T1 and thermally-excited quasiparticles effects on multi-mode transmons
ORAL
Abstract
In this study, we investigate the impact of thermally-excited quasiparticles on the performance degradation of multi-mode transmon qubits. Thermalization within the dilution refrigerator is a concern when utilizing non-superconducting cables for DC current distribution, which have a finite resistance at cryogenic temperature. Previous studies have shown that thermally excited states below a certain effect temperature unique to the device deviate from Maxwell-Boltzmann predictions, with a residual population of 0.1%. This residual quasiparticle population directly impacts the T1 time of qubits. We extend this analysis to multi-mode transmons, specifically modes A, B, and C, with frequencies of 4.09 GHz, 4.39 GHz, and 5.02 GHz, respectively. We predict the T1 degradation due to thermal quasiparticles and compare it with the empirical results. Our results show that the T1 times fall exponentially with increasing temperature, and we propose a methodology to mitigate the thermal effects. This includes optimizing the material properties and fridge setup to reduce thermal influx and maintain qubit performance within an optimal range. These findings are critical for the continued development of high-coherence multi-mode transmon qubits for scalable quantum computing.
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Publication: [1] Roy, T. (2018). Multimode superconducting circuits as building blocks for a programmable quantum processor (Doctoral dissertation, Tata Institute of Fundamental Research)<br><br>[2] Jin, X. Y., Kamal, A., Sears, A. P., Gudmundsen, T., Hover, D., Miloshi, J., Slattery, R., Yan, F., Yoder, J., Orlando, T. P., Gustavsson, S., & Oliver, W. D. (2015).. Physical Review Letters, 114(24).
Presenters
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Sophi Chen Song
University of California, Los Angeles
Authors
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Sophi Chen Song
University of California, Los Angeles
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Raphael Low
University of California Los Angeles, University of California, Los Angeles
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Murat Can Sarihan
Google Quantum AI, University of California, Los Angeles
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Jin Ho Kang
University of California Los Angeles
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Cody S Fan
University of California, Los Angeles
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Wei-che Hsu
University of California Los Angeles
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Qiyu Liu
University of California Los Angeles, University of California, Los Angeles
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Renjie Kang
University of California Los Angeles, University of California, Los Angeles
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Kangdi Yu
University of California Berkeley
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Madeline Taylor
Google Quantum AI, University of California, Los Angeles
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Ananyo Banerjee
University of California Los Angeles
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CheeWei Wong
University of California Los Angeles