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Register a new userSymmetrised Characterisation of Noisy Quantum Processes
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A major goal of developing high-precision control of many-body quantum systems is to realise their potential as quantum computers. Probably the most significant obstacle in this direction is the problem of decoherence: the extreme fragility of quantum systems to environmental noise and other control limitations. The theory of fault-tolerant quantum error correction has shown that quantum computation is possible even in the presence of decoherence provided that the noise affecting the quantum system satisfies certain well-defined theoretical conditions. However, existing methods for noise characterisation have become intractable already for the systems that are controlled in todays labs. In this paper we introduce a technique based on symmetrisation that enables direct experimental characterisation of key properties of the decoherence affecting a multi-body quantum system. Our method reduces the number of experiments required by existing methods from exponential to polynomial in the number of subsystems. We demonstrate the application of this technique to the optimisation of control over nuclear spins in the solid state.
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Every quantum system is coupled to an environment. Such system-environment interaction leads to temporal correlation between quantum operations at different times, resulting in non-Markovian noise. In principle, a full characterisation of non-Markovian noise requires tomography of a multi-time processes matrix, which is both computationally and experimentally demanding. In this paper, we propose a more efficient solution. We employ machine learning models to estimate the amount of non-Markovianity, as quantified by an information-theoretic measure, with tomographically incomplete measurement. We test our model on a quantum optical experiment, and we are able to predict the non-Markovianity measure with $90%$ accuracy. Our experiment paves the way for efficient detection of non-Markovian noise appearing in large scale quantum computers.
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