Recommender system expedited quantum control optimization

Abstract

Quantum control optimization algorithms are routinely used to synthesize optimal quantum gates or to realize efficient quantum state transfers. The computational resource required for the optimization is an essential consideration in order to scale toward quantum control of larger registers. Here, we propose and demonstrate the use of a machine learning method, specifically the recommender system (RS), to deal with the challenge of enhancing computational efficiency. Given a sparse database of a set of products and their customer ratings, RS is used to efficiently predict unknown ratings. In the quantum control problem, each iteration of a numerical optimization algorithm typically involves evaluating a large number of parameters, such as gradients or fidelities, which can be tabulated as a rating matrix. We establish that RS can rapidly and accurately predict elements of such a sparse rating matrix. Using this approach, we expedite a gradient ascent based quantum control optimization, namely GRAPE, and demonstrate the faster construction of two-qubit CNOT gate in registers with up to 8 qubits. We also describe and implement the enhancement of the computational speed of a hybrid algorithm involving simulated annealing as well as gradient ascent. Moreover, the faster construction of three-qubit Toffoli gates further confirmed the applicability of RS in larger registers.