\u201cIt\u2019s an exciting time because people are fielding quantum computers with hundreds and thousands of qubits,\u201d says Nobel Prize\u2013winning quantum physicist John Martinis, a professor emeritus at the University of California, Santa Barbara, and co-founder of quantum hardware company Qolab.<\/p>\n
In 2019 Google researchers, led by Martinis, reported that their 53-qubit processor, Sycamore, had become the first to achieve \u201cquantum advantage,\u201d performing a calculation in 200 seconds that they estimated would take the best classical supercomputers around 10,000 years to solve. That supercomputer number was later disputed\u2014IBM argued that its best classical computer could actually perform the task in just two and half days\u2014but even if it held up, the calculation was of only academic interest as a proof of principle. \u201cGoogle\u2019s 2019 demonstration of quantum [advantage] was an important milestone, but many people would say that it did not yet constitute a breakthrough on a problem of broad practical significance,\u201d notes quantum physicist Kihwan Kim, now at the Institute for Basic Science in South Korea.<\/p>\n
\u201cWe can now simulate things like superconductivity, artificial photosynthesis and small drug designs.\u201d \u2014Michelle Simmons, Silicon Quantum Computing<\/p>\n
Experts agree that to tackle useful problems that lie beyond the reach of even the best possible classical supercomputers, we need qubit numbers to jump significantly, potentially to a million or more. In addition, quantum physicists will need to engineer robust qubits that maintain their quantum properties for a longer time, and they\u2019ll have to find ways to fix errors introduced during calculations. In March a multi-institutional group of researchers reported that IBM\u2019s superconducting Heron processors could accurately predict the results of neutron-scattering experiments that measured the structure of a specific antiferromagnetic crystal to an unprecedented scale, using 50 qubits or fewer; the physicists noted, however, that classical computers could perform the same feat faster and more accurately.<\/p>\n
So what will quantum computers really be good for\u2014and when? Experts say we are still years away from quantum computers able to handle practical applications that classical computers cannot, which might include breaking common data-encryption schemes, simulating quantum processes for fundamental physics, and designing better drugs and materials. That said, Martinis\u2014an expert on scaling up quantum hardware\u2014notes there are no guarantees that million-qubit computers will ever be created. \u201cThe proof,\u201d he says, \u201cwill be building them and seeing that they work.\u201d<\/p>\n
Cryptography<\/p>\n
The most notorious promise of quantum computers has been that they will one day break RSA encryption, a long-standing protocol used worldwide to secure bank transfers, cryptocurrencies and digital communication. That day may come surprisingly soon. It was long thought that cracking encryption would require a processor with at least a million qubits. But in February a team from Iceberg Quantum in Sydney, Australia, dramatically reduced that estimate, calculating that with careful optimization and error correction, hackers might need fewer than 100,000 qubits for the feat. In March, Google announced a new commitment to migrating its systems by 2029 to protect them from quantum hacking.<\/p>\n
Although the Iceberg claims have yet to be peer-reviewed, they are credible and have caused a stir, says Artur Ekert, a cryptography expert at the University of Oxford. \u201cMany think that the threat to encryption from quantum computers is just mumbo jumbo\u2014and I have also been skeptical\u2014but it could just take a few more papers like this one to make the notion of breaking RSA relevant,\u201d he says. Martinis\u2019s opinion has also shifted in recent years. \u201cIf you are worried about RSA encryption\u2014as you should be\u2014I would say it might be broken in five to 10 years,\u201d he says.<\/p>\n
RSA encryption leverages the fact that it\u2019s easy to create a secret key by multiplying two large prime numbers together but effectively impossible for any classical computer to determine the key by efficiently factoring it back into those constituent primes. A classical computer could test successive numbers sequentially, remembering each value and looking for a pattern, but this approach is infeasible with large numbers, Ekert explains. Modern classical algorithms use other methods but remain inefficient because the execution time increases exponentially with the size of the number to be factored.<\/p>\n
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Jen Christiansen; Source: The Quantum Index Report 2025, by Jonathan Ruane et al.; M.I.T. Initiative on the Digital Economy, Massachusetts Institute of Technology, May 2025 (data)<\/p>\n
