US 12,493,811 B2
Variational analog quantum oracle learning
Nick Chancellor, Leesburg, VA (US); and Raouf Dridi, Leesburg, VA (US)
Assigned to Quantum Computing Inc., Leesburg, VA (US)
Filed by Quantum Computing Inc., Leesburg, VA (US)
Filed on May 16, 2022, as Appl. No. 17/745,752.
Prior Publication US 2023/0368063 A1, Nov. 16, 2023
Int. Cl. G06N 10/60 (2022.01); G06N 10/20 (2022.01)
CPC G06N 10/60 (2022.01) [G06N 10/20 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method for training a quantum machine to provide candidate parameters for optimization operations based on oracle outputs comprising:
configuring a quantum annealer based on a set of coupling parameters of a Hamiltonian;
performing quantum annealing using the configured quantum annealer to obtain annealer output samples, wherein each sample of the annealer output samples indicates state values of qubits of the quantum annealer; and
for each pairwise combination of indices of the annealer output samples:
determining a first subset of the annealer output samples, wherein a product of state values indexed by the pairwise combination of indices is positive for each sample of the first subset;
determining a second subset of the annealer output samples, wherein a product of state values indexed by the pairwise combination of indices is negative for each sample of the second subset;
providing the first subset of annealer output samples to an oracle executing on a classical computing system to obtain a first set of oracle outputs;
providing the second subset of annealer output samples to the oracle to obtain a second set of oracle outputs;
determining a first expectation based on the first set of oracle outputs and a second expectation based on the second set of oracle outputs;
determining a comparison value between the first and second expectations; and
updating a coupling parameter indexed by the pairwise combination of indices based on the comparison value and a learning rate parameter.