Calculus on parameterized quantum circuits

Abstract

Mitarai, Negoro, Kitagawa, and Fujii proposed a type of parameterized quantum circuits, for which they gave a way to estimate derivatives wrt the parameters using only changes in the values of the parameters, not in the circuit itself, i.e., no ancillas or controlled operations. Recently, Schuld et al. have extended the results, but they need to revert to ancillas and controlled operations for some cases. In this note, we extend the types of MiNKiF circuits for which derivatives can be computed without ancillas or controlled operations --- at the cost of a larger number of evaluation points. We also propose a "training" (i.e., optimizing the parameters) which takes advantage of our approach.

References in corpus (5)

• Quantum Computing in the NISQ era and beyond
• A variational eigenvalue solver on a quantum processor
• Quantum Circuit Learning
• Evaluating analytic gradients on quantum hardware
• Quantum advantage with shallow circuits

Cited by in corpus (9)

• General parameter-shift rules for quantum gradients
• Quantum Machine Learning: from physics to software engineering
• Generalized quantum circuit differentiation rules
• Variational quantum amplitude estimation
• Differentiable quantum computational chemistry with PennyLane
• Input Redundancy for Parameterized Quantum Circuits
• Quantum option pricing using Wick rotated imaginary time evolution
• Quantum Model-Discovery
• Information content of queries in training Parameterized Quantum Circuits