Model-Based Optimization of Superconducting Qubit Readout

被引：4

作者：

Bengtsson, Andreas ^{[1
]}

Opremcak, Alex ^{[1
]}

Khezri, Mostafa ^{[1
]}

Sank, Daniel ^{[1
]}

Bourassa, Alexandre ^{[1
]}

Satzinger, Kevin J. ^{[1
]}

Hong, Sabrina ^{[1
]}

Erickson, Catherine ^{[1
]}

Lester, Brian J. ^{[1
]}

Miao, Kevin C. ^{[1
]}

Korotkov, Alexander N. ^{[1
,2
]}

Kelly, Julian ^{[1
]}

Chen, Zijun ^{[1
]}

V. Klimov, Paul ^{[1
]}

机构：

[1] Google Quantum AI, Santa Barbara, CA 93111 USA

[2] Univ Calif Riverside, Dept Elect & Comp Engn, Riverside, CA 92521 USA

来源：

PHYSICAL REVIEW LETTERS | 2024年 / 132卷 / 10期

关键词：

D O I：

10.1103/PhysRevLett.132.100603

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Measurement is an essential component of quantum algorithms, and for superconducting qubits it is often the most error prone. Here, we demonstrate model-based readout optimization achieving low measurement errors while avoiding detrimental side effects. For simultaneous and midcircuit measurements across 17 qubits, we observe 1.5% error per qubit with a 500 ns end-to-end duration and minimal excess reset error from residual resonator photons. We also suppress measurement-induced state transitions achieving a leakage rate limited by natural heating. This technique can scale to hundreds of qubits and be used to enhance the performance of error-correcting codes and near-term applications.

引用

页数：6

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