computer science collided when it was discovered that some problems could be solved much faster with algorithms that work directly with complex numbers as encoded in quantum physics.

The next logical step was to build devices that work with light and matter to do those calculations for us automatically. This was the birth of quantum computing.

Why does quantum computing matter?

We usually think of the things our computers do in terms that mean something to us — balance my spreadsheet, transmit my live video, find my ride to the airport. However, all of these are ultimately computational problems, phrased in mathematical language.

As quantum computing is still a nascent field, most of the problems we know quantum computers will solve are phrased in abstract mathematics. Some of these will have “real world” applications we can’t yet foresee, but others will find a more immediate impact.

One early application will be cryptography. Quantum computers will be able to crack today’s internet encryption algorithms, so we will need quantum-resistant cryptographic technology. Provably secure cryptography and a fully quantum internet would use quantum computing technology.

In materials science, quantum computers will be able to simulate molecular structures at the atomic scale, making it faster and easier to discover new and interesting materials. This may have significant applications in batteries, pharmaceuticals, fertilisers and other chemistry-based domains.

Quantum computers will also speed up many difficult optimisation problems, where we want to find the “best” way to do something. This will allow us to tackle larger-scale problems in areas such as logistics, finance, and weather forecasting.

Machine learning is another area where quantum computers may accelerate progress. This could happen indirectly, by speeding up subroutines in digital computers, or directly if quantum computers can be reimagined as learning machines.

What is the current landscape?

In 2023, quantum computing is moving out of the basement laboratories of university physics departments and into industrial research and development facilities. The move is backed by the chequebooks of multinational corporations and venture capitalists.

Contemporary quantum computing prototypes – built by IBM , Google , IonQ , Rigetti and others – are still some way from perfection.

Read more: Error correcting the things that go wrong at the quantum computing scale

Today’s machines are of modest size and susceptible to errors, in what has been called the “ noisy intermediate-scale quantum ” phase of development. The delicate nature of tiny quantum systems means they are prone to many sources of error, and correcting these errors is a major technical hurdle.

The holy grail is a large-scale quantum computer which can correct its own errors. A whole ecosystem of research factions and commercial enterprises are pursuing this goal via diverse technological approaches.

Superconductors, ions, silicon, photons

The current leading approach uses loops of electric current inside superconducting circuits to store and manipulate information. This is the technology adopted by Google , IBM , Rigetti and others.

Another method, the “trapped ion” technology, works with groups of electrically charged atomic particles, using the inherent stability of the particles to reduce errors. This approach has been spearheaded by IonQ and Honeywell .

A third route of exploration is to confine electrons within tiny particles of semiconductor material, which could then be melded into the well-established silicon technology of classical computing. Silicon Quantum Computing is pursuing this angle.

Yet another direction is to use individual particles of light (photons), which can be manipulated with high fidelity. A company called PsiQuantum is designing intricate “guided light” circuits to perform quantum computations.

There is no clear winner yet from among these technologies, and it may well be a hybrid approach that ultimately prevails.

Where will the quantum future take us?

Attempting to forecast the future of quantum computing today is akin to predicting flying cars and ending up with cameras in our phones instead. Nevertheless, there are a few milestones that many researchers would agree are likely to be reached in the next decade.

Better error correction is a big one. We expect to see a transition from the era of noisy devices to small devices that can sustain computation through active error correction.

Another is the advent of post-quantum cryptography. This means the establishment and adoption of cryptographic standards that can’t easily be broken by quantum computers.

Read more: Quantum computers threaten our whole cybersecurity infrastructure: here's how scientists can bulletproof it

Commercial spin-offs of technology such as quantum sensing are also on the horizon.

The demonstration of a genuine “quantum advantage” will also be a likely development. This means a compelling application where a quantum device is unarguably superior to the digital alternative.

And a stretch goal for the coming decade is the creation of a large-scale quantum computer free of errors (with active error correction).

When this has been achieved, we can be confident the 21st century will be the “quantum era”.

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Title: quantum computing: vision and challenges.

Abstract: The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex problems that cannot be solved with conventional computing methods. These problems include modeling quantum mechanics, logistics, chemical-based advances, drug design, statistical science, sustainable energy, banking, reliable communication, and quantum chemical engineering. The last few years have witnessed remarkable advancements in quantum software and algorithm creation and quantum hardware research, which has significantly advanced the prospect of realizing quantum computers. It would be helpful to have comprehensive literature research on this area to grasp the current status and find outstanding problems that require considerable attention from the research community working in the quantum computing industry. To better understand quantum computing, this paper examines the foundations and vision based on current research in this area. We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers. Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate.

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The Current State of Quantum Computing

The Basic Concepts of Quantum Computing

The basic elements driving modern quantum computing consist of components that often have parallels in traditional computing yet have been suited for the quantum mechanical realm.

