WEPRO: Weight Prediction for Efficient Optimization of Hybrid Quantum-Classical Algorithms. (arXiv:2307.12449v2 [quant-ph] UPDATED)

The exponential run time of quantum simulators on classical machines and long
queue depths and high costs of real quantum devices present significant
challenges in the effective training of Variational Quantum Algorithms (VQAs)
like Quantum Neural Networks (QNNs), Variational Quantum Eigensolver (VQE) and
Quantum Approximate Optimization Algorithm (QAOA). To address these
limitations, we propose a new approach, WEPRO (Weight Prediction), which
accelerates the convergence of VQAs by exploiting regular trends in the
parameter weights. We introduce two techniques for optimal …

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