Quantum State Preparation via Schmidt Spectrum Optimisation

• URL: http://arxiv.org/abs/2512.20537v1
• Date: Tue, 23 Dec 2025 17:27:32 GMT
• Title: Quantum State Preparation via Schmidt Spectrum Optimisation
• Authors: Josh Green, Joshua Snow, Jingbo B Wang,
• Abstract summary: We introduce an efficient algorithm for the systematic design of shallow-depth quantum circuits.<n>The proposed method leverages Schmidt spectrum optimization (SSO) to minimize circuit depth.<n>We demonstrate state-of-the-art shallow-depth performance, improving accuracy by up to an order of magnitude over existing methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce an efficient algorithm for the systematic design of shallow-depth quantum circuits capable of preparing many-body quantum states represented as Matrix Product States (MPS). The proposed method leverages Schmidt spectrum optimization (SSO) to minimize circuit depth while preserving the entanglement structure inherent to MPS representations, thereby enabling scalable state preparation on near-term quantum hardware. The core idea is to \textit{disentangle} the target MPS using a sequence of optimised local unitaries, and then reverse this process to obtain a state preparation circuit. Specifically, we define a loss function directly on the Schmidt spectra of intermediate states and use automatic differentiation to optimise each circuit layer so as to systematically reduce entanglement entropy. Once a disentangling sequence has been learned, we take the adjoints of the optimised unitaries to obtain a shallow-depth circuit that approximately reconstructs the target MPS from the computational all-zero state. We benchmark SSO across a range of MPS approximations to the ground states of local Hamiltonians and demonstrate state-of-the-art shallow-depth performance, improving accuracy by up to an order of magnitude over existing methods. Finally, we provide numerical evidence that SSO mitigates the adverse time-complexity scaling observed in previous disentangling-based approaches.

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