Multimode Purcell Filter for Superconducting-Qubit Reset and Readout with Intrinsic Purcell Protection
Xu-Yang Gu, Da'er Feng, Zhen-Yu Peng, Gui-Han Liang, Yang He, Yongxi Xiao, Ming-Chuan Wang, Yu Yan, Bing-Jie Chen, Zheng-Yang Mei, Yi-Zhou Bu, Jia-Chi Zhang, Jia-Cheng Song, Cheng-Lin Deng, Yun-Hao Shi, Xiaohui Song, Dongning Zheng, Kai Xu, Zhongcheng Xiang, Heng Fan

TL;DR
This paper introduces a multi-mode Purcell filter in superconducting circuits that enables efficient qubit reset and readout without extra components, leveraging different resonator modes for distinct operations.
Contribution
It demonstrates a hardware-efficient multi-mode Purcell filter that achieves fast qubit reset and leakage reduction while maintaining qubit coherence, a novel approach for scalable quantum processors.
Findings
Unconditional reset achieved in 220 ns with less than 1% residual excitation.
Leakage reduction unit resets the second excited state in 62 ns with 6.1% residual |f⟩ population.
Intrinsic Purcell protection maintains qubit relaxation times despite direct coupling.
Abstract
Efficient qubit reset and leakage reduction are essential for scalable superconducting quantum computing, particularly in the context of quantum error correction. However, such operations often require additional on-chip components. Here, we propose and experimentally demonstrate a hardware-efficient approach to qubit reset and readout using a multi-mode Purcell filter in a superconducting quantum circuit. We exploit the inherent multi-mode structure of a coplanar waveguide resonator, using its fundamental and second-order modes for qubit reset and readout, respectively, thereby avoiding additional components. Implemented in a flip-chip architecture, our device achieves unconditional reset with residual excitation below 1\% in 220 ns, and a leakage reduction unit that selectively resets the second excited state within 62 ns with a residual population of 6.1\%, accounting for…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
