A newly developed framework for quantifying uncertainties enhances the predictive power of analog quantum simulations.
Simulating quantum many-body systems is a major objective in nuclear and high-energy physics. These systems involve large numbers of interacting particles governed by the laws of quantum mechanics, making them far more complex to model than simpler two-particle systems.
Due to this complexity, even the most advanced classical computers struggle to simulate many-body problems accurately. Quantum computing offers a promising solution through a technique known as analog quantum simulation. However, successful simulations rely on theoretical approximations to represent many-body systems within a quantum computer.
In a new study, nuclear physicists introduced a new framework to evaluate and reduce the impact of these approximations, improving the reliability of quantum simulations for many-body physics.
Addressing Uncertainty in Quantum Simulations
This method provides a new tool for quantifying the uncertainties in analog quantum simulations of dynamical processes.
Quantum computers are becoming more and more reliable and resilient to noise. However, to make reliable predictions, scientists need to understand and quantify sources of error and their effects on analog quantum simulations. Researchers can use the techniques developed in this work to improve the precision of future simulations.
In an analog quantum simulation, a highly controllable quantum system replicates the behavior of a more exotic system. A leading architecture for such simulations is Rydberg-atom quantum computers, which are scalable arrays of Rydberg atoms that support a universal quantum gate set. Scientists expect that with rapidly improving control, analog quantum computers will enable near-term advantages in uncovering new physics.
To make these simulations scientifically useful, researchers need robust theoretical approximations in representing systems of interest on quantum computers. Nuclear physicists at the University of Washington developed a new framework to systematically analyze the interplay of these approximations.
They showed that the impact of such approximations can be minimized by tuning simulation parameters. Such optimizations are demonstrated in the context of spin models sharing key features with nuclear interactions.
Reference: “Optimization of algorithmic errors in analog quantum simulations” by Nikita A. Zemlevskiy, Henry F. Froland and Stephan Caspar, 15 May 2024, Physical Review A.
DOI: 10.1103/PhysRevA.109.052425
This work was supported in part by the Department of Energy (DOE) Office of Science, Office of Nuclear Physics, InQubator for Quantum Simulation (IQuS) via the Quantum Horizons: QIS Research and Innovation for Nuclear Science; in part by the DOE QuantISED program through the “Intersections of QIS and Theoretical Particle Physics” theory consortium at Fermilab; and in part by the Department of Physics and the College of Arts and Sciences at the University of Washington.
Never miss a breakthrough: Join the SciTechDaily newsletter.
Follow us on Google and Google News.