A qubit is similar to a bit in traditional computing in that it’s the fundamental unit of a quantum computing system. However, it enables far more complex computations because a single qubit can consist of a superposition of logical states. For example, the quibit | ψ 〉= α |0〉 + β |1〉where α and β are complex numbers, and | α |2 + | β |2 = 1.

Therefore, a single qubit is capable of performing multiple calculations simultaneously.

Gates and Algorithms

Quantum gates manipulate coefficients of basis states. In this way, they perform the same general function as logic gates in traditional computing systems.

Quantum algorithms often follow a pattern in that they:

• Create a quantum state that encodes a data set or initial condition.
• Perform operations on the quantum state in order to amplify the answer to a problem while minimizing the states that aren’t of interest.
• Measure the quantum system to determine which states provide the most useful information.

Thus, quantum algorithms are a lot like traditional computing algorithms, at least when it comes to their logic.

Quantum Annealing

A quantum annealer is a computational model used to figure out the best solution when faced with a set of potential solutions. A company called D-Wave produces quantum annealers that users can access for business purposes.

Physical Implementations

Quantum computers are physical systems that behave like qubits. However, because quantum states are impacted by their environment, the quantum computing model only provides an approximation of what happens at the quantum level.

This is why John Preskill introduced the term noisy, intermediate-scale quantum (NISQ), which describes modern quantum computing systems. To reduce the “noisy” factor, there needs to be a scaling of qubits into the millions.

Superconducting Qubits

A superconducting qubit refers to an electronic circuit where energy levels assume quantum values. To develop superconducting qubits, researchers use 2D films made of superconducting material. It’s then cooled to cryogenic temperatures to avoid thermal energy from disturbing the qubit’s state. Electromagnetic coupling with microwave pulses is used to control the qubit.

Hardware Technology

The hardware technology needed for quantum computing is still in flux. Trapped ions and superconducting qubits are the most widely known, but there are other approaches on the horizon, such as neutral atoms, photonics, and silicon qubits.

Systems and Software

At this point, the systems and software needed for quantum computing are limited, but research is underway to develop widely usable solutions. Meeting this challenge involves:

• Building a quantum stack. One first has to build a quantum stack that can work with quantum computing principles, which, as mentioned above, are different from those that drive traditional systems.
• Overcome challenges . This includes the limited availability of qubits and combating long-range entanglement issues.
• Co-design hardware and software solutions. Co-designing software and hardware solutions ensures that applications take into account the limitations of the hardware they will be running on.
• Develop fault-tolerant quantum architecture. This involves accounting for the natural variations in a quantum system’s environment by isolating qubits from environmental factors while still enabling them to interact with each other.
• Integrating classical and quantum computing. For instance, classical pre- or post-processing is needed to make algorithms useful — including those developed through quantum computing.

Top Five Challenges and Opportunities in Quantum Computing

Despite the progress made in quantum computing, as mentioned above, there are some challenges that need to be overcome before quantum computing’s potential can be fully realized. There are also some opportunities that the computing, scientific, and business communities can take advantage of.

• The availability of qubits. There currently aren’t enough high-quality, error-corrected qubits. To overcome this, it’s important to leverage quantum memory management, which can involve matching qubits with tasks they’re well suited for.
• Limited connectivity makes long-range entanglement infeasible. Ideally, we could use entanglement to manipulate many qubits to perform a single operation. But currently, a lot of devices with limited connectivity are being built, which makes long-distance entanglement difficult.
• Limited support for circuit-level fault tolerance. While there has been progress in error correction for creating fault-tolerant qubits, it’s still a challenge to integrate them into universal computing systems.
• Verification and debugging. Due to the effect of measurement at the quantum level, it’s very difficult to verify and debug quantum computation, especially when it comes to larger systems.
• Quantum computing as-a-service. Cloud-based quantum computing services, which involve deploying quantum computers in the cloud, are making quantum computing much more accessible to the masses.

Community and Education

To fully introduce quantum computing on a large scale, engineers need to learn how to design systems for commercial consumption. This requires educational shifts at the high school and university level, as well as current engineers being willing to pivot to designing quantum computing solutions. There are some online resources, such as IBM’s Qiskit textbook and the IEEE Quantum Initiative, which can make learning about quantum computing easier.

To learn more about the current state of quantum computing, download the full paper, “Quantum Computing: Progress and Innovation.”

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What is quantum computing.

Frank Tavares

Harnessing the quantum realm for NASA’s future complex computing needs

NASA’s Ames Research Center in California’s Silicon Valley is the heart of the agency’s advanced computing efforts, including its exploration and research of quantum computing. Ames leverages its location in the heart of Silicon Valley to forge partnerships with private industry as well. Using these collaborations, the NASA Advanced Supercomputing facility’s resources , and expertise in quantum computing, Ames works to evaluate the potential of quantum computing for NASA missions.

