HYBRID QUANTUM ALGORITHMS FOR QUANTUM MANY-BODY PHYSICS
Abstract
Quantum algorithms can be used to speed up calculations on quantum many-body problems. Unfortunately, known algorithms applied to quantum materials and quantum chemistry problems often suffer from high complexity. The resulting attempts to simulate these models have poor quantum computer resource and error scaling. We argue that certain intractable but high-impact models of superconductivity and the fractional quantum Hall effects will have a much better finite-size scaling than other models. We will construct hybrid quantum/classical algorithms based on Bayesian quantum phase estimation for solving long-standing quantum many-body models used in the fractional quantum Hall effect and in high temperature superconductivity. Our algorithms will yield exact energies to within specified tolerances on error-prone quantum devices to help benchmark approximate solutions to these problems. Our algorithms will lead to improved resource scaling thus setting the stage for high-impact applications with the lowest possible complexity to operate on quantum devices.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 17, 2022
- Source ID
- FA23862114081
Entities
People
- V W Scarola
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- Virginia Tech