Resisting light-induced quasiparticle poisoning in superconducting qubit arrays
ORAL
Abstract
Quantum error correction assumes that physical qubit errors are sufficiently uncorrelated in time and space. In superconducting qubit arrays, that assumption can be broken from high-energy impact events. These events produce phonons in the device substrate which in turn generate quasiparticles (QPs) in the superconducting metal. Subsequently, QP tunneling across the qubit's Josephson junction results in suppressed coherence times and long-lived correlated errors. By engineering the superconducting gap energy difference at the junction, these tunneling processes can be reduced, allowing the device to resist the effects of high-energy impact events. Here, we evaluate the efficacy of gap engineering by generating QPs in a controlled and on-demand manner using optical light with energies well above the superconducting gap. We demonstrate that strong gap engineering enables qubits to maintain coherence even when considerable densities of QPs are introduced. Furthermore, we show that small superconducting gap differences can be measured using qubit coherence spectroscopy and injected optical light. These results confirm that gap engineering is a highly effective technique to mitigate correlated errors from QP poisoning events in superconducting qubit arrays.
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Publication: M. McEwen, K. C. Miao, et al., "Resisting high-energy impact events through gap engineering in superconducting qubit arrays", arXiv:2402.15644 (2024)
Presenters
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Kevin C Miao
Google Quantum AI, Google LLC
Authors
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Kevin C Miao
Google Quantum AI, Google LLC
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Matthew J McEwen
Google Quantum AI, Google LLC
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Juan Atalaya
Google Quantum AI
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Jenna Bovaird
Google Quantum AI
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John Mark Kreikebaum
Google Quantum AI
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Anthony E Megrant
Google Quantum AI
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Yu Chen
Google Quantum AI, Google LLC
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Charles J Neill
Google Quantum AI, University of California, Santa Barbara
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Daniel T Sank
Google Quantum AI
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Michel H. Devoret
Yale University, Google Quantum AI
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Alexander M Opremcak
Google Quantum AI, Google LLC