Multi-Outcome Circuit Optimization for Enhanced Non-Gaussian State Generation

TL;DR

This paper introduces a multi-outcome optimization approach for photonic quantum circuits that increases the success rate of generating non-Gaussian states by leveraging multiple measurement patterns, enhancing quantum resource production.

Contribution

It proposes a novel multi-outcome optimization strategy that improves success probabilities in non-Gaussian state generation for photonic quantum computing.

Findings

Success probability increased by targeting multiple measurement outcomes.

Aggregation of degenerate outcomes enhances single-state production rate.

Applicable to various quantum states like GKP, cat states, and cubic phase states.

Abstract

Photonic quantum computing has gained significant interest in recent years due to its potential for scaling to large numbers of qubits. A critical requirement for fault-tolerant quantum computation is the reliable generation of non-Gaussian quantum states, typically achieved using Gaussian operations and photon-number-resolving detectors. However, the probabilistic nature of quantum measurement typically results in low success rates for state preparation. Conventionally, these circuits are optimized to herald a single specific target outcome, thereby disregarding the potential utility of alternative measurement patterns generated by the same physical setup. In this work, we propose and demonstrate a multi-outcome optimization strategy that increases the overall acceptance probability by allowing a single circuit to produce useful quantum states across several measurement patterns. To evaluate this approach, we apply the framework to the generation of Gottesman-Kitaev-Preskill core states, Schrodinger cat states, binomial codes, and cubic phase states using both two-mode and three-mode Gaussian circuits. We demonstrate that the success probability can be enhanced through two distinct mechanisms: first, by simultaneously targeting a diverse set of useful resource states, and second, by aggregating degenerate outcomes to maximize the production rate of a single target state.