The quantum world, however, is not so constrained. Qubits can take on multiple values simultaneously and become entangled with one another, amplifying their power. \u201cEssentially it can cover all possible computational paths at the same time,\u201d Ekert says. In 1994 theoretical computer scientist Peter Shor, now at the Massachusetts Institute of Technology, proposed that a hypothetical quantum computer could use this property to crack RSA encryption. If and when one does, it will probably use the algorithm Shor developed.<\/p>\n
There are proposals for quantum-resistant cryptographic algorithms; the U.S. National Institute of Standards and Technology (NIST) published three such schemes in 2024. Zoller thinks such work largely defuses the threat because it suggests the world can move away from RSA encryption before quantum hackers arrive. \u201cShor\u2019s algorithm may ultimately be remembered as a landmark scientific achievement of great historical importance for inspiring the development of quantum computers, as much as for its implications for breaking encryption,\u201d he says.<\/p>\n
Ekert feels less reassured. Last year, he notes, a computer scientist in China proposed\u2014it turns out incorrectly\u2014a quantum algorithm capable of breaking NIST\u2019s top candidate, which is called lattice-based encryption. \u201cIt took the brainpower of the whole quantum cryptography community [more than a week] to find a mistake, showing you how close those things are,\u201d Ekert says. \u201cMaybe next time it will be correct.\u201d<\/p>\n
Fundamental Physics<\/p>\n
One domain where quantum processors are already seeing success is in modeling particle interactions to solve mysteries at the heart of fundamental physics. \u201cIt goes back to Feynman, who articulated that you can\u2019t really understand how nature works unless you build it at the same length scale,\u201d says quantum physicist and material scientist Michelle Simmons, founder and CEO of Silicon Quantum Computing (SQC) in Sydney.<\/p>\n
Simulating interactions of multiple particles rapidly becomes impossible with a classical computer, explains Daniel Gonz\u00e1lez-Cuadra, a quantum physicist at the Institute for Theoretical Physics in Austria. \u201cThe information that it takes to describe the state of these systems grows exponentially with the size of the system, and at some point you just don\u2019t have enough memory,\u201d he says. Capturing all the complexities requires an equally complex quantum machine.<\/p>\n
Groups around the world are making progress on this front. One big area of focus is increasing the \u201ccoherence\u201d of qubits so they stay in superposition long enough to carry out their calculations. In 2021 Kim\u2019s China-based team demonstrated that trapped-ion qubits can maintain coherence for more than an hour, which he says was \u201ca very important benchmark for scaling up meaningful quantum simulations.\u201d<\/p>\n
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Jen Christiansen; Source: \u201cSimulation of Matter\u2013Antimatter Creation on Quantum Platforms,\u201d by Michele Burrello, in Nature, Vol. 642; June 12, 2025 (reference)<\/p>\n
Last year two teams independently published real-time quantum simulations of the creation of matter and antimatter during a process called string breaking. According to the Standard Model of particle physics, pairs of strongly interacting subatomic particles, such as quarks, behave as though they are joined by an elastic string, \u201clike a violin string that vibrates,\u201d explains quantum physicist Pedram Roushan of Google Quantum AI in Santa Barbara. Roushan\u2019s team ran its simulation on Google\u2019s Sycamore chip, which uses superconducting loops as qubits. The simulation showed how pulling two particles apart increases the string\u2019s tension until it finally snaps, releasing the stored energy by generating a new pair of matter and antimatter particles. \u201cThese theoretical concepts were known since the 1970s, but we were able to visualize them and take a picture of the strings and their breaking,\u201d Roushan says.<\/p>\n
The Sycamore experiment was an example of a digital simulation, meaning it was performed on a multipurpose chip with circuits of qubits designed to do many different tasks. In contrast, Gonz\u00e1lez-Cuadra, Zoller and their colleagues worked with a team at QuEra Computing in Boston to create an analog simulator\u2014a lattice of neutral-atom qubits specially built to simulate string breaking. The two string-breaking simulations are among the first to model particle interactions in two spatial dimensions, Gonz\u00e1lez-Cuadra says. \u201cThe physics is richer, so we could see how these strings fluctuate,\u201d he explains.<\/p>\n
\u201cWe\u2019re optimistic that we\u2019re going to see the first practical applications in five years.\u201d \u2014Sergio Boixo, Google Quantum AI<\/p>\n
