Robust quantum gates using smooth pulses and physics-informed neural networks

Abstract

The presence of decoherence in quantum computers necessitates the suppression of noise. Dynamically corrected gates via specially designed control pulses offer a path forward, but hardware-specific experimental constraints can cause complications. Existing methods to obtain smooth pulses are either restricted to two-level systems, require an optimization over noise realizations or limited to piecewise-continuous pulse sequences. In this work, we present the first general method for obtaining truly smooth pulses that minimizes sensitivity to noise, eliminating the need for sampling over noise realizations and making assumptions regarding the underlying statistics of the experimental noise. We parametrize the Hamiltonian using a neural network, which allows the use of a large number of optimization parameters to adequately explore the functional control space. We demonstrate the capability of our approach by finding smooth shapes which suppress the effects of noise within the logical subspace as well as leakage out of that subspace.

References in corpus (10)

• Surface codes: Towards practical large-scale quantum computation
• Simple pulses for elimination of leakage in weakly nonlinear qubits
• Analytic control methods for high fidelity unitary operations in a weakly nonlinear oscillator
• Realization of high-fidelity CZ and ZZ-free iSWAP gates with a tunable coupler
• Analytically solvable driven time-dependent two-level quantum systems
• A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
• DiffEqFlux.jl - A Julia Library for Neural Differential Equations
• Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
• Robust quantum gates for stochastic time-varying noise
• Average Fidelity in n-Qubit systems

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• Reverse engineering of one-qubit filter functions with dynamical invariants
• Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
• Neural-network-designed three-qubit gates robust against charge noise and crosstalk in silicon
• Characterization of a driven two-level quantum system by Supervised Learning
• Nonadiabatic quantum control of quantum dot arrays with fixed exchange using Cartan decomposition
• Correcting noisy quantum gates with shortcuts to adiabaticity
• Variational quantum compiling for three-qubit gates design in quantum dots
• Optimizing EPR pulses for broadband excitation and refocusing
• An automated geometric space curve approach for designing dynamically corrected gates
• Fidelity Relations in an Array of Neutral Atom Qubits -- Experimental Validation of Control Noise
• Highly efficient nuclear population transfer through physics-informed neural networks
• The effects of alloy disorder on strongly-driven flopping mode qubits in Si/SiGe