The properties that govern physics at the extremely small scales and low temperatures of the quantum realm are puzzling and unique. Quantum computing is the practice of harnessing those properties to enable revolutionary algorithms that traditional computers wouldn’t be able to run. Algorithms are a set of instructions to solve a problem or accomplish a task in computing. Quantum algorithms require descriptions of what operations should do during computation on a quantum computer, which often takes the form of a software program called a “quantum circuit.” 

NASA’s computing needs are escalating as the agency aims for more complex missions across the solar system, as well as continued research in the Earth sciences and aeronautics. Quantum computing, as it matures in the coming years, could provide powerful solutions.

Quantum mechanics describes effects such as superposition, where a particle can be in many different states at once. Quantum entanglement allows particles to be correlated with each other in unique ways that can be utilized by quantum computing. Though why these properties and more occur is still a mystery of science, the way in which they function has been well characterized and researched, allowing quantum computing experts to design hardware and algorithms to use these properties to their advantage.

Since 1972, when Ames center director Hans Mark brought the first massively parallel computer – a kind of computer that uses multiple processors at the same time, or in parallel – the center has been at the forefront of developing advances in computing.

Today, the Quantum Artificial Intelligence Laboratory (QuAIL), is where NASA conducts research to determine the capabilities of quantum computers and their potential to support the agency’s goals in the decades to come. Located at Ames, the lab conducts research on quantum applications and algorithms, develops tools for quantum computing, and investigates the fundamental physics behind quantum computing. The lab also partners with other quantum labs across the country, such as those at Google; Oak Ridge National Laboratory, or ORNL; Rigetti; and is part of  two of the Department of Energy’s centers under the National Quantum Initiative, specifically the Co-design Center for Quantum Advantage and Superconducting Quantum Materials and Systems Center.

Applications and Algorithms

What future missions could quantum computing help realize?

Quantum computing is a field of study in its infancy. So far, it is too early to implement quantum computing into NASA missions. The role of QuAIL is to investigate quantum computing’s potential to serve the agency’s future needs, for missions yet to be proposed or even imagined.

The key to quantum computing is quantum algorithms – special algorithms uniquely constructed to take advantage of quantum properties, like quantum superposition and quantum entanglement. The properties of the quantum world allow for computations that would take billions of years on classical machines. By experimenting with designing quantum algorithms, QuAIL hopes to use quantum computers to tackle calculations that otherwise would be impossible.

Current research looks into applying quantum algorithms  to optimize the planning and scheduling of mission operations, machine learning for Earth science data, and simulations for the design of new materials for use in aeronautics and space exploration. In the future, quantum algorithms could impact NASA’s missions broadly. QuAIL’s role is to help define that future.

Quantum Computing Tools

How can software support quantum algorithms and their applications?

There are a variety of tools QuAIL is developing to support quantum computing. Those tools can help characterize “noise” in quantum devices, assist in error mitigation, compile algorithms for specific hardware, and simulate quantum algorithms.

Because quantum computers need extremely precise and stable conditions to operate, seemingly small issues such as impurities on a superconducting chip or accumulated charged particles can impact a computation. Thus, error mitigation will play a critical role in realizing mature quantum computers.

By modeling what kind of errors occur and the effect they have on calculations, a process called noise characterization, quantum researchers can design error mitigation techniques that can run alongside quantum algorithms to keep them on track.

All algorithms need to be compiled for use on specific hardware. Because quantum hardware is so distinct from traditional computers, researchers must make special efforts to compile quantum algorithms for quantum hardware. In the same way software needs to be coded to a particular operating system, quantum algorithms need to be coded to function on a quantum computer’s specific “operating system,” which also takes hardware into account.

Tools that allow researchers to simulate quantum circuits using non-quantum hardware are key to QuAIL’s objective to evaluate the potential of quantum hardware. By testing the same algorithm on both a traditional supercomputer using a quantum circuit simulator and on real quantum hardware, researchers can find the limits of the supercomputer.

NASA can also use these simulated quantum circuits to check the work of quantum hardware, ensuring that algorithms are being properly executed up until the limit at which the simulated quantum circuit is reached. This was an essential component of confirming that a recent milestone achieved by Google in collaboration with NASA and ORNL, demonstrating the ability to compute in seconds what would take even the largest and most advanced supercomputers days or weeks, had indeed been achieved.

Learn more:

• Google and NASA Achieve Quantum Supremacy

For researchers:

• “ From Ansätze to Z-gates: a NASA View of Quantum Computing ,” by Eleanor Rieffel et al., Advances in Parallel Computing, Vol 34, pp. 133 – 160, 2019.
• “ A NASA perspective on quantum computing: Opportunities and challenges ,” by Rupak Biswas et al.,  Parallel Computing 64, 81-98, 2017.
• QuAIL technical site

For news media:

Members of the news media interested in covering this topic should reach out to the  Ames newsroom .

• Google Quantum AI

What is quantum computing?

Quantum computers use quantum physics to access different computational abilities than classical computers. Google Quantum AI is exploring potential applications for cleaner fertilization, more efficient batteries, pharmaceutical discoveries, and more.

