State preparation with parallel-sequential circuits

We introduce parallel-sequential (PS) circuits, a family of quantum circuit layouts that interpolate between brickwall and sequential circuits, which introduces control parameters governing a trade-off between the amount of entanglement and the maximum correlation range they can express. We provide numerical evidence that PS circuits can efficiently prepare many-body ground states in one dimension. On noisy devices, characterized through both idling errors and two-qubit gate errors, we show that in a wide parameter regime, PS circuits outperform brickwall, sequential, and the log-depth circuits from [Malz, Styliaris, Wei, Cirac, PRL 132, 040404 (2024)]. Additionally, we demonstrate that properly chosen noisy random PS circuits suppress error proliferation and, when employed as a variational ansatz, exhibit superior trainability.

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Publication Details

Journal
Quantum
Published
2026-04-21
DOI
https://doi.org/10.22331/q-2026-04-21-2079
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

State preparation with parallel-sequential circuits

Daniel Malz, Zhi-Yuan Wei
Quantum
Neural Networks and Applications
article

State preparation with parallel-sequential circuits

Daniel Malz, Zhi-Yuan Wei
article en

Abstract

We introduce parallel-sequential (PS) circuits, a family of quantum circuit layouts that interpolate between brickwall and sequential circuits, which introduces control parameters governing a trade-off between the amount of entanglement and the maximum correlation range they can express. We provide numerical evidence that PS circuits can efficiently prepare many-body ground states in one dimension. On noisy devices, characterized through both idling errors and two-qubit gate errors, we show that in a wide parameter regime, PS circuits outperform brickwall, sequential, and the log-depth circuits from [Malz, Styliaris, Wei, Cirac, PRL 132, 040404 (2024)]. Additionally, we demonstrate that properly chosen noisy random PS circuits suppress error proliferation and, when employed as a variational ansatz, exhibit superior trainability.

QuantumVol. 10
University of Copenhagen (DK), Joint Center for Quantum Information and Computer Science (US), Max Planck Institute of Quantum Optics (DE), Munich Center for Quantum Science and Technology (DE), Joint Quantum Institute (US), University of Maryland, College Park (US)
Novo Nordisk Fonden
Openalex Percentile: Top 99%
Neural Networks and Applications
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