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When Christine Muschik was completing her PhD in the 2010s, the privilege of running an experiment on a quantum computer required the kind of money, fame, or insider connections few early-career researchers could muster.
“If you had an idea, it was very difficult to get it implemented on hardware. Hardware development was just crazy expensive,” says Muschik, who is now an associate faculty member at Perimeter Institute, cross-appointed with the University of Waterloo (CA).
As a theorist with great interest in moving from the whiteboard to real-world experiments, Muschik continually strained against technological and logistical restrictions. At every opportunity, she pushed to advance both the science and the technology.
“We want to have quantum simulations that work not only on paper but that also lead to proof-of-concept demonstrations. This is why we work with experimental teams: To make it real. To bring it to life in the lab,” she says.
A decade later, quantum hardware has advanced more quickly than anyone could have imagined. Quantum tech is still in its early, fragile, somewhat experimental stages, but it has become much more accessible, freeing Muschik’s curiosity and intellect.
“We’re all surprised by the rapid acceleration of hardware development,” she says. “It’s happening because industry – the Googles and the IBMs – are getting on board and pumping a lot of money into it. Everybody is hunting after the ‘quantum advantage.’ For our last publication, we just ran the program we needed on an IBM cloud-based quantum computer.”
A 2019 report in Nature estimated that private investors in North America and Europe had poured at least $450 million (USD) into quantum technology start-up companies in the preceding two years. (Similar information was not available for China, which has become a powerhouse of quantum technology.)
Many of these start-ups are racing with each other, and with established tech giants, to achieve “quantum supremacy,” an industry term for the milestone of a quantum computer solving a useful or interesting problem that is impossible for classical computers. The term is ambiguous (Google claimed they had achieved quantum supremacy in 2019, but critics disputed it) and also deceptive: Quantum supremacy does not mean that quantum computers take over from conventional computers. Each is suited for different types of computational challenges.
Muschik has been working to combine the best of both.
“She understands both the intricacies of complex theories and the subtleties of experimental implementation,” says Raymond Laflamme, the Mike and Ophelia Lazaridis John von Neumann Chair in Quantum Information at Waterloo’s Institute for Quantum Computing. “She is very hands on in both areas, which makes her stand out.”
Muschik has developed new computational approaches that squeeze more power out of existing quantum technologies and flow the results into a feedback loop with classical processors, creating increasingly capable hybrid systems. While this work will inevitably make waves in the commercial tech sector, she is more interested in using these tools to create new knowledge.
“The whole guiding theme of my group involves one question: What if quantum computers could help us to make new scientific discoveries?” she says. She’s interested in questions about matter and antimatter, the inner workings of neutron stars, and other mysteries that conventional computers haven’t been able to solve.
Hybrid computing systems offer new approaches to simulation and analysis that can move fields from cosmology to particle physics forward. But even before she begins exploring questions from other fields, Muschik’s core work in developing such systems already helps advance a central challenge that has confounded theoretical physicists for decades: reconciling quantum mechanics and classical physics.
Each of these powerful theoretical frameworks does a great job of describing the universe from a specific perspective: Quantum mechanics covers the subatomic world of protons and quarks. Classical physics describes the macroscopic world of people and planets. Each provides an accurate and precise description of the same physical reality, as seen from a different point of view.
But each is inconsistent and incompatible with the other.
Quantum-classical hybrid computers send information back and forth between these contradictory frameworks, using both to solve problems and run simulations with implications for the aerospace industry, drug discovery, financial services, and many areas of scientific research.
Muschik makes the technology sound easy.
“It’s all about how you formulate the problem,” she says. “You take a question like ‘Why is there more matter than antimatter?’ You reformulate your question in the form of an optimization problem. I teach my quantum core processor to analyze this problem and spit out numbers. And classical computers know how to deal with numbers.”
Muschik works on applications for existing “noisy intermediate-sized quantum computers,” but also plans projects that benefit – and benefit from – continuing technological developments.
“We play a dual role, not only simulating the physics now, but also focusing on method development for future quantum computers,” she says. “This is how you pave the way to scale it up for future generations.”
Muschik oversees the Quantum Simulations of Fundamental Interactions initiative, a joint venture of Perimeter Institute and the Institute for Quantum Computing at the University of Waterloo. Among other things, her lab is developing the technology to simulate forces and particles that extend beyond the Standard Model of particle physics. The rapid advance of quantum computers over the past decades has made it much more possible to simulate quantum fields and fundamental forces that the Standard Model can’t explain.
“Where our understanding fails is the most interesting part. It is a hint about where we can find new physics. The models beyond the Standard Model are freaking difficult. Standard methods cannot tackle them. My personal computer cannot tackle them. The biggest supercomputer cannot. Even future supercomputer centres that are only planned – even those will not be able to tackle these questions,” she says.
