Tensor Network Enhanced Dynamic Multiproduct Formulas

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 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mn>50</a:mn></a:math> qubits using two IBM quantum computers, and .

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Publication Details

Journal
PRX Quantum
Published
2025-05-29
DOI
https://doi.org/10.1103/8bzc-dlgt
Citations
4
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
9.08
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Tensor Network Enhanced Dynamic Multiproduct Formulas

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PRX Quantum
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Tensor Network Enhanced Dynamic Multiproduct Formulas

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article en
4 citations

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 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mn>50</a:mn></a:math> qubits using two IBM quantum computers, and .

PRX QuantumVol. 6(2)
University of Central Florida (US), National Institute of Standards and Technology (US), IBM Research - Almaden (US), IBM Research - Thomas J. Watson Research Center (US), National Institute of Standards (EG), IBM Research - Ireland (IE), Donostia International Physics Center (ES)
Openalex Percentile: Top 2%
Parallel Computing and Optimization Techniques
9.08
DOIOpen Access PDFOpenAlex
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