Quantum computers can perform tasks no classical computer can

Classical computers have enabled some of humanity's greatest achievements. But even as classical computers continue to improve, there are certain problems we do not expect them to ever be able to solve. Because quantum computers use quantum physics to access new computational abilities that are inaccessible to classical computers, we expect that they will be able to solve some problems exponentially faster than classical computers can.

Superposition is a quantum 'superpower'

In classical computing, information is stored in bits, which can take the value of either 0 or 1. Quantum computers use quantum bits (qubits) instead of bits. Those qubits take on richer states and extend beyond just 0 or 1 - they can be in a superposition, or a complex combination, of both 0 and 1. With superposition Superposition is a property where a qubit takes the value of a complex combination of both 0 and 1. , we can explore an exponentially large space of possible solutions to a problem.

Current quantum processors are noisy

Today's quantum processors are powerful enough to accomplish some contrived tasks more efficiently than the fastest classical supercomputers. But they still need to be improved to achieve their full potential. We call the current state of quantum computing the noisy intermediate-scale quantum (NISQ) While still at an early stage, NISQ era quantum computing holds the potential to explore some useful applications before fully error corrected quantum computers arrive. era. Because noise plays a large role in the performance of NISQ devices, it's challenging to compare them to classical computers, which essentially work without error. We've developed a framework based on a circuit’s "effective quantum volume" to evaluate the computational cost of quantum circuits that can be run on today's noisy devices.

A full-scale quantum computer requires error correction

Qubits are highly sensitive to their external environments, and even stray particles of light can introduce errors. For meaningful computation, these errors must be corrected, and error correction must be improved as quantum processors are scaled to larger numbers of qubits. We view the achievement of scalable error correction as a necessary step towards a truly useful quantum computer.

What can we do with quantum computers

We believe that quantum computers will be uniquely capable of solving some of the world's most pressing issues. Our current processors are being used to model phenomena that are inherently quantum mechanical. And Google is actively working with collaborators on a variety of potential long-term industrial chemistry applications such as the development of cleaner fertilization. Here are a few examples that we are most excited about.

Our focused approach to quantum computing

Our comprehensive development approach and commitment to error correction are key to realizing a useful quantum computer. By focusing on our roadmap to drive the development of a large-scale, error corrected quantum computer, we aim to grow our ability to address problems beyond the capabilities of the largest classical supercomputers.

Quantum computing

Quantum computing looms large on the horizon.

Analysts at Morgan Stanley predict that the market for high-end quantum computers will reach 10 billion dollars by 2025, double what it is now. Alongside IBM and Google, there is also Microsoft, the Chinese Internet giant Alibaba and startups such as Novarion, Rigetti and D-Wave. Yet the various manufacturers rely on different physical principles for the realization of the quantum hardware. Scientists distinguish between universal quantum computers, which can perform arbitrary quantum algorithms, and quantum annealers, which are less complex, but limited to very specific tasks. Researchers at VW have been using a D-Wave quantum annealer since 2017 to better simulate traffic flows. And BMW is investigating whether quantum annealers can help optimize its production robots’ performance.

Universal quantum computers are technically very challenging to build and operate. What sets these computers apart is that their performance doubles in power with each added qubit, thereby increasing in exponential rather than in linear fashion. In other words, two qubits yields four possible combinations, three qubits eight, and so on. The quantity of qubits matters, but evenly important is the quality of qubit entanglement and its coherence time. The latter determines how long the quantum system remains stable enough to compute before noise masks the information. Most universal quantum computers, such as Google’s 72-qubit Bristlecone, only work under special laboratory conditions.

In January 2019, IBM unveiled the IBM Q System One, the world’s first commercially viable quantum computer – meaning that it works outside a lab. A consortium of seven Fraunhofer Institutes in Germany has been tasked to look into real-world applications for quantum computing as of 2021 in a bid to drive the advance of applied quantum science in the EU. “We want to find out just what kind of applications there are for quantum computing in industry and how to write the necessary algorithms and translate them for specific applications,” explains Hauswirth. The initiative also aims to keep entry barriers low by sharing insights with companies to fast-track the industry’s efforts to build a knowledge base in quantum computing.

There are still high hurdles to clear on the path to upscale the performance of available quantum computers. The priority now is to find ways of shielding the fragile quanta from ambient influences that interfere with the computing process. For example, qubits have to be cooled to a temperature approaching absolute zero – around minus 273 degrees Celsius, which is colder than outer space. They also require a vacuum and have to be shielded against electromagnetic radiation. Vibrations and parasitic effects of electromagnetic waves used to manipulate the qubits and read out the information they carry can also cause problems.