These kinds of simulations won\u2019t replace particle physics experiments. Instead they will help physicists hone their theories and make testable predictions that can be checked at particle accelerators. And so far they have simulated only simple models that can also be checked with classical computers. But Gonz\u00e1lez-Cuadra believes quantum simulators will start to surpass their classical counterparts in a couple of years, marking an era of true quantum advantage. This possibility raises the question of how physicists can be sure their quantum simulations are spitting out reliable results. To answer it, last year Zoller and his colleagues posted a preprint paper on arXiv.org describing a strategy for developing an analog quantum machine that not only makes predictions but also quantifies the uncertainty in those predictions. \u201cIf you ask me what the big challenge for quantum simulation is, it is the frontier of verification,\u201d Zoller says.<\/p>\n
Materials Design<\/p>\n
The dream, Zoller says, is for quantum simulators to shift from a passive \u201cdiscovery mode\u201d\u2014in which they are used to model nature\u2014to an \u201cactive design mode\u201d in which quantum computers would spit out recipes for synthesizing new molecular structures with specific desirable properties. The quantum engineering of new molecules could lead to better drugs and to batteries that don\u2019t use costly, environmentally damaging commodities such as rare earth elements. \u201cThese things take billions of dollars, so if you just make something even a few percent cheaper or a few percent better, then that\u2019s really worth it,\u201d Martinis says. \u201cIt could be a huge thing not just monetarily but for changing how things are built to make them more ecological.\u201d<\/p>\n
Room-temperature superconductivity is one high-priority target. Superconductivity, the free flow of electricity without resistance, typically requires a material to be cooled to extremely low temperatures, which makes it impractical for many applications. But certain materials exhibit the phenomenon at higher temperatures, and some researchers hope quantum engineering can help them find new superconducting materials that don\u2019t need any cooling at all. \u201cThese systems consist of 1023 particles, whereas classically we can model only about 100 particles,\u201d says Henrik Dreyer, a quantum physicist at Quantinuum in Munich. Physicists would need to reduce the error rates in quantum processors to just one in a million to make it possible; at the moment the best chips are down to slightly below one in 1,000, Dreyer explains.<\/p>\n
Dreyer and his colleagues have been performing digital simulations of cuprate superconductors using Quantinuum\u2019s Helios chip, which employs 98 trapped-ion qubits. In carefully controlled laboratory conditions, shooting these materials with a laser can very briefly\u2014and surprisingly\u2014create a superconducting state at a relatively high temperature. \u201cThe first question is: Why?\u201d Dreyer says. Last year Quantinuum posted a preprint on arXiv saying its two-dimensional simulation modeling the material shows that under laser fire, its electrons pair up\u2014a condition necessary for flow without resistance. \u201cThe ultimate question is,\u201d as posed by Dreyer, \u201cCan we engineer it to do this at room temperature for a minute, an hour, 10 days, or more?\u201d<\/p>\n
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Jen Christiansen; Source: \u201cSuperconducting Pairing Correlations on a Trapped-ion Quantum Computer,\u201d by Etienne Granet et al.; February 17, 2026 (arXiv:2511.02125v3) (reference)<\/p>\n
Meanwhile Simmons and her SQC colleagues in Australia have developed a simulation system called Quantum Twins\u2014a 2D array of 15,000 clusters of phosphorus atoms embedded in silicon\u2014to create analogs of various materials. In February the team reported that the platform can simulate the transition between insulating behavior and metallic conduction. \u201cWe can now start to simulate things like superconductivity, different battery materials, artificial photosynthesis and small drug designs,\u201d Simmons says.<\/p>\n
Google Quantum AI\u2019s Sergio Boixo notes that the company has collaborated with BASF on battery design, Sandia National Laboratories in Albuquerque on fusion energy, and German chemical company Covestro on pharmaceutical development. Last year it implemented an algorithm for modeling molecular structure on Willow, Google\u2019s 105-qubit superconducting processor, that can be used in combination with nuclear magnetic spectroscopy. The technique, which works by bouncing signals onto qubits and effectively listening for their echoes, runs 13,000 times faster on Willow than an equivalent algorithm would on the best classical supercomputer. One important aspect of the algorithm\u2019s design is that it allows results to be corroborated by another quantum machine. \u201cQuantum Echoes is the world\u2019s first quantum-verifiable algorithm with quantum advantage,\u201d Boixo says. \u201cWe\u2019re optimistic that we\u2019re going to see the first practical applications in five years.\u201d<\/p>\n
