Efficient $n$-qubit entangling operations via a superconducting quantum router

Quantum Physics [Submitted on 16 Apr 2026] Efficient n-qubit entangling operations via a superconducting quantum router Xuntao Wu, Haoxiong Yan, Gustav Andersson, Alexander Anferov, Christopher R. Conner, Yash J. Joshi, Bayan Karimi, Amber M. King, Shiheng Li, Howard L. Malc, Jacob M. Miller, Harsh Mishra, Hong Qiao, Minseok Ryu, Jian Shi, Andrew N. Cleland Quantum algorithms on near-term quantum processors are typically executed using shallow quantum circuits composed of one- and two-qubit gates. However, as circuit depth and gate number increase, gate imperfections and qubit decoherence begin to dominate, limiting algorithmic complexity. An alternative approach is to explore gates involving more than two qubits. In previous work (X. Wu et al., Physical Review X 14, 041030 (2024)), we demonstrated a new superconducting qubit architecture with user-selectable two-qubit interactions via a reconfigurable router, used to connect pairs of qubits. Here, we leverage this novel architecture to realize programmable and efficient multi-qubit operations involving more than two qubits, resulting in faster preparation of multi-qubit entangled states with good fidelities. We also successfully apply model-free reinforcement learning to perform multi-qubit gates, including training a two-qubit controlled-Z gate as well as three-qubit controlled-SWAP and controlled-controlled-phase (Fredkin and Toffoli) gates. Higher nth-order gates may also be feasible, using our high-connectivity router design. This could provide a more efficient and higher-fidelity implementation of complex quantum algorithms and a more practical approach to quantum computation. Comments: 29 pages, 12 figures Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.15432 [quant-ph]   (or arXiv:2604.15432v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2604.15432 Focus to learn more Submission history From: Xuntao Wu [view email] [v1] Thu, 16 Apr 2026 18:00:04 UTC (4,641 KB) Access Paper: view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)