“And you can say, ‘Ok, we should give up.’ But this is a tremendous opportunity. Quantum computers right now are too small, but they have tremendous promise to answer these big, deep, open questions.”
Perimeter Institute for Theoretical Physics (CA) is a leading centre for scientific research, training and educational outreach in foundational theoretical physics. Founded in 1999 in Waterloo, Ontario, Canada, its mission is to advance our understanding of the universe at the most fundamental level, stimulating the breakthroughs that could transform our future. Perimeter also trains the next generation of physicists through innovative programs, and shares the excitement and wonder of science with students, teachers and the general public.
The Institute’s founding and major benefactor is Canadian entrepreneur and philanthropist Mike Lazaridis.
The original building, designed by Saucier + Perrotte, opened in 2004 and was awarded a Governor General’s Medal for Architecture in 2006. The Stephen Hawking Centre, designed by Teeple Architects, was opened in 2011 and was LEED Silver certified in 2015.
In addition to research, Perimeter also provides scientific training and educational outreach activities to the general public. This is done in part through Perimeter’s Educational Outreach team.
Research
Perimeter’s research encompasses nine fields:
Cosmology
Mathematical physics
Particle Physics
Quantum fields and strings
Quantum foundations
Quantum gravity
Quantum information
Quantum matter
Strong gravity
The future quantum computers should be capable of super-fast and reliable computation. Today, this is still a major challenge. Now, computer scientists led by ETH Zürich conduct an early exploration for reliable quantum machine learning.
Building on concepts such as quantum entanglement, quantum computers promise a wealth of machine learning applications. (Photo: Keystone/Science Photo Library)
Anyone who collects mushrooms knows that it is better to keep the poisonous and the non-poisonous ones apart. Not to mention what would happen if someone ate the poisonous ones. In such “classification problems”, which require us to distinguish certain objects from one another and to assign the objects we are looking for to certain classes by means of characteristics, computers can already provide useful support to humans.
Intelligent machine learning methods can recognise patterns or objects and automatically pick them out of data sets. For example, they could pick out those pictures from a photo database that show non-toxic mushrooms. Particularly with very large and complex data sets, machine learning can deliver valuable results that humans would not be able to find out, or only with much more time. However, for certain computational tasks, even the fastest computers available today reach their limits. This is where the great promise of quantum computers comes into play: that one day they will also perform super-fast calculations that classical computers cannot solve in a useful period of time.
The reason for this “quantum supremacy” lies in physics: quantum computers calculate and process information by exploiting certain states and interactions that occur within atoms or molecules or between elementary particles.
The fact that quantum states can superpose and entangle creates a basis that allows quantum computers the access to a fundamentally richer set of processing logic. For instance, unlike classical computers, quantum computers do not calculate with binary codes or bits, which process information only as 0 or 1, but with quantum bits or qubits, which correspond to the quantum states of particles. The crucial difference is that qubits can realise not only one state – 0 or 1 – per computational step, but also a state in which both superpose. These more general manners of information processing in turn allow for a drastic computational speed-up in certain problems.
A reliable quantum classification algorithm correctly classifies a toxic mushroom as “poisonous” while a noisy, perturbed one classifies it faultily as “edible”. (Image: npj Quantum Information / DS3Lab ETH Zürich.)
Translating classical wisdom into the quantum realm.
These speed advantages of quantum computing are also an opportunity for machine learning applications – after all, quantum computers could compute the huge amounts of data that machine learning methods need to improve the accuracy of their results much faster than classical computers.
However, to really exploit the potential of quantum computing, one has to adapt the classical machine learning methods to the peculiarities of quantum computers. For example, the algorithms, i.e. the mathematical calculation rules that describe how a classical computer solves a certain problem, must be formulated differently for quantum computers. Developing well-functioning “quantum algorithms” for machine learning is not entirely trivial, because there are still a few hurdles to overcome along the way.
On the one hand, this is due to the quantum hardware. At ETH Zürich, researchers currently have quantum computers that work with up to 17 qubits (see “ETH Zürich and PSI found Quantum Computing Hub” of 3 May 2021). However, if quantum computers are to realise their full potential one day, they might need thousands to hundreds of thousands of qubits.
Quantum noise and the inevitability of errors
One challenge that quantum computers face concerns their vulnerability to error. Today’s quantum computers operate with a very high level of “noise”, as errors or disturbances are known in technical jargon. For the American Physical Society (US), this noise is “the major obstacle to scaling up quantum computers”. No comprehensive solution exists for both correcting and mitigating errors. No way has yet been found to produce error-free quantum hardware, and quantum computers with 50 to 100 qubits are too small to implement correction software or algorithms.
To a certain extent, one has to live with the fact that errors in quantum computing are in principle unavoidable, because the quantum states on which the concrete computational steps are based can only be distinguished and quantified with probabilities. What can be achieved, on the other hand, are procedures that limit the extent of noise and perturbations to such an extent that the calculations nevertheless deliver reliable results. Computer scientists refer to a reliably functioning calculation method as “robust” and in this context also speak of the necessary “error tolerance”.
