Anti-butterfly effect enables new benchmarking of quantum computer performance

Research based on the quantum “anti-butterfly effect” solves a long-standing experimental problem in physics and establishes a method for comparing the performance of quantum computers.

“Using the simple and robust protocol we developed, we can determine the degree to which quantum computers can process information effectively, and it also applies to information loss in other complex quantum systems,” said Bin Yan, a quantum theorist at Los Angeles. Alamos National. Laboratory.

Yan is the corresponding author of an article on encoding benchmarking information, published today in Physical Review Letters. “Our protocol quantifies information encoding in a quantum system and unambiguously distinguishes it from false positive signals in the noisy background caused by quantum decoherence,” he said.

Noise in the form of decoherence erases all quantum information in a complex system like a quantum computer when it couples with the surrounding environment. Information passing through quantum chaos, on the other hand, spreads it throughout the system, protecting it and allowing it to recover.

Coherence is a quantum state that allows quantum computingand decoherence refers to the loss of that state as information leaks out into the surrounding environment.

“Our method, which is based on the quantum anti-butterfly effect we discovered two years ago, evolves a system back and forth through time in a single loop, so we can apply it to any system with reversal dynamics. in time, including quantum computers. and quantum simulators that use cold atoms,” Yan said.

The Los Alamos team demonstrated the protocol with simulations on IBM cloud-based quantum computers.

The inability to distinguish decoherence from information encoding has hampered experimental investigation of the phenomenon. First studied in black hole physics, information encoding has proven relevant in a wide range of research areas, including quantum chaos in many-body systems, phase transition, quantum machine learning, and quantum computing. Experimental platforms to study information encoding include superconductors, trapped ions, and cloud-based quantum computers.

Practical application of the quantum anti-butterfly effect

Yan and co-author Nikolai Sinitsyn published a paper in 2020 showing that the evolution of quantum processes backwards in a quantum computer to corrupt information in the simulated past causes little change when returned to the present. By contrast, a classical physics system fuzzies the information irretrievably during the round-trip time cycle.

Building on this discovery, Yan, Sinitsyn, and co-author Joseph Harris, a University of Edinburgh graduate student who worked on the current paper as a participant in the Los Alamos Quantum Computing Summer School, developed the protocol. It sets up a quantum system and a subsystem, evolves the entire system forward in time, causes a change in a different subsystem, and then evolves the system backwards over the same amount of time. Measuring the information overlap between the two subsystems shows how much information has been preserved by encoding and how much has been lost. decoherence.