Neural-network quantum state tomography

By Giacomo Torlai, Guglielmo Mazzola, Juan Felipe Carrasquilla, Matthias Troyer, Roger G. Melko, Giuseppe Carleo. Nature Physics, 2017

901 citations recorded by the source corpus.

The experimental realization of increasingly complex synthetic quantum systems calls for the development of general theoretical methods to validate and fully exploit quantum resources. Quantum state tomography (QST) aims to reconstruct the full quantum state from simple measurements, and therefore provides a key tool to obtain reliable analytics1–3. However, exact brute-force approaches to QST place a high demand on computational resources, making them unfeasible for anything except small systems4,5. Here we show how machine learning techniques can be used to perform QST of highly entangled states with more than a hundred qubits, to a high degree of accuracy. We demonstrate that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements. This approach can benefit existing and future generations of devices ranging from quantum computers to ultracold-atom quantum simulators6–8. Unsupervised machine learning techniques can efficiently perform quantum state tomography of large, highly entangled states with high accuracy, and allow the reconstruction of many-body quantities from simple experimentally accessible measurements.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9ae6389cca412f3a7d1fd5acd6941c34c7799afe

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Citations in the corpus

Barren plateaus in variational quantum computing (2024, positive): “However, these architectures can still be useful in shallow circuits or when using smart initialization strategies.”
Deep autoregressive models for the efficient variational simulation of many-body quantum systems (2019, background): “These include for example ground-state search [7], quantum-state tomography [20], dynamics [7], and quantum circuits simulation [21].”
Quantum certification and benchmarking (2019, neutral): “Also, variational sets inspired by machine learning have been considered [92].”
The randomized measurement toolbox (2022, background): “matrix product states with polynomial bond dimension [47] or neural network states [48].”