Quantum Natural Stochastic Pairwise Coordinate Descent
This work addresses the computational intensity and high sample complexity of quantum natural gradient descent for researchers in quantum computing, offering an incremental improvement.
The paper tackles the problem of sub-optimal convergence in variational quantum algorithms by developing a quantum optimization algorithm that uses a novel quantum information metric and an unbiased estimator from single-shot measurements, resulting in better sample complexity and faster convergence compared to state-of-the-art approaches.
Variational quantum algorithms, optimized using gradient-based methods, often exhibit sub-optimal convergence performance due to their dependence on Euclidean geometry. Quantum natural gradient descent (QNGD) is a more efficient method that incorporates the geometry of the state space via a quantum information metric. However, QNGD is computationally intensive and suffers from high sample complexity. In this work, we formulate a novel quantum information metric and construct an unbiased estimator for this metric using single-shot measurements. We develop a quantum optimization algorithm that leverages the geometry of the state space via this estimator while avoiding full-state tomography, as in conventional techniques. We provide the convergence analysis of the algorithm under mild conditions. Furthermore, we provide experimental results that demonstrate the better sample complexity and faster convergence of our algorithm compared to the state-of-the-art approaches. Our results illustrate the algorithm's ability to avoid saddle points and local minima.