Learning robust control for generating universal quantum gates

Abstract

Constructing a set of universal quantum gates is a fundamental task for quantum computation. The existence of noises, disturbances and fluctuations is unavoidable during the process of implementing quantum gates for most practical quantum systems. This paper employs a sampling-based learning method to find robust control pulses for generating a set of universal quantum gates. Numerical results show that the learned robust control fields are insensitive to disturbances, uncertainties and fluctuations during the process of realizing universal quantum gates.

13 pages, 8 figures

References in corpus (7)

• Superconducting Circuits and Quantum Information
• Dynamical decoupling and noise spectroscopy with a superconducting flux qubit
• Low-decoherence flux qubit
• Experimental noise filtering by quantum control
• Sliding mode control of quantum systems
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• Robust Learning Control Design for Quantum Unitary Transformations
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• Robust single-qubit gates by composite pulses in three-level systems
• Robust quantum gates for stochastic time-varying noise
• Robust quantum gates using smooth pulses and physics-informed neural networks
• Assessing three closed-loop learning algorithms by searching for high-quality quantum control pulses
• Pulse shape optimization for electron-positron production in rotating fields
• Robust quantum control with disorder-dressed evolution
• Learning Control of Quantum Systems
• Machine Learning for Estimation and Control of Quantum Systems
• Optimized continuous dynamical decoupling via differential geometry and machine learning
• Robust controllability of two-qubit Hamiltonian dynamics
• Engineering of the qubit initialization in an imperfect physical system
• Fidelity Relations in an Array of Neutral Atom Qubits -- Experimental Validation of Control Noise
• Geometrical versus time-series representation of data in quantum control learning