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Continuous Quantum Gate Sets and Pulse-Class Meta-Optimization
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DOI：10.1103/PRXQuantum.3.040311.png)
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Abstract
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Reduction of the circuit depth of quantum circuits is a crucial bottleneck to enabling quantum tech-nology. This depth is inversely proportional to the number of available quantum gates that have been synthesized. Moreover, quantum gate-synthesis and control problems exhibit a vast range of external parameter dependencies, both physical and application specific. In this paper, we address the possibil-ity of learning families of optimal-control pulses that 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 to produce continuous classes of gates. Our proposed method is tested on different experimen-tally relevant quantum gates and proves capable of producing high-fidelity pulses even in the presence of multiple variables or uncertain parameters with wide ranges.
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