Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule

  title={Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule},
  author={L. Banchi and G. Crooks},
Hybrid quantum-classical optimization algorithms represent one of the most promising application for near-term quantum computers. In these algorithms the goal is to optimize an observable quantity with respect to some classical parameters, using feedback from measurements performed on the quantum device. Here we study the problem of estimating the gradient of the function to be optimized directly from quantum measurements, generalizing and simplifying some approaches present in the literature… Expand

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DOI: 10.22331/q-2020- 05-25-269
  • Quantum natural gradient. Quantum,
  • 2020
Stochastic gradient descent for hybrid quantumclassical optimization
  • 2020
  • Rev. A 99, 032331
  • 2019
  • Rev. A 98, 032309
  • 2018
npj Quantum Inf
  • 2, 16019
  • 2016
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  • 2020