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Anti-butterfly effect enables new benchmarking of quantum computer performance

The anti-butterfly effect enables new benchmarking of quantum-computer performance

Bin Yan, shown here, Nikolai Sinitsyn and Joseph Harris have developed a new method that determines how much information is lost from a quantum system to decoherence and how much is preserved through information scrambling. Source: Los Alamos National Laboratory

Research drawing on the quantum “anti-butterfly effect” solves a long-standing experimental problem in physics and establishes a way to benchmark the performance of quantum computers.

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

Yan is the corresponding author of a paper on benchmarking information scrambling, published today in Physical Review Letters. “Our protocol quantifies information scrambling in a quantum system and clearly distinguishes it from false positive signals in the noisy background due to quantum decoherence,” he said.

Noise in the form of decoherence removes all quantum information in a complex system such as a quantum computer as it interacts with the surrounding environment. Information scrambling through quantum chaos, on the other hand, spreads information throughout the system, protects it and allows it to be retrieved.

Coherence is a quantum state that enables quantum computing, and decoherence refers to the loss of that state as information escapes the surrounding environment.

“Our method, which captures the quantum anti-butterfly effect we discovered two years ago, transforms a system forward and backward in time in a loop, so we can apply it to any system with time-varying dynamics, including quantum computers. and quantum simulators that use cold atoms,” said Yan.

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

The inability to distinguish decoherence from information scrambling has prevented experimental research into the phenomenon. First studied in black-hole physics, information scrambling has proven relevant to a wide range of research areas, including quantum chaos in many-body systems, phase transitions, quantum machine learning and quantum computing. . Experimental platforms for studying information scrambling 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 that proved that the development of quantum processes backwards on a quantum computer to destroy information in the simulated past caused a small change if back to the present. In contrast, a classical-physics system erases information irretrievably during the back-and-forth time loop.

Building on this discovery, Yan, Sinitsyn and co-author Joseph Harris, a graduate student at the University of Edinburgh who works in his current role as a participant at the Los Alamos Quantum Computing Summer School, developed the protocol. It prepares a quantum system and subsystem, evolves the entire system forward in time, causes a change in another subsystem, then evolves the system backward in the same amount of time. Measuring the overlapping information between two subsystems shows how much information is preserved by scrambling and how much is lost by decoherence.

Finding unity in quantum chaos

More information:
Joseph Harris et al, Analyzing Benchmarking Information, Physical Review Letters (2022). DOI: 10.1103/PhysRevLett.129.050602

Provided by Los Alamos National Laboratory

Citation: Anti-butterfly effect enables new benchmarking of quantum computer performance (2022, July 26) retrieved 26 July 2022 from -benchmarking-quantum.html

This document is subject to copyright. Except for any fair dealing for the purpose of private study or research, no part may be reproduced without written permission. Content is provided for informational purposes only.

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