Self-Guided Quantum State Learning for Mixed States

TL;DR

This paper introduces an adaptive quantum state tomography algorithm for mixed states that is efficient, noise-robust, and improves fidelity over existing methods, suitable for noisy quantum devices.

Contribution

It presents a novel adaptive learning algorithm based on stochastic approximation for efficient and noise-resilient quantum state tomography of mixed states.

Findings

Achieves $O(d^3)$ post-processing complexity.

Demonstrates robustness against measurement and channel noise.

Shows improved infidelity performance compared to existing algorithms.

Abstract

We provide an adaptive learning algorithm for tomography of general quantum states. Our proposal is based on the simultaneous perturbation stochastic approximation algorithm and is applicable on mixed qudit states. The salient features of our algorithm are efficient ($O \left( d^3 \right)$) post-processing in the dimension $d$ of the state, robustness against measurement and channel noise, and improved infidelity performance as compared to the contemporary adaptive state learning algorithms. A higher resilience against measurement noise makes our algorithm suitable for noisy intermediate-scale quantum applications.