This is exactly what the research group led by Ce Zhang, ETH computer science professor and member of the ETH AI Center, has has recently explored, somehow “accidentally” during an endeavor to reason about the robustness of classical distributions for the purpose of building better machine learning systems and platforms. Together with Professor Nana Liu from Shanghai Jiao Tong University [海交通大学](CN) and with Professor Bo Li from the University of Illinois Urbana-Champaign(US), they have developed a new approach. This allows them to prove the robustness conditions of certain quantum-based machine learning models, for which the quantum computation is guaranteed to be reliable and the result to be correct. The researchers have published their approach, which is one of the first of its kind, in the scientific journal npj Quantum Information.
Protection against errors and hackers
“When we realised that quantum algorithms, like classical algorithms, are prone to errors and perturbations, we asked ourselves how we can estimate these sources of errors and perturbations for certain machine learning tasks, and how we can guarantee the robustness and reliability of the chosen method,” says Zhikuan Zhao, a postdoc in Ce Zhang’s group. “If we know this, we can trust the computational results, even if they are noisy.”
The researchers investigated this question using quantum classification algorithms as an example – after all, errors in classification tasks are tricky because they can affect the real world, for example if poisonous mushrooms were classified as non-toxic. Perhaps most importantly, using the theory of quantum hypothesis testing – inspired by other researchers’ recent work in applying hypothesis testing in the classical setting – which allows quantum states to be distinguished, the ETH researchers determined a threshold above which the assignments of the quantum classification algorithm are guaranteed to be correct and its predictions robust.
With their robustness method, the researchers can even verify whether the classification of an erroneous, noisy input yields the same result as a clean, noiseless input. From their findings, the researchers have also developed a protection scheme that can be used to specify the error tolerance of a computation, regardless of whether an error has a natural cause or is the result of manipulation from a hacking attack. Their robustness concept works for both hacking attacks and natural errors.
“The method can also be applied to a broader class of quantum algorithms,” says Maurice Weber, a doctoral student with Ce Zhang and the first author of the publication. Since the impact of error in quantum computing increases as the system size rises, he and Zhao are now conducting research on this problem. “We are optimistic that our robustness conditions will prove useful, for example, in conjunction with quantum algorithms designed to better understand the electronic structure of molecules.”
The university is an attractive destination for international students thanks to low tuition fees of 809 CHF per semester, PhD and graduate salaries that are amongst the world’s highest, and a world-class reputation in academia and industry. There are currently 22,200 students from over 120 countries, of which 4,180 are pursuing doctoral degrees. In the 2021 edition of the QS World University Rankings ETH Zürich is ranked 6th in the world and 8th by the Times Higher Education World Rankings 2020. In the 2020 QS World University Rankings by subject it is ranked 4th in the world for engineering and technology (2nd in Europe) and 1st for earth & marine science.
As of November 2019, 21 Nobel laureates, 2 Fields Medalists, 2 Pritzker Prize winners, and 1 Turing Award winner have been affiliated with the Institute, including Albert Einstein. Other notable alumni include John von Neumann and Santiago Calatrava. It is a founding member of the IDEA League and the International Alliance of Research Universities (IARU) and a member of the CESAER network.
ETH Zürich was founded on 7 February 1854 by the Swiss Confederation and began giving its first lectures on 16 October 1855 as a polytechnic institute (eidgenössische polytechnische Schule) at various sites throughout the city of Zurich. It was initially composed of six faculties: architecture, civil engineering, mechanical engineering, chemistry, forestry, and an integrated department for the fields of mathematics, natural sciences, literature, and social and political sciences.
It is locally still known as Polytechnikum, or simply as Poly, derived from the original name eidgenössische polytechnische Schule, which translates to “federal polytechnic school”.
ETH Zürich is a federal institute (i.e., under direct administration by the Swiss government), whereas the University of Zürich [Universität Zürich ] (CH) is a cantonal institution. The decision for a new federal university was heavily disputed at the time; the liberals pressed for a “federal university”, while the conservative forces wanted all universities to remain under cantonal control, worried that the liberals would gain more political power than they already had. In the beginning, both universities were co-located in the buildings of the University of Zürich.
From 1905 to 1908, under the presidency of Jérôme Franel, the course program of ETH Zürich was restructured to that of a real university and ETH Zürich was granted the right to award doctorates. In 1909 the first doctorates were awarded. In 1911, it was given its current name, Eidgenössische Technische Hochschule. In 1924, another reorganization structured the university in 12 departments. However, it now has 16 departments.
ETH Zürich is ranked among the top universities in the world. Typically, popular rankings place the institution as the best university in continental Europe and ETH Zürich is consistently ranked among the top 1-5 universities in Europe, and among the top 3-10 best universities of the world.
