Work

Tensor Network Enhanced Dynamic Multiproduct Formulas

Year: 2025

Type: article

Abstract: Tensor networks and quantum computation are two of the most powerful tools for the simulation of quantum many-body systems. Rather than viewing them as competing approaches, here we consider how these two methods can work in tandem. We introduce a novel algorithm that combines tensor networks and quantum computation to produce results that are more accurate than what could be achieved by either method used in isolation. Our algorithm is based on multiproduct formulas (MPFs)—a technique that linearly combines Trotter product formulas to reduce algorithmic error. It uses a quantum computer to calculate the expectation values and tensor networks to calculate the coefficients used in the linear combination. We present a detailed error analysis of the algorithm and demonstrate the full workflow on a one-dimensional quantum simulation problem on 50 qubits using two IBM quantum computers, and .

Source: PRX Quantum

DOI: 10.1103/8bzc-dlgt

Authors: Niall F. Robertson, Bibek Pokharel, Bryce Fuller, Eric Switzer, Oles Shtanko, Mirko Amico, Adam Byrne, Andrea D’Urbano, Salome Hayes-Shuptar, Albert Akhriev, Nathan Keenan, Sergey Bravyi, Sergiy Zhuk

Institutions: IBM (United States), IBM Research - Almaden, IBM Research - Thomas J. Watson Research Center, University of Central Florida, National Institute of Standards and Technology, Donostia International Physics Center

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,450

OpenAlex ID: W4410862182

FWCI: 2.062

Cites: 31

Cited by: 5

Related to: 10