Continuous quantum gate sets and pulse class meta-optimization

Abstract

Reducing the circuit depth of quantum circuits is a crucial bottleneck to enabling quantum technology. This depth is inversely proportional to the number of available quantum gates that have been synthesised. Moreover, quantum gate synthesis and control problems exhibit a vast range of external parameter dependencies, both physical and application-specific. In this article we address the possibility of learning families of optimal control pulses which depend adaptively on various parameters, in order to obtain a global optimal mapping from the space of potential parameter values to the control space, and hence continuous classes of gates. Our proposed method is tested on different experimentally relevant quantum gates and proves capable of producing high-fidelity pulses even in presence of multiple variable or uncertain parameters with wide ranges.

20 pages, 9 figures, published on PRX Quantum on the 25th of October 2022

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Cited by in corpus (10)

• Reinforcement learning pulses for transmon qubit entangling gates
• Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
• Gate-set evaluation metrics for closed-loop optimal control on nitrogen-vacancy center ensembles in diamond
• Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
• Universal pulses for superconducting qudit ladder gates
• Time-Optimal Quantum Driving by Variational Circuit Learning
• Efficient control pulses for continuous quantum gate families through coordinated re-optimization
• Pulse family optimization for parametrized quantum gates using spectral clustering
• Exponentiation of Parametric Hamiltonians via Unitary interpolation
• Using optimal control to guide neural-network interpolation of continuously-parameterized gates