Pareto-Front Engineering of Dynamical Sweet Spots in Superconducting Qubits
Zhen Yang, Shan Jin, Yajie Hao, Guangwei Deng, Xiu-Hao Deng, Re-Bing Wu, Xiaoting Wang

TL;DR
This paper develops a multi-objective optimization framework for superconducting qubits operating at dynamical sweet spots, significantly improving coherence times and gate performance by balancing relaxation and dephasing constraints.
Contribution
It introduces a fully parameterized, multi-objective periodic-flux modulation method that enhances dephasing times while quantifying fundamental limits on relaxation.
Findings
Enhanced dephasing time by a factor of 3-5
Maintained relaxation time in the hundred-microsecond range
Identified robust double-DSS regions for stable operation
Abstract
Operating superconducting qubits at dynamical sweet spots (DSSs) suppresses decoherence from low-frequency flux noise. A key open question is how long coherence can be extended under this strategy and what fundamental limits constrain it. Here we introduce a fully parameterized, multi-objective periodic-flux modulation framework that simultaneously optimizes energy relaxation and pure dephasing , thereby quantifying the tradeoff between them. For fluxonium qubits with realistic noise spectra, our method enhances by a factor of 3-5 compared with existing DSS strategies while maintaining in the hundred-microsecond range. We further prove that, although DSSs eliminate first-order sensitivity to low-frequency noise, relaxation rate cannot be reduced arbitrarily close to zero, establishing an upper bound on achievable . At the optimized working points, we…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum and electron transport phenomena · Physics of Superconductivity and Magnetism
