In the past 20 years, hundreds of companies, including giants like Google, Microsoft, and IBM, have staked a claim in the rush to establish quantum computing. Investors have put in well over $5 billion so far. All this effort has just one purpose: creating the world\u2019s next big thing.<\/p>\n
Quantum computers use the counterintuitive rules that govern matter at the atomic and subatomic level to process information in ways that are impossible with conventional, or \u201cclassical,\u201d computers. Experts suspect that this technology will be able to make an impact in fields as disparate as drug discovery, cryptography, finance, and supply-chain logistics.<\/p>\n
The promise is certainly there, but so is the hype. In 2022, for instance, Haim Israel, managing director of research at Bank of America, declared that quantum computing will be \u201cbigger than fire and bigger than all the revolutions that humanity has seen.\u201d Even among scientists, a slew of claims and vicious counterclaims have made it a hard field to assess.<\/p>\n
Ultimately, though, assessing our progress in building useful quantum computers comes down to one central factor: whether we can handle the noise. The delicate nature of quantum systems makes them extremely vulnerable to the slightest disturbance, whether that\u2019s a stray photon created by heat, a random signal from the surrounding electronics, or a physical vibration. This noise wreaks havoc, generating errors or even stopping a quantum computation in its tracks. It doesn\u2019t matter how big your processor is, or what the killer applications might turn out to be: unless noise can be tamed, a quantum computer will never surpass what a classical computer can do.<\/p>\n
For many years, researchers thought they might just have to make do with noisy circuitry, at least in the near term\u2014and many hunted for applications that might do something useful with that limited capacity. The hunt hasn\u2019t gone particularly well, but that may not matter now. In the last couple of years, theoretical and experimental breakthroughs have enabled researchers to declare that the problem of noise might finally be on the ropes. A combination of hardware and software strategies is showing promise for suppressing, mitigating, and cleaning up quantum errors. It\u2019s not an especially elegant approach, but it does look as if it could work\u2014and sooner than anyone expected.<\/p>\n
\u201cI\u2019m seeing much more evidence being presented in defense of optimism,\u201d says Earl Campbell, vice president of quantum science at Riverlane, a quantum computing company based in Cambridge, UK.<\/strong><\/p>\n Even the hard-line skeptics are being won over. University of Helsinki professor Sabrina Maniscalco, for example, researches the impact of noise on computations. A decade ago, she says, she was writing quantum computing off. \u201cI thought there were really fundamental issues. I had no certainty that there would be a way out,\u201d she says. Now, though, she is working on using quantum systems to design improved versions of light-activated cancer drugs that are effective at lower concentrations and can be activated by a less harmful form of light. She thinks the project is just two and a half years from success. For Maniscalco, the era of \u201cquantum utility\u201d\u2014the point at which, for certain tasks, it makes sense to use a quantum rather than a classical processor\u2014is almost upon us. \u201cI\u2019m actually quite confident about the fact that we will be entering the quantum utility era very soon,\u201d she says.<\/p>\n Putting qubits in the cloud<\/strong> To some people, these problems seemed insurmountable. But others, like Jay Gambetta, were unfazed.<\/p>\n A quiet-spoken Australian, Gambetta has a PhD in physics from Griffith University, on Australia\u2019s Gold Coast. He chose to go there in part because it allowed him to feed his surfing addiction. But in July 2004, he wrenched himself away and skipped off to the Northern Hemisphere to do research at Yale University on the quantum properties of light. Three years later (by which time he was an ex-surfer thanks to the chilly waters around New Haven), Gambetta moved even further north, to the University of Waterloo in Ontario, Canada. Then he learned that IBM wanted to get a little more hands-on with quantum computing. In 2011, Gambetta became one of the company\u2019s new hires.<\/p>\n IBM\u2019s quantum engineers had been busy building quantum versions of the classical computer\u2019s binary digit, or bit. In classical computers, the bit is an electronic switch, with two states to represent 0 and 1. In quantum computers, things are less black and white. If isolated from noise, a quantum bit, or \u201cqubit,\u201d can exist in a probabilistic combination of those two possible states, a bit like a coin in mid-toss. This property of qubits, along with their potential to be \u201centangled\u201d with other qubits, is the key to the revolutionary possibilities of quantum computing.<\/p>\n A year after joining the company, Gambetta spotted a problem with IBM\u2019s qubits: everyone could see that they were getting pretty good. Whenever he met up with his fellow physicists at conferences, they would ask him to test out their latest ideas on IBM\u2019s qubits. Within a couple of years, Gambetta had begun to balk at the volume of requests. \u201cI started thinking that this was insane\u2014why should we just run experiments for physicists?