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Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning

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Document pages: 7 pages

Abstract: In this paper, we present a machine learning framework to designhigh-fidelity multi-qubit gates for quantum processors based on quantum dots insilicon, with qubits encoded in the spin of single electrons. In this hardwarearchitecture, the control landscape is vast and complex, so we use the deepreinforcement learning method to design optimal control pulses to achieve highfidelity multi-qubit gates. In our learning model, a simulator models thephysical system of quantum dots and performs the time evolution of the system,and a deep neural network serves as the function approximator to learn thecontrol policy. We evolve the Hamiltonian in the full state-space of thesystem, and enforce realistic constraints to ensure experimental feasibility.

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