Historically, ETH Zürich has achieved its reputation particularly in the fields of chemistry, mathematics and physics. There are 32 Nobel laureates who are associated with ETH Zürich, the most recent of whom is Richard F. Heck, awarded the Nobel Prize in chemistry in 2010. Albert Einstein is perhaps its most famous alumnus.
In 2018, the QS World University Rankings placed ETH Zürich at 7th overall in the world. In 2015, ETH Zürich was ranked 5th in the world in Engineering, Science and Technology, just behind the Massachusetts Institute of Technology(US), Stanford University(US) and University of Cambridge(UK). In 2015, ETH Zürich also ranked 6th in the world in Natural Sciences, and in 2016 ranked 1st in the world for Earth & Marine Sciences for the second consecutive year.
In a comparison of Swiss universities by swissUP Ranking and in rankings published by CHE comparing the universities of German-speaking countries, ETH Zürich traditionally is ranked first in natural sciences, computer science and engineering sciences.
In the survey CHE ExcellenceRanking on the quality of Western European graduate school programs in the fields of biology, chemistry, physics and mathematics, ETH Zürich was assessed as one of the three institutions to have excellent programs in all the considered fields, the other two being Imperial College London(UK) and the University of Cambridge(UK), respectively.
01 March 2020 [Missed this very important article. Making amends here.]
Edwin Cartlidge
Despite much recent fanfare, quantum computers still need to show that they can do something useful.
Google 54-qubit Sycamore superconducting processor quantum computer.
Judging by the cover of Nature that day, 24 October 2019 marked a turning point in the decades-long effort to harness the strange laws of quantum mechanics in the service of computing.
The words “quantum supremacy,” emblazoned in large capital letters on the front of the prestigious journal, announced to the world that a quantum computer had, for the first time, performed a computation impossible to carry out on a classical supercomputer in any reasonable amount of time—despite having vastly less in the way of processors, memory and software to draw on.
The quantum computer in question, Sycamore, comprised a mere 53 superconducting quantum bits, or qubits. It was built by a group of scientists at Google led by physicist John Martinis, who used it to execute an algorithm that generated a semi-random series of numbers. Those researchers then worked out how long they would have needed to simulate that operation on the IBM-built Summit supercomputer at Oak Ridge National Laboratory in Tennessee, USA, the processors of which include tens of trillions of transistors and which has 250,000 terabytes of storage.
ORNL IBM AC922 SUMMIT supercomputer, was No.1 on the TOP500. Credit: Carlos Jones, Oak Ridge National Laboratory/U.S. Dept. of Energy.
The IBM-built Summit supercomputer at the Oak Ridge National Laboratory, USA, contains tens of trillions of transistors and can carry out about 200,000 trillion operations a second. Credit:ORNL.
Amazingly, Martinis and colleagues concluded that what Sycamore could do in a little over three minutes, Summit would take 10,000 years to simulate.
Google CEO Sundar Pichai next to the company’s quantum computer. Credit: Google.
Long-sought milestone?
For many scientists, Sycamore’s result represents a major milestone on the road to a real-world, general-purpose quantum computer. Having invested millions of dollars in the field over the course of more than 30 years, governments and, increasingly, industry have bet that the exponential speed-up in processing power offered by quantum states in theory can be realized practically.
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Google’s Sycamore processor. Credit: Erik Lucero, Google.
Sycamore—A quantum chip bearing fruit
Google’s Sycamore processor consists of a 1-cm^2 piece of aluminum containing a 2D array of 53 qubits—each acting as a tiny superconducting resonator that encodes the values 0 and 1 in its two lowest energy levels, and coupled to its four nearest neighbors. Cooled to below 20 mK to minimize thermal interference, the qubits are subject to “gate” operations—having their coupling turned on and off, as well as absorbing microwaves and experiencing variations in magnetic flux.
The Google team executed a series of cycles, each involving a random selection of one-qubit gates and a specific two-qubit gate. After completing the last cycle, they then read out the value of each qubit to yield a 53-bit-long string of 0s and 1s. That sequence appears random, but quantum entanglement and interference dictate that some of the 253 permutations are much more likely to occur than others. Repeating the process a million times builds up a statistically significant number of bit strings that can be compared with the theoretical distribution calculated using a classical computer.
Measuring Sycamore’s “fidelity” to the theoretical distribution over 14 cycles, Martinis and coworkers found that the figure, 0.8%, agreed with calculations based on the fidelities of individual gates—and used that fact to estimate that after 20 cycles, the fidelity would have been about 0.1% (as the fidelity is gradually eroded by gate errors). At this level of complexity and fidelity, the team calculated, the classical Summit supercomputer would require a whopping 10,000 years to simulate the quantum wave function—whereas Sycamore needed a mere 200 seconds to take its 1 million samples.
