Towards Efficient and Effective Optimization of Variational Quantum Algorithms through Parameter Transfer

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Synopsis

Optimizing parameters for variational quantum algorithms in the presence of noise is challenging due to the susceptibility of the algorithm to noise. In our approach, we address this challenge by utilizing the parameter transfer technique, which involves first identifying optimal parameters for a smaller instance of the problem and then transferring those parameters to the larger instance. By doing so, we can reduce the impact of noise on the optimization process and improve the accuracy of the optimizer. Our experimental results demonstrate that our technique effectively reduces the circuit size by 28% in terms of qubit counts and 38% in terms of circuit depth, leading to optimal points that are closer to the globally optimal points than the baseline. This approach has potential applications in quantum computing and can enable more accurate solutions for larger instances of problems.