Genetic Algorithms for Error Mitigation in Quantum Measurement

Noisy Intermediate Scale Quantum (NISQ) devices are expected to demonstrate the real potential of quantum computing in solving hard problems. However, quantum noise that characterizes this kind of devices still remains an obstacle for their practical exploitation in real world scenarios. As a consequence, there is a strong emergence for error correction techniques aimed at making NISQ devices stable and fully operative. Unfortunately, current approaches for quantum error correction are prohibitive for NISQ devices because of the enormous multiplicative cost in resources that they require. For this reason, so-called quantum error mitigation methods are emerging as alternative approaches able to attenuate the quantum error as much as possible, without requiring a strong additional computational effort. Among the most error-prone operations, there is surely the quantum measurement. Conventionally, mitigation methods for quantum measurement error compute a so-called mitigation matrix capable of correcting results outputted by a quantum processor. In this paper, a new measurement error mitigation approach based on genetic algorithms is proposed to learn an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method is comparable with or better than a conventional algebraic approach in terms of the Hellinger fidelity.