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Winning that bet, however, depends on being able to protect a quantum computer’s delicate superposition states from even the smallest amounts of noise, such as tiny temperature fluctuations or minuscule electric fields. The Google result shows that noise can be controlled sufficiently to enable the execution of a classically difficult algorithm, according to Greg Kuperberg, a mathematician at the University of California, Davis, USA. “This advance is a major blow against arguments that quantum computers are impossible,” he says. “It is a tremendous confidence builder for the future.”
Not everyone, however, is convinced by the research. A number of experts, including several at IBM, believe that the Google group has seriously underestimated the capacity of traditional digital computers to simulate the kind of algorithms that could be run on Sycamore. More fundamentally, it still remains to be seen whether scientists can develop a quantum algorithm that is resilient to noise and that does something people are willing to pay for—given how little practical utility the current algorithm is likely to have.
“For me, the biggest value in the Google research is the technical achievement,” says Lieven Vandersypen, who works on rival quantum-dot qubits at the Delft University of Technology in the Netherlands. He points out that the previous best superconducting computer featured just 20 quite poorly controlled qubits. “But what we in the field are after is a computer that can solve useful problems, and we are still far from that.”
Quantum’s power
Quantum computers offer the possibility of carrying out certain tasks far more quickly than is possible with classical devices, owing to a number of bizarre properties of the quantum world. Whereas a classical computer processes data sequentially, a quantum computer should operate as a massively parallel processor. It does so thanks to the fact that each qubit—encoded in quantum particles such as atoms, electrons or photons—can exist in a superposition of the “0” and “1” states, rather than simply one or the other, and because the qubits are linked together through entanglement.
For N qubits, each of the 2^N possible states that can be represented has an associated amplitude. The idea is to carry out a series of operations on the qubits, specified by a quantum algorithm, such that the system’s wave function evolves in a predetermined way, causing the amplitudes to change at each step. When the computer’s output is then obtained by measuring the value of each qubit, the wave function collapses to yield the result.
The Google experiment, carried out in company labs in Santa Barbara, CA, USA, was designed to execute an algorithm whose answer could only be found classically by simulating the system’s wave function. So while running the algorithm on a quantum computer would only take as long as is needed to execute its limited number of steps, simulating that algorithm classically would involve tracking the 2^N probability amplitudes. Even with just 53 qubits that is an enormous number—9×10^15, or 9,000 trillion.
Sycamore is not the first processor to have harnessed quantum interference to perform a calculation considered very difficult, if not impossible, to do using a classical computer. In 2017, two groups in the U.S. each used about 50 interacting, individually controllable qubits to simulate collections of quantum spins. Christopher Monroe and colleagues at the University of Maryland, College Park, manipulated electrically trapped ions using laser pulses, while OSA Fellow Mikhail Lukin of Harvard University and coworkers used a laser to excite neutral atoms. Both groups used their devices to determine the critical point at which a magnetic-phase transition occurs.
However, these systems were designed to carry out very specific tasks, somewhat akin to early classical analog computers. Google’s processor, in contrast, is a programmable digital machine. By employing a handful of different logic gates—specific operations applied either to one or two qubits—it in principle can execute many types of quantum algorithms.
Martinis and colleagues showed that they could use these gates to reliably generate a sample of numbers from the semi-random algorithm. Crucially, they found that they could prevent errors in the gates from building up and generating garbage at the output—leading them to declare that they had achieved quantum supremacy.
“We are thrilled,” says Martinis, who is also a professor at the University of California, Santa Barbara. “We have been trying to do this for quite a few years and have been talking about it, but of course there is a bit of pressure on you to make good on your claims.”
Classical shortcuts
When the Google team published its results—a preliminary version of which had been accidently posted online at NASA a month earlier—rivals lost little time in criticizing them. In particular, researchers at IBM, which itself works on superconducting qubits, posted a paper on the arXiv server arguing that Summit could in fact simulate Sycamore’s operations in just 2.5 days (and at higher fidelity). Google’s oversight, they said, was to not have considered how much more efficiently the supercomputer could track the system’s wave function if it fully exploited all of its hard disk space.
Kuperberg argues that Sycamore’s performance still merits the label “supremacy” given the disparity in resources available to the two computers. (In fact, the IBM researchers didn’t actually carry out the simulation, possibly because it would have been too expensive.) Kuperberg adds that with just a dozen or so more qubits, the simulation time would climb from days to centuries. “If this is what passes as refutation, then this is still a quantum David versus a classical Goliath,” he says. “This is supremacy enough as far as I am concerned.”
Indeed, in their paper Martinis and colleagues write that while they expect classical simulation techniques to improve, they also expect that “they will be consistently outpaced by hardware improvements on larger quantum processors.” Others, however, suggest that quantum computers might struggle to deliver any meaningful speed-up over classical devices. In particular, argue critics, it remains to be seen just how “quantum mechanical” future quantum computers will be—and therefore how easy it might be to imitate them.
