Wed. Jan 26th, 2022


The race is underway to find the first practical use for quantum computers. It will probably pale in comparison to the ultimate potential of technology: to power a computer that can tackle any problem at lightning speed. But many in the field believe a less ambitious milestone will be reached within two years, causing a rabbit to be the first to bring it into the mainstream.

Since 1982, when physicist Richard Feynman first outlined how the strange properties of quantum mechanics can be harnessed to revolutionize computers, much attention has been drawn to the point where a quantum system far surpasses today’s “classical” systems – something that known as quantum. supremacy.

Google claimed to have reached this milestone two years ago. Demonstrating it, however, did not address a practical problem – a calculation that would be impossible for a classic computer to solve – and IBM and others soon showed that classic computers could be adapted to some of the supposed benefits of Counter Google’s system.

Since then, many researchers have shifted their focus to something less ambitious. Known as quantum advantage, it is the point at which a system uses the technology to bring about a step-by-step change in the solution of a practical computer task.

The first practical application based on quantum benefit will officially introduce the quantum age, predicted Peter Chapman, CEO of IonQ, which in 2021 became the first quantum computer company listed on Wall Street. He compared it to the VisiCalc spreadsheet program, which “made the computer usable for business” in 1979 and began the computer age of computers.

The prospect of finding a practical application for the technology has fueled the industry in recent months and unleashed a competition to be first, said Matt Johnson, CEO of QC Ware, a quantum software company. “The challenge is to be one of the first to help enterprise customers get quantum acceleration,” he said.

The industry’s gear shift has been spurred by improvements to quantum hardware systems announced in late 2021, along with projections of the kind of systems that will be available two years from now.

The hardware has seen a 10-fold improvement over the past two years, according to Will Oliver, a professor at the Massachusetts Institute of Technology. Speaking at the recent Q2B practical quantum computing conference in Silicon Valley, he said it raised a new question for the industry: whether it comes close to producing “commercially relevant algorithms”.

Today’s quantum machines are known as NISQ systems, short for noisy intermediate scale quantum. Their number of quantum bits, or quantum bits, is still limited, and the quantum bits are unable to hold their quantum states for more than a few microseconds, something that brings errors, or “noise,” into calculations. Yet even these can be harnessed to make small but significant progress in solving real problems, according to people working on the technology.

“If you use it to detect cancer, and you can have a 2 percent better detection rate, are you going to use the lesser one for your patients?” said Christopher Savoie, CEO of Zapata, a quantum software company.

IBM released its first system using 127 qubits in November and confirmed a roadmap that it said would see this rise to more than 1,000 qubits over two years.

“The ability to demonstrate quantum advantage over the next two years is possible,” said Dario Gil, head of research at IBM. While acknowledging that “it is an open question” whether the remaining technical challenges can be overcome, he said that hardware advances and improvements in the design of algorithms pave the way for reducing the noise in today’s systems to a level where it is useful.

Others say they are on a similar path. Rigetti Computing, a start-up company that plans to go public with a sale to a special purpose-sourcing company, announced an 80-kwbit system in December, based on a new modular design that combine two 40-kwbit processors.

Using this new architecture, “we expect to have systems of approximately 1,000 qubits in 2024 and 4,000 qubits in 2026,” said Chad Rigetti, the company’s CEO. “We expect those systems to carry us through the milestones of narrow and then broad quantum advantage.”

The latest hardware, while not itself capable of supporting breakthrough applications, will put more powerful tools in the hands of researchers. This will accelerate understanding of how to program practical systems, he and others said.

IBM said it has 27 quantum systems in use, and that 170 organizations, including many companies, are now using them for research.

“Our machines are now on the speed and scale and performance where they can code and represent practical cases of problems, rather than toy cases of problems,” Rigetti said. This made it possible to compare their performance in solving practical problems with today’s fastest classical computers, which gave researchers their first real understanding of the path to quantum advantage, he added.

One widespread use for quantum computers – simulating complex molecules, paving the way for breakthroughs in things like drug discovery and new battery technology – is likely to be beyond the scope of the first practical systems, according to experts in the field.

Unless there is a “eureka moment”, computers capable of this kind of simulation are at least three years away, says Ilyas Khan, CEO of Quantinuum, the company formed by the recent merger of Cambridge Quantum and Honeywell ‘s quantum computer department.

Instead, he and others said much of the short-term attention shifted to the attempt to use quantum systems in tandem with classical computers to improve machine learning, the technique behind much of today’s artificial intelligence. The large datasets that already exist to support machine learning, coupled with the scale and complexity of the problems, have ripe this field for a quantum breakthrough, Rigetti said.

Recent work in designing more efficient quantum algorithms has focused on things like solving optimization problems and selecting a single item from a large dataset – techniques that can be used for better weather modeling, or to identify potential credit card fraud. Goldman Sachs was among the banks working to use the technology to improve option pricing, while Volkswagen explored ways to optimize its manufacturing processes.

Whether the first practical use of quantum computing comes from one of these companies, or one from the other banks, pharmaceutical companies or manufacturers trying to apply the technology, there is little doubt that the race is on now.



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