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Benchmarking Modular Optimization Strategies for Parameterized Quantum Circuits
Training parameterized quantum circuits requires balancing optimization progress with limited evaluation budgets and the statistical uncertainty of near-term quantum hardware. We present a modular benchmarking framework that separates quantum search-direction estimation from classical parameter-upda
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