Fast Quantum Process Tomography via Riemannian Gradient Descent
<|reference_start|>Fast Quantum Process Tomography via Riemannian Gradient Descent: Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science and becomes especially challenging for high-dimensional complex structure problems. One specific issue is that of quantum process tomography, in which the goal is to retrieve the underlying quantum process based on a given set of measurement data. In this paper, we introduce a modified version of stochastic gradient descent on a Riemannian manifold that integrates recent advancements in numerical methods for Riemannian optimization. This approach inherently supports the physically driven constraints of a quantum process, takes advantage of state-of-the-art large-scale stochastic objective optimization, and has superior performance to traditional approaches such as maximum likelihood estimation and projected least squares. The data-driven approach enables accurate, order-of-magnitude faster results, and works with incomplete data. We demonstrate our approach on simulations of quantum processes and in hardware by characterizing an engineered process on quantum computers.<|reference_end|>
@article{volya2024fast,
    title={Fast Quantum Process Tomography via Riemannian Gradient Descent},
    author={Daniel Volya, Andrey Nikitin, Prabhat Mishra},
    journal={arXiv preprint arXiv:2404.18840},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18840},
    primaryClass={quant-ph cs.LG}
}