Quantum Circuit Evolution on NISQ Devices

Abstract

Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empirical sampling distribution of the circuit is sufficiently close to a desired outcome. Numerical first-order methods are applied frequently to fit the parameters of the circuit, but most of the time, the circuit itself, that is, the actual composition of gates, is fixed. Methods for optimizing the circuit design jointly with the weights have been proposed, but empirical results are rather scarce. Here, we consider a simple evolutionary strategy that addresses the trade-off between finding appropriate circuit architectures and parameter tuning. We evaluate our method both via simulation and on actual quantum hardware. Our benchmark problems include the transverse field Ising Hamiltonian and the Sherrington-Kirkpatrick spin model. Despite the shortcomings of current noisy intermediate-scale quantum hardware, we find only a minor slowdown on actual quantum machines compared to simulations. Moreover, we investigate which mutation operations most significantly contribute to the optimization. The results provide intuition on how randomized search heuristics behave on actual quantum hardware and lay out a path for further refinement of evolutionary quantum gate circuits.

arXiv comment8 pages, 7 figures. To appear in the proceedings of IEEE Congress on Evolutionary Computation (CEC) 2022

References in corpus (14)

• Quantum Computing in the NISQ era and beyond
• A variational eigenvalue solver on a quantum processor
• Elementary gates for quantum computation
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• An adaptive variational algorithm for exact molecular simulations on a quantum computer
• Variational ansatz-based quantum simulation of imaginary time evolution
• From transistor to trapped-ion computers for quantum chemistry
• Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule
• Using models to improve optimizers for variational quantum algorithms
• A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
• Abrupt Transitions in Variational Quantum Circuit Training
• Natural Evolutionary Strategies for Variational Quantum Computation

Cited by in corpus (7)

• Quantum Deep Reinforcement Learning for Robot Navigation Tasks
• Efficient variational synthesis of quantum circuits with coherent multi-start optimization
• Representation of binary classification trees with binary features by quantum circuits
• Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction
• Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
• Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
• Quantum circuit evolutionary framework applied on set partitioning problem