On the Experimental Feasibility of Quantum State Reconstruction via Machine Learning
| Autor: | Ryan T. Glasser, Brian T. Kirby, Sanjaya Lohani, Thomas A. Searles |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer science FOS: Physical sciences Inference Machine learning computer.software_genre IBM Q Machine Learning (cs.LG) Matrix decomposition Quantum state Atomic physics. Constitution and properties of matter Materials of engineering and construction. Mechanics of materials Quantum computer Quantum Physics Noise measurement business.industry Artificial Intelligence (cs.AI) machine learning Qubit TA401-492 Artificial intelligence Tomography State (computer science) Quantum Physics (quant-ph) business computer quantum tomography QC170-197 |
| Zdroj: | IEEE Transactions on Quantum Engineering, Vol 2, Pp 1-10 (2021) |
| ISSN: | 2689-1808 |
| Popis: | We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of inference and training, for systems of up to four qubits when constrained to pure states. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state reconstruction method on an IBM Q quantum computer, and compare against both unconstrained and constrained MLE state reconstruction. 9 pages |
| Databáze: | OpenAIRE |
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