Prediction and real-time compensation of qubit decoherence via machine-learning

Abstract

The wide-ranging adoption of quantum technologies requires practical, high-performance advances in our ability to maintain quantum coherence while facing the challenge of state collapse under measurement. Here we use techniques from control theory and machine learning to predict the future evolution of a qubit's state; we deploy this information to suppress stochastic, semiclassical decoherence, even when access to measurements is limited. First, we implement a time-division-multiplexed approach, interleaving measurement periods with periods of unsupervised but stabilised operation during which qubits are available, for e.g. quantum information experiments. Second, we employ predictive feedback during sequential but time delayed measurements to reduce the Dick effect as encountered in passive frequency standards. Both experiments demonstrate significant improvements in qubit phase stability over "traditional" measurement-based feedback approaches by exploiting time domain correlations in the noise processes. This technique requires no additional hardware and is applicable to all two-level quantum systems where projective measurements are possible.

Additional results presented. Related work available from http://www.physics.usyd.edu.au/~mbiercuk/Publications.html

References in corpus (14)

• Quantum feedback control of a superconducting qubit: Persistent Rabi oscillations
• Coherent quantum LQG control
• Measurement and control of a mechanical oscillator at its thermal decoherence rate
• Adaptive Control: Algorithms, Analysis and Applications
• Enhancing the Coherence of a Spin Qubit by Operating it as a Feedback Loop That Controls its Nuclear Spin Bath
• Suppressing qubit dephasing using real-time Hamiltonian estimation
• Experimental noise filtering by quantum control
• Quantum Feedback Control of Atomic Motion in an Optical Cavity
• Prediction and retrodiction for a continuously monitored superconducting qubit
• Optimal state estimation for cavity optomechanical systems
• Robustness of composite pulses to time-dependent control noise
• Experimental bath engineering for quantitative studies of quantum control
• Quantum filter reduction for measurement-feedback control via unsupervised manifold learning
• Analytically exploiting noise correlations inside the feedback loop to improve locked-oscillator performance

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