Solving complex problems

What kind of real problems can quantum computers solve? “In a few years from now, quantum computers will provide highly efficient means for prime factorization. That will leave current cryptographic systems vulnerable, which is why major research into post-quantum cryptography is underway,” says Hauswirth. Quantum computers will be able to tackle even more complex problems a few years down the road: “Today’s fintech, for example, has trouble managing billions of cash flows in parallel and in real time within the confines of a very tight regulatory girdle. Sequential processing is still prone to errors, but quantum computers would help get around this.”

Prof. Anita Schöbel is director of the Fraunhofer Institute for Industrial Mathematics ITWM in Kaiserslautern. She and Hauswirth are mainly responsible for quantum computing at Fraunhofer. Pointing to an application in the works at her institute, she says, “We’re working on projects that use stochastic partial differential equations such as the Fokker-Planck equations. These serve to develop lithium ion batteries and wind turbines, calculate granular flows and determine prices in quantitative finance. These equations can be converted into quantum mechanics equations for quantum computers to crunch the numbers, probably much faster.”

Applied quantum computing is clearly taking shape in the real world. Will we all have a quantum home computer or a quantum processor in our smartphones in a few years? “Quantum computers will only ever be able to solve very specific problems, so they won’t replace conventional computers. It’s likely that cloud-based models will prevail – that is, quantum computing as a service (QCaaS). We’ll probably also see hybrids of quantum computing and conventional high-performance computing,” says Hauswirth.

When will the quantum computer arrive?

Three questions for Prof. Manfred Hauswirth, Fraunhofer FOKUS, on the quantum computer initiative with IBM

What is this project all about.

In partnership with IBM, we are going to install Europe’s first commercial quantum computer at a location in Germany. The aim is to develop applied quantum computing solutions for a range of fields and assess their viability. We would like to see companies of all sizes involved in this project.

Why does this matter?

It is early days yet for applied research in quantum computing. We need to define quantum algorithms and then convert them for easy use in applications programming. That requires expertise on the part of industry, so we want to fast-track efforts to build a knowledge base here in Germany. This initiative will also enable us to pursue quantum computing under full data sovereignty according to European law, without being dependent on large Internet corporations from overseas.

When do you expect to see the first results?

A quantum computer is to be installed in Germany in 2021. But even optimistic forecasts suggest it’s going to take another 10 to 20 years before businesses can use quantum computers.

From supercomputer to superinternet

Research teams around the world are working on the most efficient way to couple together multiple supercomputers using quantum information to create a quantum internet. At QuTech in Delft, a number of partners, among them the Fraunhofer Institute for Laser Technology ILT, are currently working on a highly ambitious project. By 2022, they hope to have built the world’s first quantum internet demonstrator in the Netherlands with the aim of achieving lasting entanglement of qubits over long distances. Nodes at four locations will be connected together via fibre-optic cable. This will enable greater computing capacity, as well as completely new applications, such as blind quantum computing, where computations are performed securely, privately and anonymously on quantum computers in the cloud. According to Florian Elsen, coordinator for quantum technology at Fraunhofer ILT in Aachen, the big challenge lies in “transmitting single, fragile qubits through a fibre-optic cable as losslessly as possible. To achieve this, we carry out frequency conversion, meaning that we modify the wavelength of single photons without changing other significant properties.” Once you have a quantum internet, it is not much of a leap to quantum communication.

How does a quantum computer work?

A conventional computer works with bits; a quantum computer with qubits. Like bits, qubits can have a value of 0 or 1. Unlike bits, they occupy a superposition of overlapping quantum states, so they can also have any combination of the two. A qubit does not take on a definite value until it is measured. Adding one qubit doubles the system’s performance so that 50 qubits, for example, would yield 2 to the power of 50 (2 50 ) possible combinations. This way, big problems and complex tasks are computed in parallel rather than in linear fashion.

David Di Vincenzo’s* five criteria for a quantum computer

1. A scalable physical system with well characterized qubit 2. The ability to initialize the state of the qubits to a simple fiducial state 3. A "universal" set of quantum gates 4. A qubit-specific measurement capability 5. Long relevant decoherence times  

*Pioneer of quantum information science and professor of theoretical physics at RWTH Aachen

Quantum computing projects

• Putting quantum bits into the fiber optic network: Launching the QFC-4-1QID project (Press Release Fraunhofer ILT Oct 15, 2019)  (ilt.fraunhofer.de)
• Quantum computing at Fraunhofer SCAI  (scai.fraunhofer.de)

Related Links:

• Quantum communication
• Quantum imaging
• Quantum sensors

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• 22 February 2023

Google’s quantum computer hits key milestone by reducing errors

• Davide Castelvecchi

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Physicists at Google have reached what they describe as their second milestone along the path to a useful quantum computer. At a laboratory in Santa Barbara, California, they have demonstrated that they can lower the error rate of calculations by making their quantum code bigger.

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doi: https://doi.org/10.1038/d41586-023-00536-w

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Research team demonstrates modular, scalable hardware architecture for a quantum computer

by Massachusetts Institute of Technology

Quantum computers hold the promise of being able to quickly solve extremely complex problems that might take the world's most powerful supercomputer decades to crack.

