The 2002 Nobel Prize in Physics Shows the Potential of Quantum Computers


Quantum computing, one of the technologies of the future like nuclear fusion power or self-driving cars, is not only forever out of reach, but it seems to have the potential to be transformative. If researchers can develop a stable and reliable quantum machine, it will slow down according to Moore’s Law (Intel co-founder Gordon Moore’s long-accurate prediction of how his chips will speed up). The pace of computing that exists is likely to accelerate. And cheaper — seems to be coming to an end. But the road to a practical quantum computer is long and difficult, combining some of the hardest problems in quantum science with the hardest problems in computing his hardware.

Tuesday morning, when the 2022 Nobel Prize in Physics was awarded to three researchers, it became clear again just how long the road to quantum computing is and how important it is to get there. A new era in quantum technology, as stated by the Nobel Committee on Physics.

American John F. Crowther showed in 1972 that photon pairs are entangled, emphasizing that particles behave like single units even when they are far apart. His Alain Aspect at the University of Paris carried the work forward a decade later, and in 1998 Austrian physicist Anton Zeilinger investigated his entanglement of three or more particles. Together, the two paved the way for “new technologies based on quantum information,” as the Nobel Committee put it.

But as I learned earlier this year when I visited the small community of Yorktown Heights in Westchester County, a practical, real-world quantum computer requires more than theory. There, among rolling hills and old farmhouses, is the Thomas J. Watson Research Center, his 1960s jet-era IBM research headquarters designed by Eero Saarinen.

Deep inside the building, through endless corridors and security gates guarded by iris scanners, is where IBM scientists work hard. Dario Gil, director of research at IBM, is developing “the next frontier in computing,” quantum computers.

I was at the Watson Center to preview IBM’s latest technology roadmap for making quantum computing practical at scale. This includes “qubit counts”, “quantum coherence”, “error mitigation”, “software orchestration”, and many other topics that require a background in computer science and what it takes to become a quantum-savvy electrical engineer. included the story of A mechanism for full compliance.

I’m not one of them, but I’ve been watching the world of quantum computing for a long time, and I’ve seen a lot of work done here by researchers at IBM (competitors like Google and Microsoft, and countless startups around the world). I have found that the work being done is effective. Powering the next big leap in computing. Given that computing, as Gill told me, is a “horizontal technology that impacts everything,” this can be applied to everything from cybersecurity to artificial intelligence to designing better batteries. will have a significant impact on the progress of

Provided, of course, that you can actually make these things work.

enter the quantum realm

The best way to understand quantum computers, short of setting aside a few years of graduate school at MIT or Caltech, is to compare them to the kind of machines I’m typing in this article. on: Classic computer.

My MacBook Air runs on an M1 chip packed with 16 billion transistors. Each of these transistors can represent a ‘1’ or ‘0’ of binary information at once. i.e. bits. A huge number of transistors gives the machine its computing power.

16 billion transistors packed into a 120.5 mm square chip is a lot, the first transistorized computer, TRADIC, had less than 800. The semiconductor industry’s ability to design more transistors on a chip than ever before is a trend prediction by Intel co-founder Gordon Moore. The law that bears his name enabled the exponential growth of computing power, which enabled almost everything else.

A view of the IBM System One quantum computer at the Thomas J. Watson Research Center.
Brian Walsh/Vox

But no matter how many transistors are packed into a square of silicon in a Taiwanese semiconductor manufacturing plant (or “fab” in industry parlance), there are some things a conventional computer can and never can do. This is where a unique and frankly bizarre property of quantum computers comes into play.

Quantum computers process information using qubits, which can simultaneously represent ‘0’ and ‘1’, instead of bits. how do they do that? I’m straining my level of expertise here, but basically qubits make use of a quantum mechanical phenomenon known as ‘superposition’. This leaves the properties of some subatomic particles undefined until they are measured. Think of Schrödinger’s cat. Dead and alive at the same time until you open the box.

