RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations

TL;DR

RhoDARTS introduces a quantum-aware, differentiable architecture search method that models quantum circuit optimization as the evolution of a mixed state, effectively handling noise and reducing simulation costs.

Contribution

It presents a novel quantum architecture search algorithm based on density matrix simulations, incorporating noise models and avoiding classical neural network sampling.

Findings

Comparable or better performance than existing QAS methods

Requires fewer quantum simulations during training

Enhanced robustness to noise

Abstract

Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers. However, choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task. Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem. Existing QAS approaches typically adapt classical neural architecture search techniques, training machine learning models to sample relevant circuits, but often overlook the inherent quantum nature of the circuits they produce. By reformulating QAS from a quantum perspective, we propose a sampling-free differentiable QAS algorithm that models the search process as the evolution of a quantum mixed state, which emerges from the search space of quantum circuits. The mixed state formulation also enables our method to incorporate generic noise models, for example the depolarizing channel, which cannot be modeled by state vector simulation. We validate our method by finding circuits for state initialization and Hamiltonian optimization tasks, namely the variational quantum eigensolver and the unweighted max-cut problems. We show our approach to be comparable to, if not outperform, existing QAS techniques while requiring significantly fewer quantum simulations during training, and also show improved robustness levels to noise.