Fast and noise-aware machine learning variational quantum eigensolver optimiser

Abstract The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for preparing ground states in the current era of noisy devices. The classical component of the algorithm requires a large number of measurements on intermediate parameter values that are typically discarded. However, intermediate steps across many calculations can contain valuable information about the relationship between the quantum circuit parameters, resultant measurements, and noise specific to the device. In this work, we use supervised machine learning on the intermediate parameter and measurement data to predict optimal final parameters. Our technique optimises parameters leading to chemically accurate ground state energies much faster than conventional techniques. It requires significantly fewer iterations and simultaneously shows resilience to coherent errors if trained on noisy devices. We demonstrate this technique on IBM quantum devices by predicting ground state energies of H2 for one and two qubits; H3 for three qubits; and HeH + for four qubits where it finds optimal angles using only modeled data for training. Our technique uses data that is already generated in VQE runs and requires no quantum overhead.