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Abstract

The present disclosure relates to quantum computing methods and devices, wherein a computation corresponding to a certain desired unitary transformation of the Hilbert space of a quantum system, such as a set of qubits, is implemented by selective application of control pulses in order realize a continuous time quantum computation rather than by decomposing the unitary transformation into a sequence of gates taken from a fixed set of gates. In this way an effective Hamiltonian having an input matrix A encoded as an off-diagonal block connecting a first and a second subspace of the quantum system is then applied alternatingly with some standard Hamiltonian in order to obtain a time evolution corresponding to a given function f applied to the input matrix A. The required control pulses are optimized by means of classical compression techniques such as a deep learning neural network, in order to maximize, during the information loading phase of the computation, the amount of classical information loaded into the system, and, during the computational phase, the number of elementary continuous time quantum transformations, respectively.

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First Claim