A two-step method minimizes entanglement entropy of target states before using matrix product state representations to achieve high-accuracy quantum state preparation on NISQ devices.
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The paper defines QNN expressivity as the effective rank of the Fisher information matrix and shows numerically that this rank can reach its maximum 4^n-1 when data, measurement, and circuit are jointly optimized, then uses the rank as a reward for automated circuit design.
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Minimizing entanglement entropy for enhanced quantum state preparation
A two-step method minimizes entanglement entropy of target states before using matrix product state representations to achieve high-accuracy quantum state preparation on NISQ devices.
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Learning to Maximize Quantum Neural Network Expressivity via Effective Rank
The paper defines QNN expressivity as the effective rank of the Fisher information matrix and shows numerically that this rank can reach its maximum 4^n-1 when data, measurement, and circuit are jointly optimized, then uses the rank as a reward for automated circuit design.