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

TL;DR

This paper demonstrates a superconducting quantum router enabling efficient multi-qubit entangling operations, including complex gates, which could improve quantum algorithm implementation on near-term devices.

Contribution

It introduces a reconfigurable superconducting qubit architecture that allows programmable multi-qubit gates and applies reinforcement learning for gate optimization.

Findings

Faster preparation of multi-qubit entangled states with high fidelity.

Successful implementation of multi-qubit gates like controlled-SWAP and Toffoli.

Potential for higher-order gates using the high-connectivity router design.

Abstract

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 $n$th-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.