Work

Synergy between noisy quantum computers and scalable classical deep learning for quantum error mitigation

Year: 2024

Type: article

Abstract: We investigate the potential of combining the computational power of noisy quantum computers and of classical scalable convolutional neural networks (CNNs). The goal is to accurately predict exact expectation values of parameterized quantum circuits representing the Trotter-decomposed dynamics of quantum Ising models. By incorporating (simulated) noisy expectation values alongside circuit structure information, our CNNs effectively capture the underlying relationships between circuit architecture and output behaviour, enabling, via transfer learning, also predictions for circuits with more qubits than those included in the training set. Notably, thanks to the quantum information, our CNNs succeed even when supervised learning based only on classical descriptors fails. Furthermore, they outperform a popular error mitigation scheme, namely, zero-noise extrapolation, demonstrating that the synergy between quantum and classical computational tools leads to higher accuracy compared with quantum-only or classical-only approaches. By tuning the noise strength, we explore the crossover from a computationally powerful classical CNN assisted by quantum noisy data, towards rather precise quantum computations, further error-mitigated via classical deep learning.

Source: EPJ Quantum Technology

DOI: 10.1140/epjqt/s40507-024-00256-8

Authors: Simone Cantori, Andrea Mari, David Vitali, Sebastiano Pilati

Institutions: Università di Camerino, Istituto Nazionale di Fisica Nucleare, Sezione di Perugia

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,990

Funders: European High Performance Computing Joint Undertaking, Ministero dell'Università e della Ricerca