aiqbench analysis
This research introduces ShadowNet, a hybrid neural network architecture that merges classical shadows with convolutional and attention mechanisms to overcome data collection bottlenecks in large-scale quantum system learning. By establishing unified dataset construction rules, the model efficiently performs critical tasks like quantum state tomography on systems up to 60 qubits while leveraging prior knowledge for improved subsequent performance. The study validates a promising data-centric paradigm that significantly reduces computational demands without sacrificing fidelity in understanding complex quantum dynamics.
ShadowNet for Data-Centric Quantum System Learning
arXiv:2308.11290v2 Announce Type: replace-cross Abstract: Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime through neural network protocols and classical shadows, while both methods have limitations: the former suffers from incompatible dataset construction rules, resulting in substantial computational demands for data collection when addressing different tasks; the latter lacks the ability to distill knowledge from prior data to enhance subsequent learning endeavors. In this study, we propose a data-centric learning paradigm combining the strengths of these two approaches to advance quantum system learning (QSL). Central to our paradigm lies a unified dataset construction rule, achieved by classical shadows along with other easily obtainable information of quantum systems. To illustrate our approach, we present ShadowNet, implemented under both convolutional and attention mechanisms, to efficiently and faithfully tackle two pivotal QSL tasks: quantum state tomography (QST) and direct fidelity estimation (DFE). Numerical simulations on QST and DFE up to 60 qubits validate the efficacy of our proposal, showcasing how ShadowNet advances classical shadows with limited state copies, and highlighting how the varied neural networks impact the performance. Our work underscores the immense potential of a data-centric approach in comprehending novel and large quantum systems.
Abstract:Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime through neural network protocols and classical shadows, while both methods have limitations: the former suffers from incompatible dataset construction rules, resulting in substantial computational demands for data collection when addressing different tasks; the latter lacks the ability to distill knowledge from prior data to enhance subsequent learning endeavors. In this study, we propose a data-centric learning paradigm combining the strengths of these two approaches to advance quantum system learning (QSL). Central to our paradigm lies a unified dataset construction rule, achieved by classical shadows along with other easily obtainable information of quantum systems. To illustrate our approach, we present ShadowNet, implemented under both convolutional and attention mechanisms, to efficiently and faithfully tackle two pivotal QSL tasks: quantum state tomography (QST) and direct fidelity estimation (DFE). Numerical simulations on QST and DFE up to 60 qubits validate the efficacy of our proposal, showcasing how ShadowNet advances classical shadows with limited state copies, and highlighting how the varied neural networks impact the performance. Our work underscores the immense potential of a data-centric approach in comprehending novel and large quantum systems.
Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 pages. 12 Figures
Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2308.11290 [quant-ph]
(or arXiv:2308.11290v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2308.11290 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 312 words
