Bayesian Parameter Shift Rule enhances VQE gradient estimation

Researchers have introduced a Bayesian variant of the parameter shift rule (PSR) for variational quantum eigensolvers (VQEs). This new method utilizes Gaussian processes to estimate objective function gradients, offering flexibility in gradient estimation from arbitrary observation points and incorporating uncertainty information. The Bayesian PSR can accelerate optimization in stochastic gradient descent by reusing previous observations and reducing observation costs through a concept called gradient confident region (GradCoRe). Numerical experiments indicate that this approach significantly speeds up VQE optimization compared to existing methods. AI

IMPACT Introduces a novel technique for optimizing quantum algorithms, potentially accelerating research in quantum machine learning.

RANK_REASON This is a research paper detailing a new method for gradient estimation in variational quantum eigensolvers. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Kim Andrea Nicoli
• sequential minimal optimization
• stochastic gradient descent
• Gaussian processes

• paper
• other

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