An entropy-based feedback rule, S = k * 2^H, is proposed to adapt the per-iteration shot count in VQAs, claiming about 50% shot savings over fixed-shot training while preserving final cost accuracy.
As the number of qubits increases, entropy also increases, often requiring a smaller constant value to maintain the desired shot count
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Distribution-Adaptive Dynamic Shot Optimization for Variational Quantum Algorithms
An entropy-based feedback rule, S = k * 2^H, is proposed to adapt the per-iteration shot count in VQAs, claiming about 50% shot savings over fixed-shot training while preserving final cost accuracy.