Learning to measure


Many prominent quantum computing algorithms with applications in fields such as chemistry and materials science require a large number of measurements, which represents an important roadblock for future real-world use cases. In this talk I present a novel approach to tackle this problem through an adaptive measurement scheme, introduced in our recent paper PRX Quantum 2, 040342 (2021). We employ a generalised class of quantum measurements that can be iteratively adapted to minimize the number of times the target quantum state should be prepared and observed. We show its advantage by improving the efficiency of the variational quantum eigensolver in calculating ground-state energies of molecular Hamiltonians with extensive numerical simulations. As the algorithm proceeds, it reuses previous measurement outcomes to adjust its own settings and increase the accuracy of subsequent runs. We make the most out of every sample by combining all data produced while fine-tuning the measurement into a single, highly accurate estimate of the energy. Furthermore, all the measurement data contain complete information about the state: once collected, they can be reused to calculate any other property of the system without additional costs.

Feb 2, 2022 11:30 AM
Gemini Center on Quantum Computing