تقرير
Fast Quantum Process Tomography via Riemannian Gradient Descent
| العنوان: | Fast Quantum Process Tomography via Riemannian Gradient Descent |
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| المؤلفون: | Volya, Daniel, Nikitin, Andrey, Mishra, Prabhat |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.18840 |
| رقم الأكسشن: | edsarx.2404.18840 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |