Transferable Lindblad Variational Ansatz for Accelerated Steady-State Preparation

Open quantum systems naturally relax toward steady states that underpin diverse nonequilibrium phenomena and quantum technologies. We independently control the coherent and dissipative constituents of a Lindblad generator to construct a variational product formula, termed the Lindblad variational ansatz (LVA), that accelerates steady-state preparation over natural relaxation through an optimized preparation path. The protocol consists of small-system learning, continuation to greater depth, and transfer to larger systems, followed when needed by reference-free post-training on quantum simulators. We demonstrate LVA in a bulk-dissipative Ising chain and a boundary-driven Heisenberg chain, where transferred protocols achieve matched preparation accuracies with fewer generator blocks than time-optimized first- and second-order Trotter formulas. The acceleration arises from coordinated endpoint preparation: earlier controls shape the dissipative-mode amplitudes so that the final dissipative step brings multiple components simultaneously close to their target values. Our results suggest a route to open-system quantum simulation that combines classical training on small systems with deployment and optional refinement on larger quantum devices.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-11-03
DOI
https://doi.org/10.48550/arxiv.2611.04919
Primary Topic
Quantum Computing Algorithms and Architecture
Type
preprint
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preprint

Transferable Lindblad Variational Ansatz for Accelerated Steady-State Preparation

Yun-Zhuo Fan, Dan-Bo Zhang
arXiv (Cornell University)
Quantum Computing Algorithms and Architecture
preprint

Transferable Lindblad Variational Ansatz for Accelerated Steady-State Preparation

Yun-Zhuo Fan, Dan-Bo Zhang
preprint en

Abstract

Open quantum systems naturally relax toward steady states that underpin diverse nonequilibrium phenomena and quantum technologies. We independently control the coherent and dissipative constituents of a Lindblad generator to construct a variational product formula, termed the Lindblad variational ansatz (LVA), that accelerates steady-state preparation over natural relaxation through an optimized preparation path. The protocol consists of small-system learning, continuation to greater depth, and transfer to larger systems, followed when needed by reference-free post-training on quantum simulators. We demonstrate LVA in a bulk-dissipative Ising chain and a boundary-driven Heisenberg chain, where transferred protocols achieve matched preparation accuracies with fewer generator blocks than time-optimized first- and second-order Trotter formulas. The acceleration arises from coordinated endpoint preparation: earlier controls shape the dissipative-mode amplitudes so that the final dissipative step brings multiple components simultaneously close to their target values. Our results suggest a route to open-system quantum simulation that combines classical training on small systems with deployment and optional refinement on larger quantum devices.

arXiv (Cornell University)
Quantum Computing Algorithms and Architecture
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