Deep Circuit Compression for Quantum Dynamics via Tensor Networks

Dynamic quantum simulation is a leading application for achieving quantum advantage. However, high circuit depths remain a limiting factor on near-term quantum hardware. We present a compilation algorithm based on Matrix Product Operators for generating compressed circuits enabling real-time simulat...

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Main Authors: Joe Gibbs, Lukasz Cincio
Format: Article
Language:English
Published: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften 2025-07-01
Series:Quantum
Online Access:https://quantum-journal.org/papers/q-2025-07-09-1789/pdf/
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author Joe Gibbs
Lukasz Cincio
author_facet Joe Gibbs
Lukasz Cincio
author_sort Joe Gibbs
collection DOAJ
description Dynamic quantum simulation is a leading application for achieving quantum advantage. However, high circuit depths remain a limiting factor on near-term quantum hardware. We present a compilation algorithm based on Matrix Product Operators for generating compressed circuits enabling real-time simulation on digital quantum computers, that for a given depth are more accurate than all Trotterizations of the same depth. By the efficient use of environment tensors, the algorithm is scalable in depth far beyond prior work, and we present circuit compilations of up to 64 layers of $SU(4)$ gates. Surpassing only 1D circuits, our approach can flexibly target a particular quasi-2D gate topology. We demonstrate this by compiling a 52-qubit 2D Transverse-Field Ising propagator onto the IBM Heavy-Hex topology. For all circuit depths and widths tested, we produce circuits with smaller errors than all equivalent depth Trotter unitaries, corresponding to reductions in error by up to 4 orders of magnitude and circuit depth compressions with a factor of over 6.
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institution Kabale University
issn 2521-327X
language English
publishDate 2025-07-01
publisher Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
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spelling doaj-art-7bf9becd2b2346d1b812feddfc3370cd2025-08-20T03:50:01ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2025-07-019178910.22331/q-2025-07-09-178910.22331/q-2025-07-09-1789Deep Circuit Compression for Quantum Dynamics via Tensor NetworksJoe GibbsLukasz CincioDynamic quantum simulation is a leading application for achieving quantum advantage. However, high circuit depths remain a limiting factor on near-term quantum hardware. We present a compilation algorithm based on Matrix Product Operators for generating compressed circuits enabling real-time simulation on digital quantum computers, that for a given depth are more accurate than all Trotterizations of the same depth. By the efficient use of environment tensors, the algorithm is scalable in depth far beyond prior work, and we present circuit compilations of up to 64 layers of $SU(4)$ gates. Surpassing only 1D circuits, our approach can flexibly target a particular quasi-2D gate topology. We demonstrate this by compiling a 52-qubit 2D Transverse-Field Ising propagator onto the IBM Heavy-Hex topology. For all circuit depths and widths tested, we produce circuits with smaller errors than all equivalent depth Trotter unitaries, corresponding to reductions in error by up to 4 orders of magnitude and circuit depth compressions with a factor of over 6.https://quantum-journal.org/papers/q-2025-07-09-1789/pdf/
spellingShingle Joe Gibbs
Lukasz Cincio
Deep Circuit Compression for Quantum Dynamics via Tensor Networks
Quantum
title Deep Circuit Compression for Quantum Dynamics via Tensor Networks
title_full Deep Circuit Compression for Quantum Dynamics via Tensor Networks
title_fullStr Deep Circuit Compression for Quantum Dynamics via Tensor Networks
title_full_unstemmed Deep Circuit Compression for Quantum Dynamics via Tensor Networks
title_short Deep Circuit Compression for Quantum Dynamics via Tensor Networks
title_sort deep circuit compression for quantum dynamics via tensor networks
url https://quantum-journal.org/papers/q-2025-07-09-1789/pdf/
work_keys_str_mv AT joegibbs deepcircuitcompressionforquantumdynamicsviatensornetworks
AT lukaszcincio deepcircuitcompressionforquantumdynamicsviatensornetworks