To make classical simulation more competitive, the IBM researchers, as well as counterparts at the Chinese tech company Alibaba, are looking to make better use of supercomputer hardware. But Graeme Smith, a theoretical physicist at the University of Colorado and the JILA research institute in Boulder, USA, thinks that more radical improvement might be possible. He argues that the noise in Google’s gates, low as it is, could still swamp much of the system’s quantum information after multiple cycles. As such, he reckons it may be possible to develop a classical algorithm that sidesteps the need to calculate the 53-qubit wave function. “There is nothing to suggest that you have to do that to sample from [Google’s] circuit,” he says.
Indeed, Itay Hen, a numerical physicist at the University of Southern California in Los Angeles, USA, is trying to devise a classical algorithm that directly samples from the distribution output by Google’s circuit. Although too early to know whether the scheme will work, he says it would involve calculating easy bits of the wave function and interfering them to generate a succession of individual data strings very quickly. “I am guessing that lots of other people are doing a similar thing,” he adds.
As Hen explains, Martinis and colleagues had to make a compromise when designing their quantum-supremacy experiment—making the circuit complex enough to be classically hard, but not so complex that its output ended up being pure noise. And he says that the same compromise faces all developers of what is hoped will become the first generation of useful quantum computers—a technology known as “noisy intermediate-scale quantum,” or NISQ.
Such devices might consist of several hundred qubits, perhaps allowing them to simulate molecules and other small quantum systems. This is how Richard Feynman, back in the early 1980s, originally envisaged quantum computers being used—conceivably allowing scientists to design new materials or develop new drugs. But as their name suggests, these devices, too, would be limited by noise. The question, says Hen, is whether they can be built with enough qubits and processor cycles to do something that a classical computer can’t.
Dots, ions and photons
To try and meet the challenge, physicists are working on a number of competing technologies—superconducting circuits, qubits encoded in nuclear or electronic spins, trapped atoms or ions—each of which has its strengths and weaknesses (see OPN, October 2016, Quantum Computing: How Close Are We?). Vandersypen, for instance, is hopeful that spin qubits made from quantum dots—essentially artificial atoms—can be scaled up. He points out that such qubits have been fabricated in an industrial clean room at the U.S. chip giant Intel, which has teamed up with him and his colleagues at the Delft University of Technology to develop the technology. “We have done measurements [on the qubits],” he adds, “but not yet gotten to the point of qubit manipulation.”
Collaborating scientists from Intel and QuTech at the Delft University of Technology with Intel’s 17-qubit superconducting test chip. [Courtesy of Intel Corp.]
Trapped-ion qubits, meanwhile, are relatively slow, but have higher fidelities and can operate more cycles than their superconducting rivals. Monroe is confident that by linking up multiple ion traps, perhaps optically, it should be possible to make NISQ devices with hundreds of qubits. Indeed, he cofounded the company IonQ with OSA Fellow Jungsang Kim from Duke University, USA, to commercialize the technology.
A completely different approach is to encode quantum information in light rather than matter. Photonic qubits are naturally resistant to certain types of noise, but being harder to manipulate they may ultimately be more suited to communication and sensing rather than computing (see “A look at optics,”).
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Xanadu’s quantum chip. Credit: Xanadu Quantum Technologies Inc.
A look at optics
As qubits, photons have several virtues. Because they usually don’t interact with one another they are immune to stray electromagnetic fields, while their high energies at visible wavelengths make them robust against thermal fluctuations—removing the need for refrigeration. But their isolation makes them tricky to manipulate and process.
Two startups are working to get around this problem—and raising tens of millions of dollars in the process. PsiQuantum in Palo Alto, CA, USA, aims to make a chip with around 1 million qubits. Because photons are bosons and tend to stick together, their paths combine after entering 50-50 beam splitters from opposite sides, effectively interacting. Xanadu in Toronto, Canada, instead relies on the uncertainty principle, generating beams of “squeezed light” that have lower uncertainty in one quantum property at the expense of greater uncertainty in another. In theory, interfering these beams and counting photons at the output might enable quantum computation.
Both Xanadu and PsiQuantum have major, if different, technical hurdles to overcome before their computers become reality, according to OSA Fellow Michael Raymer, an optical physicist at the University of Oregon, USA, and a driving force behind the U.S. National Quantum Initiative.
Raymer adds that photons might also interact not directly, but via matter intermediaries, potentially enabling quantum-logic operations between single photons. Or they might be used to link superconducting processors to slower but longer-lived trapped-ion qubits (acting as memory). Alternatively, photon–matter interactions could be exploited in the quantum repeaters needed to ensure entanglement between distant particles—potentially a boost for both communication and sensing.
“Whether or not optics will be used to create free-standing quantum computers,” says Raymer, “I will defer prediction on that.”
