RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations
This work addresses the challenge of efficient quantum circuit design for NISQ computers, offering a novel approach that could enhance the practicality of variational quantum algorithms, though it appears incremental as it builds on existing QAS methods.
The paper tackled the problem of automatically designing optimal quantum circuits for variational quantum algorithms by proposing a differentiable quantum architecture search method that models the search process as a quantum mixed state evolution, showing it performs comparably or better than existing techniques while requiring significantly fewer quantum simulations and improving noise robustness.
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.