But achieving that performance involves building a system with millions of interconnected building blocks called qubits. Making and controlling so many qubits in a hardware architecture is an enormous challenge that scientists around the world are striving to meet.

Toward this goal, researchers at MIT and MITRE have demonstrated a scalable, modular hardware platform that integrates thousands of interconnected qubits onto a customized integrated circuit. This "quantum-system-on-chip" (QSoC) architecture enables the researchers to precisely tune and control a dense array of qubits. Multiple chips could be connected using optical networking to create a large-scale quantum communication network.

By tuning qubits across 11 frequency channels, this QSoC architecture allows for a new proposed protocol of "entanglement multiplexing" for large-scale quantum computing.

The team spent years perfecting an intricate process for manufacturing two-dimensional arrays of atom-sized qubit microchiplets and transferring thousands of them onto a carefully prepared complementary metal-oxide semiconductor (CMOS) chip. This transfer can be performed in a single step.

"We will need a large number of qubits, and great control over them, to really leverage the power of a quantum system and make it useful. We are proposing a brand new architecture and a fabrication technology that can support the scalability requirements of a hardware system for a quantum computer," says Linsen Li, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this architecture.

Li's co-authors include Ruonan Han, an associate professor in EECS, leader of the Terahertz Integrated Electronics Group, and member of the Research Laboratory of Electronics (RLE); senior author Dirk Englund, professor of EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE; as well as others at MIT, Cornell University, the Delft Institute of Technology, the Army Research Laboratory, and the MITRE Corporation. The paper appears in Nature .

Diamond microchiplets

While there are many types of qubits, the researchers chose to use diamond color centers because of their scalability advantages. They previously used such qubits to produce integrated quantum chips with photonic circuitry.

Qubits made from diamond color centers are "artificial atoms" that carry quantum information. Because diamond color centers are solid-state systems, the qubit manufacturing is compatible with modern semiconductor fabrication processes. They are also compact and have relatively long coherence times, which refers to the amount of time a qubit's state remains stable, due to the clean environment provided by the diamond material.

In addition, diamond color centers have photonic interfaces which allow them to be remotely entangled, or connected, with other qubits that aren't adjacent to them.

"The conventional assumption in the field is that the inhomogeneity of the diamond color center is a drawback compared to identical quantum memory like ions and neutral atoms. However, we turn this challenge into an advantage by embracing the diversity of the artificial atoms: Each atom has its own spectral frequency. This allows us to communicate with individual atoms by voltage tuning them into resonance with a laser, much like tuning the dial on a tiny radio," says Englund.

This is especially difficult because the researchers must achieve this at a large scale to compensate for the qubit inhomogeneity in a large system.

To communicate across qubits, they need to have multiple such "quantum radios" dialed into the same channel. Achieving this condition becomes near-certain when scaling to thousands of qubits.

To this end, the researchers surmounted that challenge by integrating a large array of diamond color center qubits onto a CMOS chip which provides the control dials. The chip can be incorporated with built-in digital logic that rapidly and automatically reconfigures the voltages, enabling the qubits to reach full connectivity.

"This compensates for the in-homogenous nature of the system. With the CMOS platform, we can quickly and dynamically tune all the qubit frequencies," Li explains.

Lock-and-release fabrication

To build this QSoC, the researchers developed a fabrication process to transfer diamond color center "microchiplets" onto a CMOS backplane at a large scale.

They started by fabricating an array of diamond color center microchiplets from a solid block of diamond. They also designed and fabricated nanoscale optical antennas that enable more efficient collection of the photons emitted by these color center qubits in free space.

Then, they designed and mapped out the chip from the semiconductor foundry. Working in the MIT.nano cleanroom, they post-processed a CMOS chip to add microscale sockets that match up with the diamond microchiplet array.

They built an in-house transfer setup in the lab and applied a lock-and-release process to integrate the two layers by locking the diamond microchiplets into the sockets on the CMOS chip. Since the diamond microchiplets are weakly bonded to the diamond surface, when they release the bulk diamond horizontally, the microchiplets stay in the sockets.

"Because we can control the fabrication of both the diamond and the CMOS chip, we can make a complementary pattern. In this way, we can transfer thousands of diamond chiplets into their corresponding sockets all at the same time," Li says.

The researchers demonstrated a 500-micron by 500-micron area transfer for an array with 1,024 diamond nanoantennas, but they could use larger diamond arrays and a larger CMOS chip to further scale up the system. In fact, they found that with more qubits, tuning the frequencies actually requires less voltage for this architecture.

"In this case, if you have more qubits, our architecture will work even better," Li says.

The team tested many nanostructures before they determined the ideal microchiplet array for the lock-and-release process. However, making quantum microchiplets is no easy task, and the process took years to perfect.

"We have iterated and developed the recipe to fabricate these diamond nanostructures in MIT cleanroom, but it is a very complicated process. It took 19 steps of nanofabrication to get the diamond quantum microchiplets, and the steps were not straightforward," he adds.

