Variational learning of integrated quantum photonic circuits

TL;DR

This paper introduces a variational learning method for designing integrated quantum photonic circuits that directly incorporates post-selection and elementary elements, enabling real-time optimization and successful quantum stochastic simulation.

Contribution

It presents a novel variational approach that treats complex photonic circuits as single nonlinear operators, overcoming challenges of traditional circuit decomposition.

Findings

Achieved improved success rate for a single ancilla CNOT gate.

First demonstration of quantum stochastic simulation using integrated photonics.

Real-time parameter optimization of photonic chips.

Abstract

Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, we present a variational learning approach for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic elements into the training process. The complicated circuit is treated as a single nonlinear logical operator, and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control, we adjust and optimize the internal parameters of the chip in real time for task-specific cost functions. We utilize a simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate to illustrate how our proposed approach works, and then apply the approach in the first demonstration of quantum stochastic simulation using integrated photonics.