The Shadows Researchers

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" What We Learn From the Shadows Researchers have been trying to use classical computers to predict quantum states since at least 1989. Typically, a quantum system with n qubits — the quantum equivalent of a bit — can be represented by a classical array of 2n numbers. The size of this array increases exponentially with the number of qubits, meaning that the required computing power quickly becomes prohibitive. John Preskill in a gray shirt, posing in front of a blue background and smiling. John Preskill helped show how “classical shadows” of quantum systems could, in theory, allow researchers to process quantum data on classical computers. Max Gerber In late 2017, the computer scientist Scott Aaronson suggested that it's not necessary to know the full classical representation of a quantum system.


Instead, you might be able to learn about a given quantum state and predict Phone Number List its properties using only a subset of the representation. Then in 2020, the physicists Hsin Yuan (Robert) Huang and Richard Kueng pioneered a practical approach to Aaronson's method. Their technique allowed them to predict many characteristics of the quantum state of a system from very few measurements using classical methods. The process involved constructing a “classical shadow” from these measurements: a succinct classical representation of the quantum system, akin to an actual shadow, which conveys a lot of information — but not everything — about the object casting it. “You have to lower your sights and only try to predict certain quantum observables," said John Preskill, a theoretical physicist at the California Institute of Technology who worked with Huang and Kueng on the project.



Are classical shadows enough to capture quantum complexity, or do we need a fully quantum approach? Are there quantum properties or dynamics that will forever be out of reach? “Their work has been pioneering to start thinking about these questions,” said Soonwon Choi, a physicist at the Massachusetts Institute of Technology. And maybe one day, Preskill said, researchers will collect enough experimental data to be able to predict system features that have never been encountered in the lab. “This is one of the big-picture goals of applying machine learning to quantum physics, " he said. "And we were able to show that at least in some settings, you can make accurate predictions." Editor's note: Scott Aaronson is a member of Quanta Magazine's advisory board.