Bayesian inference with engineered likelihood functions for robust amplitude estimation

The number of measurements demanded by hybrid quantum-classical algorithms is prohibitively high for many problems of practical value. Quantum algorithms that reduce this cost (e.g. quantum amplitude and phase estimation) require error rates that are too low for near-term implementation. Here we propose methods that take advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. Our scheme derives inspiration from quantum metrology, phase estimation, and the "alpha-VQE" proposal, arriving at a general formulation that is robust to error and does not need ancilla qubits. The central object of this method is what we call the "engineered likelihood function" (ELF), used for carrying out Bayesian inference. In this talk we show how the ELF formalism enhances the information gain rate in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond. Similar to VQE, we expect small-scale implementations to be realizable on today's quantum devices.