Year: 2024
Type: preprint
Abstract: Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algorithm for efficiently learning a large set of non-commuting observables, using shallow circuits to rotate into measurement bases. Exploiting tensor network techniques to ensure polynomial scaling of classical resources, our algorithm outputs a set of shallow measurement circuits that approximately minimizes the sample complexity of estimating a given set of Pauli strings. We numerically demonstrate systematic improvement, in comparison with state-of-the-art techniques, for energy estimation of quantum chemistry benchmarks and verification of quantum many-body systems, and we observe DSS's performance consistently improves as one allows deeper measurement circuits. These results indicate that in addition to being an efficient, low-depth, stand-alone algorithm, DSS can also benefit many larger quantum algorithms requiring estimation of multiple non-commuting observables.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, Harvard University, European Commission, Austrian Science Fund, QuantERA, Defense Advanced Research Projects Agency, Institute for Theoretical Atomic, Molecular and Optical Physics, Harvard University, National Defense Science and Engineering Graduate, HORIZON EUROPE Framework Programme, Center for Ultracold Atoms, Massachusetts Institute of Technology
Awards: G1264789558, G1286236842, G1863003419, PROGRAMMABLE QUANTUM SIMULATORS FOR LATTICE GAUGE THEORIES AND GAUGE-GRAVITY CORRESPONDENCE, Programmable Atomic Large-scale Quantum Simulation 2 - SGA1, G2475912542, G3083819904, G3514304032, G42916606, G4495373907, G4856404060, G5206177008, G526230902, G5284946700, G5302217128, Physics and Applications of Quantum Nanophotonics Systems, G5989920273, G6695961427, G8959349008, G969889393, G980307288
OpenAlex ID: W4405901463