Quantum Circuit Evolution on NISQ Devices
This work provides insights into the behavior of randomized search heuristics on actual quantum hardware, which is important for researchers developing evolutionary quantum gate circuits.
This paper explores an evolutionary strategy to jointly optimize quantum circuit architecture and parameters for variational quantum circuits. The method was evaluated on benchmark problems like the transverse field Ising Hamiltonian and Sherrington-Kirkpatrick spin model, showing only a minor slowdown on noisy intermediate-scale quantum hardware compared to simulations.
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.