102 Cardwell Hall
We describe a systematic method, using machine learning, to "program" a large-scale quantum computer. Current algorithmic approaches use a "building block" strategy, in which a procedure is formulated as a sequence of steps from a universal set, e.g., a sequence of CNOT, Hadamard, and phase shift gates. This assumes we even have such an algorithm; often, we do not. Using quantum learning enables us to perform computations without knowing the algorithm, and without breaking it down into its building blocks, thus eliminating a difficult step and potentially increasing efficiency by simplifying and reducing unnecessary complexity. Moreover, we demonstrate robustness of quantum learning to noise and to decoherence.