Hybrid Tree Tensor Networks for Quantum Simulation
Year: 2025
Type: article
Abstract: Hybrid tensor networks (hTNs) offer a promising solution for encoding variational quantum states beyond the capabilities of efficient classical methods or noisy quantum computers alone. However, their practical usefulness and many operational aspects of hTN-based algorithms, like the optimization of hTNs, the generalization of standard contraction rules to an hybrid setting, and the design of application-oriented architectures have not been thoroughly investigated yet. In this work, we introduce a novel algorithm to perform ground-state optimizations with hybrid tree tensor networks (hTTNs), discussing its advantages and roadblocks, and identifying a set of promising applications. We benchmark our approach on two paradigmatic models, namely the Ising model at the critical point and the Toric-code Hamiltonian. In both cases, we successfully demonstrate that hTTNs can improve upon classical equivalents with equal bond dimension in the classical part.
Source: PRX Quantum
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,450
Funders: National Science Foundation, International Council of Shopping Centers Educational Foundation, International Business Machines Corporation, Dipartimenti di Eccellenza, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials
OpenAlex ID: W4406958699