Alongside their QSoC, the researchers developed an approach to characterize the system and measure its performance on a large scale. To do this, they built a custom cryo-optical metrology setup.

Using this technique, they demonstrated an entire chip with over 4,000 qubits that could be tuned to the same frequency while maintaining their spin and optical properties. They also built a digital twin simulation that connects the experiment with digitized modeling, which helps them understand the root causes of the observed phenomenon and determine how to efficiently implement the architecture.

In the future, the researchers could boost the performance of their system by refining the materials they used to make qubits or developing more precise control processes. They could also apply this architecture to other solid-state quantum systems.

Journal information: Nature

Provided by Massachusetts Institute of Technology

This story is republished courtesy of MIT News ( web.mit.edu/newsoffice/ ), a popular site that covers news about MIT research, innovation and teaching.

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Microsoft doubles down on quantum computing and other science projects in latest reorg

• Microsoft on Monday cut hundreds Azure jobs to focus on AI investments, Business Insider reported .
• An internal memo said a focus to "define the AI wave" was behind the changes.
• The company is also increasing investments in quantum computing and other science projects.

Quantum computing is a gnarly technical challenge that tech giants have been hacking away at for more than a decade.

Microsoft isn't giving up, according to an internal memo detailing cuts in other parts of the company's operations.

Business Insider broke the news on Monday that Microsoft is cutting hundreds of jobs from its Azure cloud business. Executive Jason Zander blamed the cuts on Microsoft's need to purse AI investments, according to an internal memo obtained by BI.

Related stories

In that memo, Zander also discussed other areas where Microsoft is doubling down, although he still described this as part of the company's broader AI efforts.

"Looking ahead, we will increase investments in Quantum and Science, demonstrating the exciting possibilities of AI and cloud computing," Zander wrote in the memo.

He highlighted Microsoft breakthroughs, such as the discovery of a new battery electrolyte that he said uses 70% less lithium, as well as the first demonstration of "reliable logical qubits."

Traditional computers rely on bits representing ones and zeros to complete tasks. In contrast, a quantum bit, or "qubit," represents a one and a zero at the same time. Many qubits working together could in theory create a computer that performs some calculations exponentially faster.

"We have more innovations like this already under way and are seeing an acceleration of discovery with our customers as well," Zander also wrote in the memo. "Going forward we will accelerate the product roadmap and build up this next generation business, building on the foundation we established with Azure Quantum Elements and infrastructure components built in AFO."

AFO is Microsoft's Azure for Operators, a team from which Microsoft on Monday cut as many as 1,500 employees , according to an estimate from one of the people familiar with the cuts.

Are you a Microsoft employee or someone else with insight to share?

Contact Ashley Stewart via email ( [email protected] ), or send a secure message from a non-work device via Signal (+1-425-344-8242).

Watch: How tech layoffs could affect the economy

• Main content

Modular, scalable hardware architecture for a quantum computer

A new quantum-system-on-chip enables the efficient control of a large array of qubits, moving toward practical quantum computing..

Quantum computers hold the promise of being able to quickly solve extremely complex problems that might take the world's most powerful supercomputer decades to crack.

But achieving that performance involves building a system with millions of interconnected building blocks called qubits. Making and controlling so many qubits in a hardware architecture is an enormous challenge that scientists around the world are striving to meet.

Toward this goal, researchers at MIT and MITRE have demonstrated a scalable, modular hardware platform that integrates thousands of interconnected qubits onto a customized integrated circuit. This "quantum-system-on-chip" (QSoC) architecture enables the researchers to precisely tune and control a dense array of qubits. Multiple chips could be connected using optical networking to create a large-scale quantum communication network.

By tuning qubits across 11 frequency channels, this QSoC architecture allows for a new proposed protocol of "entanglement multiplexing" for large-scale quantum computing.

The team spent years perfecting an intricate process for manufacturing two-dimensional arrays of atom-sized qubit microchiplets and transferring thousands of them onto a carefully prepared complementary metal-oxide semiconductor (CMOS) chip. This transfer can be performed in a single step.

"We will need a large number of qubits, and great control over them, to really leverage the power of a quantum system and make it useful. We are proposing a brand new architecture and a fabrication technology that can support the scalability requirements of a hardware system for a quantum computer," says Linsen Li, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this architecture.

Li's co-authors include Ruonan Han, an associate professor in EECS, leader of the Terahertz Integrated Electronics Group, and member of the Research Laboratory of Electronics (RLE); senior author Dirk Englund, professor of EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE; as well as others at MIT, Cornell University, the Delft Institute of Technology, the Army Research Laboratory, and the MITRE Corporation. The paper appears in Nature .

Diamond microchiplets

While there are many types of qubits, the researchers chose to use diamond color centers because of their scalability advantages. They previously used such qubits to produce integrated quantum chips with photonic circuitry.