Single qubits are also attractive, but when you start adding them, it becomes very exciting.Conventional computing power increases linearly with the addition of each transistor, whereas quantum computer power increases exponentially. Each new trusted qubit is added. This is due to another quantum mechanical property called ‘entanglement’. This allows each qubit’s individual probability to be influenced by other qubits in the system.

All of this means that the upper bound of viable quantum computing power far exceeds what is possible with conventional computing.

So quantum computers can theoretically solve problems that even the most powerful conventional computers could not. What kind of problem are you having? What about the fundamental nature of physical reality, which ultimately runs on quantum mechanics rather than classical mechanics? (Sorry, Newton.) “Quantum computers simulate problems found in nature and chemistry. ,” said Jay Gambetta, vice president of quantum computing at IBM.

Quantum computers could simulate the properties of theoretical batteries to help design batteries that are far more efficient and powerful than current versions. Solve complex logistics problems, discover optimal delivery routes, and enhance climate science forecasts.

On the security side, quantum computers can break cryptography, potentially compromising everything from emails to financial data to national secrets. This is why the race for quantum supremacy is also an international race, into which the Chinese government is pouring billions of dollars. These concerns prompted the White House to announce a new memorandum earlier this month to build national leadership in quantum computing and prepare the nation for the threat of quantum-assisted cybersecurity.

Beyond security issues, potential financial benefits can be important. Companies are already offering nascent quantum computing services via the cloud for customers such as Exxon Mobil and Spanish bank BBVA. While the global quantum computing market was worth less than his $500 million in 2020, the International Data Corporation predicts revenues will reach his $8.6 billion by 2027 with more than $16 billion of investment. doing.

But that won’t be possible unless researchers can still do the hard engineering work of turning quantum computers from largely scientific experiments into credible industries.

cold room

Inside the Watson building, Jerry Chow, who directs IBM’s Experimental Quantum Computer Center, opens a nine-foot glass cube to reveal what looks like a gold chandelier. It’s his Quantum System One from IBM. Many of the chandeliers are essentially high-tech refrigerators, complete with coils carrying superfluids that can cool hardware to temperatures one hundred degrees Celsius above absolute zero.

Refrigeration is key to making IBM’s quantum computer work, and it also shows why doing so is a major engineering challenge. Quantum computers are potentially much more powerful than classical computers, but they are also much more cumbersome.

Remember what I said about the quantum properties of superposition and entanglement? Qubits can do things you never dreamed possible, but small changes in temperature, noise, or radiation can Their properties can be lost through something called decoherence.

This fancy cooling is designed to prevent the system’s qubits from decohealing before the computer has finished computing. The earliest superconducting qubits lost coherence in less than a nanosecond, but today’s state-of-the-art quantum computers from IBM can maintain coherence for as long as 400 microseconds. (Each second contains 1 million microseconds.)

The challenge faced by IBM and others is to “scale systems beyond thousands or tens of thousands of qubits, perhaps to millions of qubits,” while creating an error-less quantum computer. to design.

It may take years. Last year IBM announced Eagle, his 127-qubit processor. With a new technology roadmap, later this year he will unveil a 433-qubit processor called Osprey, and by 2025 he aims to unveil a computer with more than 4,000 qubits. , Quantum computing could go beyond the experimental stage, IBM CEO Arvind Krishna told reporters at a news conference earlier this month.

Many experts are skeptical whether IBM or its competitors will get there, and the engineering problems presented by quantum computers raise the possibility that the system will be too difficult to be truly reliable. “What’s happened in the last decade is there’s been a huge number of claims about what quantum computers can do right away, like solving all these machine learning problems,” Texas, told me last year. . “But these claims are about 90% bullshit.” To deliver on that promise, “innovative developments will be required.”

In our increasingly digital world, further progress depends on making the computers we create better than ever before. And that depends on the work of researchers like Chow and his colleagues. He’s struggling in a windowless lab to solve some of computer engineering’s toughest problems, and build the future in the process.

A version of this story first appeared in the Future Perfect newsletter. sign up here apply!

Update Oct. 4 4:00 PM ET: This article was originally published on May 24 and has been updated to reflect the awarding of the 2022 Nobel Prize in Physics to Krauser, Aspect and Seilinger.



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