Superfast maximum likelihood reconstruction for quantum tomography

TL;DR

This paper introduces a fast, reliable convex optimization algorithm for quantum state maximum likelihood reconstruction, significantly reducing computation time and improving accuracy in quantum tomography compared to traditional methods.

Contribution

The authors develop an accelerated projected-gradient method for quantum MLE that outperforms existing algorithms in speed and accuracy, challenging the notion that MLE is inherently slow.

Findings

8-qubit tomography completed in under a minute

Higher accuracy than previous methods

Refutes the claim that MLE is slow for quantum tomography

Abstract

Conventional methods for computing maximum-likelihood estimators (MLE) often converge slowly in practical situations, leading to a search for simplifying methods that rely on additional assumptions for their validity. In this work, we provide a fast and reliable algorithm for maximum likelihood reconstruction that avoids this slow convergence. Our method utilizes the state-of-the-art convex optimization scheme---an accelerated projected-gradient method---that allows one to accommodate the quantum nature of the problem in a different way than in the standard methods. We demonstrate the power of our approach by comparing its performance with other algorithms for n-qubit state tomography. In particular, an 8-qubit situation that purportedly took weeks of computation time in 2005 can now be completed in under a minute for a single set of data, with far higher accuracy than previously possible. This refutes the common claim that MLE reconstruction is slow, and reduces the need for alternative methods that often come with difficult-to-verify assumptions. In fact, recent methods assuming Gaussian statistics or relying on compressed sensing ideas are demonstrably inapplicable for the situation under consideration here. Our algorithm can be applied to general optimization problems over the quantum state space; the philosophy of projected gradients can further be utilized for optimization contexts with general constraints.