Discovery of an exchange-only gate sequence for CNOT with record-low gate time using reinforcement learning

TL;DR

This paper uses reinforcement learning to discover exchange-only quantum gate sequences that significantly reduce the total gate time for CNOT and CZ gates, enhancing quantum computation fidelity.

Contribution

It introduces a reinforcement learning approach to optimize exchange-only quantum gate sequences, achieving record-low gate times for CNOT and CZ gates.

Findings

Record-low gate times for CNOT and CZ gates achieved.

Reinforcement learning effectively optimizes quantum gate sequences.

Improved gate fidelity potential due to shorter operation times.

Abstract

Exchange-only quantum computation is a version of spin-based quantum computation that entirely avoids the difficulty of controlling individual spins by a magnetic field and instead functions by sequences of exchange pulses. The challenge for exchange-only quantum computation is to find short sequences that generate the required logical quantum gates. A reduction of the total gate time of such synthesized quantum gates can help to minimize the effects of decoherence and control errors during the gate operation and thus increase the total gate fidelity. We apply reinforcement learning to the optimization of exchange-gate sequences realizing the CNOT and CZ two-qubit gates which lend themselves to the construction of universal gate sets for quantum computation. We obtain a significant improvement regarding the total gate time compared to previously known results.