Hybrid Tree Tensor Networks for Quantum Simulation
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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-01-01
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| Series: | PRX Quantum |
| Online Access: | http://doi.org/10.1103/PRXQuantum.6.010320 |
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| _version_ | 1832582490463666176 |
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| author | Julian Schuhmacher Marco Ballarin Alberto Baiardi Giuseppe Magnifico Francesco Tacchino Simone Montangero Ivano Tavernelli |
| author_facet | Julian Schuhmacher Marco Ballarin Alberto Baiardi Giuseppe Magnifico Francesco Tacchino Simone Montangero Ivano Tavernelli |
| author_sort | Julian Schuhmacher |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-03b3bbbe1cc34b7ca309d498ddb5084d |
| institution | Kabale University |
| issn | 2691-3399 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | PRX Quantum |
| spelling | doaj-art-03b3bbbe1cc34b7ca309d498ddb5084d2025-01-29T15:01:31ZengAmerican Physical SocietyPRX Quantum2691-33992025-01-016101032010.1103/PRXQuantum.6.010320Hybrid Tree Tensor Networks for Quantum SimulationJulian SchuhmacherMarco BallarinAlberto BaiardiGiuseppe MagnificoFrancesco TacchinoSimone MontangeroIvano TavernelliHybrid 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.http://doi.org/10.1103/PRXQuantum.6.010320 |
| spellingShingle | Julian Schuhmacher Marco Ballarin Alberto Baiardi Giuseppe Magnifico Francesco Tacchino Simone Montangero Ivano Tavernelli Hybrid Tree Tensor Networks for Quantum Simulation PRX Quantum |
| title | Hybrid Tree Tensor Networks for Quantum Simulation |
| title_full | Hybrid Tree Tensor Networks for Quantum Simulation |
| title_fullStr | Hybrid Tree Tensor Networks for Quantum Simulation |
| title_full_unstemmed | Hybrid Tree Tensor Networks for Quantum Simulation |
| title_short | Hybrid Tree Tensor Networks for Quantum Simulation |
| title_sort | hybrid tree tensor networks for quantum simulation |
| url | http://doi.org/10.1103/PRXQuantum.6.010320 |
| work_keys_str_mv | AT julianschuhmacher hybridtreetensornetworksforquantumsimulation AT marcoballarin hybridtreetensornetworksforquantumsimulation AT albertobaiardi hybridtreetensornetworksforquantumsimulation AT giuseppemagnifico hybridtreetensornetworksforquantumsimulation AT francescotacchino hybridtreetensornetworksforquantumsimulation AT simonemontangero hybridtreetensornetworksforquantumsimulation AT ivanotavernelli hybridtreetensornetworksforquantumsimulation |