Interpreting Quantum Phenomena with Classical Machines

Saturday - September 16 2023, 05:21 UTC - 1 year ago

Researchers have developed a technique which allows classical computers to predict quantum behavior and understand the quantum universe better. The process involves constructing a "shadow" which conveys information about the quantum system. This method allows researchers to escape the exponential complexity of full quantum system modeling.

Understanding the quantum universe is not an easy thing. Intuitive notions of space and time break down in the tiny realm of subatomic physics, allowing for behavior that seems, to our macro sensibilities, downright weird. Quantum computers should allow us to harness this strangeness. Such machines could theoretically explore molecular interactions to create new drugs and materials. But perhaps most important, the world itself is built upon this quantum universe — if we want to understand how it works, we probably need quantum tools. However, current near-term quantum devices are still far from fulfilling that promise, since they can’t reliably execute a large number of quantum interactions. Until researchers can overcome this issue, classical computers remain the best way to solve real-world problems, however inefficiently they do so.

But maybe there’s a workaround, a kind of quantum compromise. A spate of recent papers suggests that it may be possible to take the quantum system you’d like to understand, input its properties into classical machines, and use those machines to predict the quantum system’s behavior. By combining a new way of modeling quantum systems with increasingly sophisticated machine learning algorithms, researchers have established a method for classical machines to model and predict quantum behavior. "I think the work is very significant," said Yi-Zhuang You, a physicist at the University of California, San Diego who is unaffiliated with the studies. "It fundamentally changes the field in the sense that it’s the right way to combine quantum computation and machine learning." .

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.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 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.

With this model, if you want to predict a certain number of properties of the system, you need to perform only around cubic root of 2ⁿn measurements, allowing the researchers to escape the exponential complexity of modeling the full system.