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Yet turning NISQ computers into practical devices will need more than just improvements in hardware, according to William Oliver, an electrical engineer and physicist at the Massachusetts Institute of Technology, USA. Also essential, he says, will be developing new algorithms that can exploit these devices for commercial ends—be those ends optimizing investment portfolios or simulating new materials. “The most important thing,” Oliver says, “is to find commercial applications that gain advantage from the qubits we have today.”
According to Hen, though, it remains to be seen whether any suitable algorithms can be found. For simulation of chemical systems, he says, it is not clear if even hundreds of qubits would be enough to reproduce the interactions of just 40 electrons—the current classical limit—given the inaccuracies introduced by noise. Indeed, Smith is pessimistic about NISQ computers being able to do anything useful. “There is a lot of hope,” he says, “but not a lot of good science to substantiate that hope.”
Erring on the side of caution
The only realistic aim, Hen argues—and one that all experts see as the ultimate goal of quantum computing—is to build large, fault-tolerant machines. These would rely on error correction, which involves spreading the value of a single “logical qubit” over multiple physical qubits to make computations robust against errors on any specific bit (since quantum information cannot simply be copied). But implementing error correction will require that the error rate on individual qubits and logic gates is low enough that adding the error-correcting qubits doesn’t introduce more noise into the system than it removes.
Vandersypen reckons that this milestone could be achieved in as little as a year or two. The real challenge, he argues, will be scaling up—given how many qubits are likely to be needed for full-scale fault-tolerant computers. Particularly challenging will be making a machine that can find the prime factors of huge numbers, an application put forward by mathematician Peter Shor in 1994 that could famously threaten internet encryption. Martinis himself estimates that a device capable of finding the prime factors of a 2000-bit number in a day would need about 20 million physical qubits, given a two-qubit error probability of about 0.1%.
Despite the huge challenges that lie ahead, Martinis is optimistic about future progress. He says that he and his colleagues at Google are aiming to get two-qubit error rates down to 0.1% by increasing the coherence time of their qubits—doubling their current value of 10–20 microseconds within six months, and then quadrupling it in two years. They then hope to build a computer with 1,000 logical qubits within 10 years—a device that he says wouldn’t be big enough to threaten internet security but could solve problems in quantum chemistry. “We are putting together a plan and a timeline and we are going to try to stick to that,” he says.
However, Oliver is skeptical that such an ambitious timeframe can be met, estimating that a full-scale fault-tolerant computer is likely to take “a couple of decades” to build. Indeed, he urges his fellow scientists not to overstate quantum computers’ near-term potential. Otherwise, he fears, the field could enter a “quantum winter” in which enthusiasm gives way to pessimism and the withdrawal of funding. “A better approach,” according to Oliver, “is to be realistic about the promise and the challenges of quantum computing so that progress remains steady.”
Optics and Photonics News (OPN) is The Optical Society’s monthly news magazine. It provides in-depth coverage of recent developments in the field of optics and offers busy professionals the tools they need to succeed in the optics industry, as well as informative pieces on a variety of topics such as science and society, education, technology and business. OPN strives to make the various facets of this diverse field accessible to researchers, engineers, businesspeople and students. Contributors include scientists and journalists who specialize in the field of optics. We welcome your submissions.
Google has reportedly demonstrated for the first time that a quantum computer is capable of performing a task beyond the reach of even the most powerful conventional supercomputer in any practical time frame—a milestone known in the world of computing as “quantum supremacy.”
The ominous-sounding term, which was coined by theoretical physicist John Preskill in 2012, evokes an image of Darth Vader–like machines lording it over other computers. And the news has already produced some outlandish headlines, such as one on the Infowars website that screamed, “Google’s ‘Quantum Supremacy’ to Render All Cryptography and Military Secrets Breakable.” Political figures have been caught up in the hysteria, too: Andrew Yang, a presidential candidate, tweeted that “Google achieving quantum computing is a huge deal. It means, among many other things, that no code is uncrackable.”
Nonsense. It doesn’t mean that at all. Google’s achievement is significant, but quantum computers haven’t suddenly turned into computing colossi that will leave conventional machines trailing in the dust. Nor will they be laying waste to conventional cryptography in the near future—though in the longer term, they could pose a threat we need to start preparing for now.
Here’s a guide to what Google appears to have achieved—and an antidote to the hype surrounding quantum supremacy.
What do we know about Google’s experiment?
We still haven’t had confirmation from Google about what it’s done. The information about the experiment comes from a paper titled “Quantum Supremacy Using a Programmable Superconducting Processor,” which was briefly posted on a NASA website before being taken down. Its existence was revealed in a report in the Financial Times—and a copy of the paper can be found here.
