Year: 2026
Type: preprint
Abstract: Quantum neural networks are a prominent model of quantum machine learning. Their training consists in the minimization of a given loss function over a parametrized family of quantum circuits, mathematically described by unitary operators, or, more generally, completely positive linear maps. In this work, we extend the notion of quantum neural network, using random sampling and classical data processing to enlarge the optimization space in a way that includes linear combinations of completely positive maps. Our extended model, called virtual quantum neural networks, leverages its enlarged optimization space to achieve increased expressivity and improved noise robustness. These benefits are illustrated in three representative tasks: quantum error mitigation, binary classification, and estimation of ground-state energies. Overall, virtual quantum neural networks offer a flexible learning paradigm that expands the space of achievable computations and strengthens the applications of near-term quantum hardware.
Source: arXiv (Cornell University)
Institutions: University of Hong Kong, Fudan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, Science and Technology Commission of Shanghai Municipality, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, CAS Key Laboratory of Quantum Information
OpenAlex ID: W7212257277