Determinantal reinforcement learning
Takayuki Osogami, Rudy Raymond
AAAI 2019
Obtaining precise estimates of quantum observables is a crucial step of variational quantum algorithms. We consider the problem of estimating expectation values of quantum Hamiltonians, obtained on states prepared on a quantum computer. We propose a novel estimator for this task, which is locally optimised with knowledge of the Hamiltonian and a classical approximation to the underlying quantum state. Our estimator is based on the concept of classical shadows of a quantum state, and has the important property of not adding to the circuit depth for the state preparation. We test its performance numerically for molecular Hamiltonians of increasing size, finding a sizable reduction in variance with respect to current measurement protocols that do not increase circuit depths.
Takayuki Osogami, Rudy Raymond
AAAI 2019
Sergey Bravyi, Libor Caha, et al.
Physical Review Letters
Sergey Bravyi
Physical Review A - AMO
Sergey Bravyi, David Fattal, et al.
Journal of Mathematical Physics