The experiment is a pretty arcane one, but it required a great deal of computational effort. Google’s team used a quantum processor code-named Sycamore to prove that the figures pumped out by a random number generator were indeed truly random. They then worked out how long it would take Summit, the world’s most powerful supercomputer, to do the same task.
ORNL IBM AC922 SUMMIT supercomputer, No.1 on the TOP500. Credit: Carlos Jones, Oak Ridge National Laboratory/U.S. Dept. of Energy
The difference was stunning: while the quantum machine polished it off in 200 seconds, the researchers estimated that the classical computer would need 10,000 years.
When the paper is formally published, other researchers may start poking holes in the methodology, but for now it appears that Google has scored a computing first by showing that a quantum machine can indeed outstrip even the most powerful of today’s supercomputers. “There’s less doubt now that quantum computers can be the future of high-performance computing,” says Nick Farina, the CEO of quantum hardware startup EeroQ.
Why are quantum computers so much faster than classical ones?
In a classical computer, bits that carry information represent either a 1 or a 0; but quantum bits, or qubits—which take the form of subatomic particles such as photons and electrons—can be in a kind of combination of 1 and 0 at the same time, a state known as “superposition.” Unlike bits, qubits can also influence one another through a phenomenon known as “entanglement,” which baffled even Einstein, who called it “spooky action at a distance.”
Thanks to these properties, which are described in more detail in our quantum computing explainer, adding just a few extra qubits to a system increases its processing power exponentially. Crucially, quantum machines can crunch through large amounts of data in parallel, which helps them outpace classical machines that process data sequentially. That’s the theory. In practice, researchers have been laboring for years to prove conclusively that a quantum computer can do something even the most capable conventional one can’t. Google’s effort has been led by John Martinis, who has done pioneering work in the use of superconducting circuits to generate qubits.
Doesn’t this speedup mean quantum machines can overtake other computers now?
No. Google picked a very narrow task. Quantum computers still have a long way to go before they can best classical ones at most things—and they may never get there. But researchers I’ve spoken to since the paper appeared online say Google’s experiment is still significant because for a long time there have been doubts that quantum machines would ever be able to outstrip classical computers at anything.
Until now, research groups have been able to reproduce the results of quantum machines with around 40 qubits on classical systems. Google’s Sycamore processor, which harnessed 53 qubits for the experiment, suggests that such emulation has reached its limits. “We’re entering an era where exploring what a quantum computer can do will now require a physical quantum computer … You won’t be able to credibly reproduce results anymore on a conventional emulator,” explains Simon Benjamin, a quantum researcher at the University of Oxford.
Isn’t Andrew Yang right that our cryptographic defenses can now be blown apart?
Again, no. That’s a wild exaggeration. The Google paper makes clear that while its team has been able to show quantum supremacy in a narrow sampling task, we’re still a long way from developing a quantum computer capable of implementing Shor’s algorithm, which was developed in the 1990s to help quantum machines factor massive numbers. Today’s most popular encryption methods can be broken only by factoring such numbers—a task that would take conventional machines many thousands of years.
But this quantum gap shouldn’t be cause for complacency, because things like financial and health records that are going to be kept for decades could eventually become vulnerable to hackers with a machine capable of running a code-busting algorithm like Shor’s. Researchers are already hard at work on novel encryption methods that will be able to withstand such attacks (see our explainer on post-quantum cryptography for more details).
Why aren’t quantum computers as supreme as “quantum supremacy” makes them sound?
The main reason is that they still make far more errors than classical ones. Qubits’ delicate quantum state lasts for mere fractions of a second and can easily be disrupted by even the slightest vibration or tiny change in temperature—phenomena known as “noise” in quantum-speak. This causes mistakes to creep into calculations. Qubits also have a Tinder-like tendency to want to couple with plenty of others. Such “crosstalk” between them can also produce errors.
Google’s paper suggests it has found a novel way to cut down on crosstalk, which could help pave the way for more reliable machines. But today’s quantum computers still resemble early supercomputers in the amount of hardware and complexity needed to make them work, and they can tackle only very esoteric tasks. We’re not yet even at a stage equivalent to the ENIAC, IBM’s first general-purpose computer, which was put to work in 1945.
So what’s the next quantum milestone to aim for?
Besting conventional computers at solving a real-world problem—a feat that some researchers refer to as “quantum advantage.” The hope is that quantum computers’ immense processing power will help uncover new pharmaceuticals and materials, enhance artificial-intelligence applications, and lead to advances in other fields such as financial services, where they could be applied to things like risk management.
If researchers can’t demonstrate a quantum advantage in at least one of these kinds of applications soon, the bubble of inflated expectations that’s blowing up around quantum computing could quickly burst.
When I asked Google’s Martinis about this in an interview for a story last year, he was clearly aware of the risk. “As soon as we get to quantum supremacy,” he told me, “we’re going to want to show that a quantum machine can do something really useful.” Now it’s time for his team and other researchers to step up to that pressing challenge.
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