Qubits made from diamond color centers are "artificial atoms" that carry quantum information. Because diamond color centers are solid-state systems, the qubit manufacturing is compatible with modern semiconductor fabrication processes. They are also compact and have relatively long coherence times, which refers to the amount of time a qubit's state remains stable, due to the clean environment provided by the diamond material.

In addition, diamond color centers have photonic interfaces which allows them to be remotely entangled, or connected, with other qubits that aren't adjacent to them.

"The conventional assumption in the field is that the inhomogeneity of the diamond color center is a drawback compared to identical quantum memory like ions and neutral atoms. However, we turn this challenge into an advantage by embracing the diversity of the artificial atoms: Each atom has its own spectral frequency. This allows us to communicate with individual atoms by voltage tuning them into resonance with a laser, much like tuning the dial on a tiny radio," says Englund.

This is especially difficult because the researchers must achieve this at a large scale to compensate for the qubit inhomogeneity in a large system.

To communicate across qubits, they need to have multiple such "quantum radios" dialed into the same channel. Achieving this condition becomes near-certain when scaling to thousands of qubits. To this end, the researchers surmounted that challenge by integrating a large array of diamond color center qubits onto a CMOS chip which provides the control dials. The chip can be incorporated with built-in digital logic that rapidly and automatically reconfigures the voltages, enabling the qubits to reach full connectivity.

"This compensates for the in-homogenous nature of the system. With the CMOS platform, we can quickly and dynamically tune all the qubit frequencies," Li explains.

Lock-and-release fabrication

To build this QSoC, the researchers developed a fabrication process to transfer diamond color center "microchiplets" onto a CMOS backplane at a large scale.

They started by fabricating an array of diamond color center microchiplets from a solid block of diamond. They also designed and fabricated nanoscale optical antennas that enable more efficient collection of the photons emitted by these color center qubits in free space.

Then, they designed and mapped out the chip from the semiconductor foundry. Working in the MIT.nano cleanroom, they post-processed a CMOS chip to add microscale sockets that match up with the diamond microchiplet array.

They built an in-house transfer setup in the lab and applied a lock-and-release process to integrate the two layers by locking the diamond microchiplets into the sockets on the CMOS chip. Since the diamond microchiplets are weakly bonded to the diamond surface, when they release the bulk diamond horizontally, the microchiplets stay in the sockets.

"Because we can control the fabrication of both the diamond and the CMOS chip, we can make a complementary pattern. In this way, we can transfer thousands of diamond chiplets into their corresponding sockets all at the same time," Li says.

The researchers demonstrated a 500-micron by 500-micron area transfer for an array with 1,024 diamond nanoantennas, but they could use larger diamond arrays and a larger CMOS chip to further scale up the system. In fact, they found that with more qubits, tuning the frequencies actually requires less voltage for this architecture.

"In this case, if you have more qubits, our architecture will work even better," Li says.

The team tested many nanostructures before they determined the ideal microchiplet array for the lock-and-release process. However, making quantum microchiplets is no easy task, and the process took years to perfect.

"We have iterated and developed the recipe to fabricate these diamond nanostructures in MIT cleanroom, but it is a very complicated process. It took 19 steps of nanofabrication to get the diamond quantum microchiplets, and the steps were not straightforward," he adds.

Alongside their QSoC, the researchers developed an approach to characterize the system and measure its performance on a large scale. To do this, they built a custom cryo-optical metrology setup.

Using this technique, they demonstrated an entire chip with over 4,000 qubits that could be tuned to the same frequency while maintaining their spin and optical properties. They also built a digital twin simulation that connects the experiment with digitized modeling, which helps them understand the root causes of the observed phenomenon and determine how to efficiently implement the architecture.

In the future, the researchers could boost the performance of their system by refining the materials they used to make qubits or developing more precise control processes. They could also apply this architecture to other solid-state quantum systems.

• Quantum Computers
• Computers and Internet
• Spintronics Research
• Computer Science
• Artificial Intelligence
• Information Technology
• Mobile Computing
• Neural Interfaces
• Computer software
• Quantum computer
• Quantum entanglement
• Computer security
• Quantum tunnelling
• Quantum dot
• Introduction to quantum mechanics

Story Source:

Materials provided by Massachusetts Institute of Technology . Original written by Adam Zewe. Note: Content may be edited for style and length.

Journal Reference :

• Linsen Li, Lorenzo De Santis, Isaac B. W. Harris, Kevin C. Chen, Yihuai Gao, Ian Christen, Hyeongrak Choi, Matthew Trusheim, Yixuan Song, Carlos Errando-Herranz, Jiahui Du, Yong Hu, Genevieve Clark, Mohamed I. Ibrahim, Gerald Gilbert, Ruonan Han, Dirk Englund. Heterogeneous integration of spin–photon interfaces with a CMOS platform . Nature , 2024; DOI: 10.1038/s41586-024-07371-7

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