Quantum AI<\/p>\n
If you really want to generate hype, combine the word \u201cquantum\u201d with \u201cAI,\u201d jokes Jacob Biamonte, an expert on quantum machine learning at \u00c9TS Montreal. Indeed, as quantum processors get bigger, some physicists are focusing on using them to boost the performance and energy efficiency of classical artificial intelligence.<\/p>\n
Last year SQC launched Watermelon, a quantum-enhanced AI processor, to help speed up machine learning. Classical AI systems are already adept at finding patterns in vast datasets, which makes them particularly useful for optimizing communications and energy networks, for instance. SQC\u2019s quantum technique builds on classical reservoir computing, a method for taking input data points and transforming them onto a higher-dimensional neural network, making it easier to find patterns. In 2017 scientists in Japan predicted that the classical nodes of the neural network could be replaced by a smaller number of qubits subject to quantum interference. \u201cThe advantage of having a quantum reservoir is that you get an exponential increase in dimensionality,\u201d Simmons says, enabling a quantum reservoir to achieve the same training results as a classical reservoir but potentially faster and using fewer resources.<\/p>\n
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Jen Christiansen; Source: \u201cQuantum Reservoir Computing Implementation on Coherently Coupled Quantum Oscillators,\u201d by Julien Dudas et al., in npj Quantum Information, Vol. 9; July 7, 2023 (reference)<\/p>\n
Watermelon\u2019s first commercial trial\u2014in collaboration with Australian telecommunications company Telstra\u2014has shown promising results. Telstra already uses AI to monitor latency and bandwidth patterns on its networks. It takes about three weeks to train the company\u2019s models using standard classical methods. With Watermelon\u2019s help, Telstra achieved the same training results in just two days. \u201cIn the grand scheme of the world, that is quite significant because at the moment, data centers are very power hungry,\u201d Simmons says, noting that similar optimizations could be rapidly rolled out to other energy-intensive tasks, such as training AIs for image recognition, fraud detection and market prediction. \u201cI feel like I\u2019m in this freight train that\u2019s going at superhigh speeds,\u201d she says.<\/p>\n
Ekert, however, remains cautious about the longer-term benefits of using quantum AI processors to analyze classical datasets. \u201cTurning classical data into a quantum form is terribly inefficient,\u201d he says. Where quantum computing and machine learning are already being combined most helpfully, Ekert argues, is in physicists\u2019 use of classical AI to design quantum error-correcting codes and better quantum hardware. Last year, for example, Finnish company QMill launched a classical AI service for compressing quantum circuits, reducing the number of gates needed for operation by 20 to 50 percent. Biamonte also thinks the current vision is too small. \u201cIf the goal is to use quantum computers to do machine learning for classical data, it doesn\u2019t even make sense, because classical machine learning is already so good,\u201d he says.<\/p>\n
If quantum processors could one day be used to analyze quantum data directly, however, that would be a game changer. \u201cThere should be these wonderful patterns that classical computers cannot detect because there are just too many data for their memory,\u201d Biamonte says. A quantum AI could riff on the molecular structure of an existing patented drug, for instance, to generate multiple different configurations with the same benefits. It could then assess those molecules to see whether they could be synthesized and patented before a company committed funds to trying to make them. \u201cThat\u2019s the exciting future that doesn\u2019t exist yet,\u201d Biamonte says.<\/p>\n","protected":false},"excerpt":{"rendered":"The 21st-century fervor about building the first industrial-scale quantum computer, pioneering theoretical physicist Peter Zoller says, is akin…\n","protected":false},"author":2,"featured_media":680455,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24],"tags":[49,48,314,66],"class_list":["post-680454","post","type-post","status-publish","format-standard","has-post-thumbnail","category-physics","tag-ca","tag-canada","tag-physics","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/680454","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=680454"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/680454\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/680455"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=680454"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=680454"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=680454"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}