\u201d he recalls.<\/p>\n \u201cWe watched the first jobs come in. We could see them pinging on the quantum computer. When it didn\u2019t break, we started to relax.\u201d Jay Gambetta<\/p>\n It occurred to him that his life might be easier if he could find a way for physicists to operate IBM\u2019s qubits for themselves\u2014maybe via cloud computing. He mentioned it to his boss, and then he found himself with five minutes to pitch the idea to IBM\u2019s executives at a gathering in late 2014. The only question they asked was whether Gambetta was sure he could pull it off. \u201cI said yes,\u201d he says. \u201cI thought, how hard can it be?\u201d<\/p>\n Very hard, it turned out, because IBM\u2019s executives told Gambetta he had to get it done quickly. \u201cI wanted to spend two years doing it,\u201d he says. They gave him a year.<\/p>\n It was a daunting challenge: he barely knew what the cloud was back then. Fortunately, some of his colleagues did, and they were able to upgrade the team\u2019s remote access protocols\u2014useful for tweaking the machine in the evening or on the weekend\u2014to create a suite of interfaces that could be accessed from anywhere in the world. The world\u2019s first cloud-access quantum computer, built using five qubits, went live at midnight on May the 4th, 2016. The date, Star Wars Day, was chosen by nerds, for nerds. \u201cI don\u2019t think anyone in upper management was aware of that,\u201d Gambetta says, laughing.<\/p>\n Not that upper management\u2019s reaction to the launch date was uppermost in his mind. Of far more concern, he says, was whether a system reflecting years of behind-the-scenes development work would survive being hooked up to the real world. \u201cWe watched the first jobs come in. We could see them pinging on the quantum computer,\u201d he says. \u201cWhen it didn\u2019t break, we started to relax.\u201d<\/p>\n Cloud-based quantum computing was an instant hit. Seven thousand people signed up in the first week, and there were 22,000 registered users by the end of the month. Their ventures made it clear, however, that quantum computing had a big problem.<\/p>\n The field\u2019s eventual aim is to have hundreds of thousands, if not millions, of qubits working together. But when it became possible for researchers to test out quantum computers with just a few qubits working together, many theory-based assumptions about how much noise they would generate turned out to be seriously off.<\/p>\n Some noise was always in the cards. Because they operate at temperatures above absolute zero, where thermal radiation is always present, everyone expected some random knocks to the qubits. But there were nonrandom knocks too. Changing temperatures in the control electronics created noise. Applying pulses of energy to put the qubits in the right states created noise. And worst of all, it turned out that sending a control signal to one qubit created noise in other, nearby qubits. \u201cYou\u2019re manipulating a qubit and another one over there feels it,\u201d says Michael Biercuk, director of the Quantum Control Laboratory at the University of Sydney in Australia.<\/p>\n By the time quantum algorithms were running on a dozen or so qubits, the performance was consistently shocking. In a 2022 assessment, Biercuk and others calculated the probability that an algorithm would run successfully before noise destroyed the information held in the qubits and forced the computation off track. If an algorithm with a known correct answer was run 30,000 times, say, the correct answer might be returned only three times.<\/p>\n Though disappointing, it was also educational. \u201cPeople learned a lot about these machines by actually using them,\u201d Biercuk says. \u201cWe found a lot of stuff that more or less nobody knew about\u2014or they knew and had no idea what to do about it.\u201d<\/p>\n Fixing the errors<\/strong> Broadly speaking, solutions can be classed into three categories. The base layer is error suppression. This works through classical software and machine-learning algorithms, which continually analyze the behavior of the circuits and the qubits and then reconfigure the circuit design and the way instructions are given so that the information held in the qubits is better protected. This is one of the things that Biercuk\u2019s company, Q-CTRL, works on; suppression, the company says, can make quantum algorithms 1,000 times more likely to produce a correct answer.<\/p>\n The next layer, error mitigation, uses the fact that not all errors cause a computation to fail; many of them will just steer the computation off track. By looking at the errors that noise creates in a particular system running a particular algorithm, researchers can apply a kind of \u201canti-noise\u201d to the quantum circuit to reduce the chances of errors during the computation and in the output. This technique, something akin to the operation of noise-\u00adcanceling headphones, is not a perfect fix. It relies, for instance, on running the algorithm multiple times, which increases the cost of operation, and the algorithm only estimates the noise. Nonetheless, it does a decent job of reducing errors in the final output, Gambetta says.<\/p>\n Helsinki-based Algorithmiq, where Maniscalco is CEO, has its own way of cleaning up noise after the computation is done. \u201cIt basically eliminates the noise in post-\u00adprocessing, like cleaning up the mess from the quantum computer,\u201d Maniscalco says. So far, it seems to work at reasonably large scales.<\/p>\n On top of all that, there has been a growing roster of achievements in \u201cquantum error correction,\u201d or QEC. Instead of holding a qubit\u2019s worth of information in one qubit, QEC encodes it in the quantum states of a set of qubits. A noise-induced error in any one of those is not as catastrophic as it would be if the information were held by a single qubit: by monitoring each of the additional qubits, it\u2019s possible to detect any change and correct it before the information becomes unusable.<\/p>\n Implementing QEC has long been considered one of the essential steps on the path to large-scale, noise-tolerant quantum computing\u2014to machines that can achieve all the promise of the technology, such as the ability to crack popular encryption schemes. The trouble is, QEC uses a lot of overhead. The gold-standard error correction architecture, known as a surface code, requires at least 13 physical qubits to protect a single useful \u201clogical\u201d qubit. As you connect logical qubits together, that number balloons: a useful processor might require 1,000 physical qubits for every logical qubit.<\/p>\n There are now multiple reasons to be optimistic even about this, however. In July 2022, for instance, Google\u2019s researchers published a demonstration of a surface code in action where performance got better\u2014not worse\u2014when more qubits were connected together.<\/p>\n That so many noise-handling techniques are flourishing is a huge deal\u2014especially at a time when the notion that we might get something useful out of small-scale, noisy processors has turned out to be a bust.<\/p>\n There have also been promising demonstrations of theoretical alternatives to surface codes. In August 2023, an IBM team that included Gambetta showed an error correction technique that could control the errors in a 12-qubit memory circuit using an extra 276 qubits, a big improvement over the thousands of extra qubits required by surface codes.<\/p>\n In September, two other teams demonstrated similar improvements with a fault-tolerant circuit called a CCZ gate, using superconducting circuitry and ion-trap processors.<\/p>\n That so many noise-handling techniques are flourishing is a huge deal\u2014especially at a time when the notion that we might get something useful out of small-scale, noisy processors has turned out to be a bust.<\/p>\n Actual error correction is not yet happening on commercially available quantum processors (and is not generally implementable as a real-time process during computations). But Biercuk sees quantum computing as finally hitting its stride. \u201cI think we\u2019re well on the way now,\u201d he says. \u201cI don\u2019t see any fundamental issues at all.\u201d<\/p>\n And these innovations are happening alongside general improvements in hardware performance\u2014meaning that there are ever fewer baseline errors in the functioning qubits\u2014and an increase in the number of qubits on each processor, making bigger and more useful calculations possible. Biercuk says he is starting to see places where he might soon choose a quantum computer over the best-\u00adperforming classical machines. Neither a classical nor a quantum computer can fully solve large-scale tasks like finding the optimal routes for a nationwide fleet of delivery trucks. But, Biercuk points out, accessing and running the best classical supercomputers costs a great deal of money\u2014potentially more than accessing and running a quantum computer that might even give a slightly better solution.<\/p>\n \u201cLook at what high-performance computing centers are doing on a daily basis,\u201d says Kuan Tan, CTO and cofounder of the Finland-based quantum computer provider IQM. \u201cThey\u2019re running power-hungry scientific calculations that are reachable [by] quantum computers that will consume much less power.\u201d A quantum computer doesn\u2019t have to be a better computer than any other kind of machine to attract paying customers, Tan says. It just has to be comparable in performance and cheaper to run. He expects we\u2019ll achieve that quantum energy advantage in the next three to five years.<\/p>\n","protected":false},"excerpt":{"rendered":" In the past 20 years, hundreds of companies, including giants like Google, Microsoft, and IBM, have staked a claim in the rush to establish quantum computing. Investors have put in well over $5 billion so far. All this effort has just one purpose: creating the world\u2019s next big thing. Quantum computers use the counterintuitive rules […]<\/p>\n","protected":false},"author":3,"featured_media":5870,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[6,29],"tags":[50],"acf":[],"yoast_head":"\n
\nThis breakthrough moment comes after more than a decade of creeping disappointment. Throughout the late 2000s and the early 2010s, researchers building and running real-world quantum computers found them to be far more problematic than the theorists had hoped.<\/p>\n
\nOnce they had recovered from this noisy slap, researchers began to rally. And they have now come up with a set of solutions that can work together to bring the noise under control.<\/p>\n