Why don’t we have useful quantum computers yet?

Quantum computers have long promised to solve some problems faster than any conventional or classical computer can. In fact, Google delivered on this promise in 2019, announcing that its quantum computer had achieved quantum supremacy, performing a calculation that the best classical computers of the time could not. can be done. EQUAL New Scientist Google said at the time that it had “secured another place in history”.

However, the next chapter of the quantum revolution is struggling to be written. Since Google’s breakthrough in 2019, other groups have made similar claims, but in each case, improved algorithms for classical computers have reasserted dominance over quantum machines. , or at least threaten their dominance. With this back and forth going on, will quantum computers ever move forward?

While Google’s initial results are unlikely to be replicated by classical computers in any reasonable time frame, in 2022 researchers have found a new algorithm to do so. there. Solving the question of quantum supremacy once and for all will depend on both the number of qubits or quantum bits used and the complexity with which they are programmed, known as circuit depth. Only when a computer scores high enough on both counts will the result be out of reach for any classical computing or algorithmic improvements.

“Eventually, the number of qubits will become large enough that no classical algorithm can could catch up, but it’s not clear at what point — that’s something Google is trying to figure out,” said Bill Fefferman at the University of Chicago, Illinois.

Google’s initial results demonstrated a task called random circuit sampling, which involves testing whether the values ​​of qubits after they undergo random operations are truly random. It uses 54 superconducting qubits for 20 cycles, which refers to the time they take to perform those random operations and relates to the depth of the circuit.

Increasing complexity

In April of this year, Google researchers made the same feat but with 70 qubits in 24 cycles. While the gain may seem modest, the jump in complexity is large and, it hopes, enough to make the quantum-classical gap more permanent. Google claims that a calculation on a 70-qubit machine would now take the best supercomputers 47 years to perform.

Currently, this is considered the best demonstration of quantum supremacy, which has not been beaten by classical computers, but these 70 qubits are far from perfect – they are hampered by “noise”, making it difficult to verify that the computer Full operation becomes difficult. utilizes its quantum nature and is not susceptible to classical advances. Researchers at Google are currently working on how they can prove and quantify that the computer is performing a real quantum task and how this noise affects that measurement.

So far, they’ve done this through a benchmark that uses classical computers to predict outputs for a quantum machine and then calculates the difference between the final answers. The larger the difference, the more complex the quantum system.

But it remains unclear how faithful this measure is to the true nature of quantum computers, and at what point noise renders this measurement useless. Google and, in a separate result, Fefferman and his colleagues pinpointed the noise level at which we could still effectively use this benchmark for a quantum computer with a certain amount of qubits. certain. “This is really important because it gives us a benchmark by which we can compare, in a similar way, the next generations of these experiments,” Fefferman said.

Researchers at the University of Science and Technology of China (USTC) also demonstrated quantum supremacy using 56 qubits of a superconducting quantum computer called Zuchongzhi – a piece of hardware similar to that of a superconducting quantum computer called Zuchongzhi. Google – but they are also working on an alternative quantum computing design that uses photons for qubits. The machine, called Jiuzhang, has demonstrated a quantum advantage but also comes with some unique challenges.

Jiuzhang performs boson sampling, measuring a sample of photons bouncing around a maze of mirrors and beamsplitters. Classical computers cannot make these measurements exactly on a certain number of photons. Verifying that the measurements are indeed quantum is by no means straightforward – in fact, a coherent way to do so currently does not exist. “The theory for certifying these machines remains largely an open question,” says Nicolás Quesada at Polytechnique Montréal in Canada.

Because of this, the researchers’ results are susceptible to classical breakthroughs. The USTC claims their initial Jiuzhang results will take 600 million years to verify classically, but in 2022 a team of researchers have shown that it could instead be done in months, due to a flaw in the way photons are measured with the detector. In April, the USTC patched the flaw using a new type of photon detector and reaffirmed its quantum advantage – but without a coherent means of verifying this advantage, improvements could not be made. Classical advances can still eliminate it, says Quesada.

Actual problem

While the USTC team is focused on consolidating its quantum advantage and understanding how the machines work, quantum advantage itself has yet to find any practical application, though not for lack of try.

In February, researchers from USTC published a paper exploring how boson sampling could be applied to graph problems, which could be practically useful for things like drug design and machine learning. “The way we describe a quantum computer, the kind of mathematical framework used to do it, is very similar to other interesting mathematical frameworks,” said Naomi Solomons at the University of Bristol, UK.

Although the authors conclude that boson sampling can make some graph problems much faster, they face the same verification problem as before and cannot rule out whether the algorithms Can classical math give the same performance boost?

Mapping real-world problems to quantum computers and vice versa is likely to make up a large part of research and development in the coming years, says Jay Gambetta at IBM. “We could say quantum processors are reaching this utility-scale size, but I don’t think we’re doing enough as a community to figure out what circuits we’re going to run. – I think that problem is as difficult as other people.”

Gambetta and his colleagues are part of four separate working groups with scientists in other fields looking at how to apply current quantum machines to scientific problems, to topics like high-energy physics, materials, life sciences and finance. In July, the first results of the high-energy physics team, following discussions at CERN in Switzerland, were published. Specific problems, such as how particles bounce off each other and how pairs of particles separate, are highlighted as having particular promise for quantum machines in the near future.

Rather than mark the point at which quantum computers can be said to ultimately have an advantage over classical machines using benchmarks and mathematical proofs, it may be more reasonable to define it as when Scientists in other fields choose to use quantum computers for their work, Gambetta said. “I think there will be many quantum advantages, but I think when it comes from someone who is not a quantum information scientist that is when I will be interested,” he said.