Report
Gradient-descent quantum process tomography by learning Kraus operators
| العنوان: | Gradient-descent quantum process tomography by learning Kraus operators |
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| المؤلفون: | Ahmed, Shahnawaz, Quijandría, Fernando, Kockum, Anton Frisk |
| المصدر: | Physical Review Letters 130, 150402 (2023) |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Physics (Other) Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Computer Science - Machine Learning, Physics - Data Analysis, Statistics and Probability |
| الوصف: | We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems by learning a process representation using Kraus operators. The Kraus form ensures that the reconstructed process is completely positive. To make the process trace-preserving, we use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. Our ansatz uses a few Kraus operators to avoid direct estimation of large process matrices, e.g., the Choi matrix, for low-rank quantum processes. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods. Similar to CS (but unlike PLS), GD-QPT can reconstruct a process from just a small number of random measurements, and similar to PLS (but unlike CS) it also works for larger system sizes, up to at least five qubits. We envisage that the data-driven approach of GD-QPT can become a practical tool that greatly reduces the cost and computational effort for QPT in intermediate-scale quantum systems. Comment: 10 pages, 5 figures, Code and data available at https://github.com/quantshah/gd-qpt |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1103/PhysRevLett.130.150402 |
| URL الوصول: | http://arxiv.org/abs/2208.00812 |
| رقم الانضمام: | edsarx.2208.00812 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1103/PhysRevLett.130.150402 |
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