Quantum gate learning in engineered qubit networks: Toffoli gate with always-on interactions

TL;DR

This paper introduces a machine learning-inspired method to encode quantum gates directly into the natural dynamics of engineered qubit networks without external control, enabling stable, high-fidelity quantum operations.

Contribution

It presents a novel optimization scheme to design unmodulated quantum networks that naturally perform complex gates like Toffoli, Fredkin, and remote logic operations.

Findings

Successfully engineered four-qubit networks implementing Toffoli and Fredkin gates

The proposed gates are stable against imperfections and suitable for fault-tolerant quantum computing

The gates operate quickly based on non-equilibrium dynamics

Abstract

We put forward a strategy to encode a quantum operation into the unmodulated dynamics of a quantum network without the need of external control pulses, measurements or active feedback. Our optimization scheme, inspired by supervised machine learning, consists in engineering the pairwise couplings between the network qubits so that the target quantum operation is encoded in the natural reduced dynamics of a network section. The efficacy of the proposed scheme is demonstrated by the finding of uncontrolled four-qubit networks that implement either the Toffoli gate, the Fredkin gate, or remote logic operations. The proposed Toffoli gate is stable against imperfections, has a high-fidelity for fault tolerant quantum computation, and is fast, being based on the non-equilibrium dynamics.