Quantum Template Resonance Optimization (QTRO) A Template-Driven Variational Strategy for Structured Convergence in the TFIM
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901755562819584 |
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| author | Quinto, Antonio |
| author_facet | Quinto, Antonio |
| contents | <p>We introduce Quantum Template Resonance Optimization (QTRO), a template-driven variational <br>optimization strategy designed to guide quantum circuits toward physically structured regions <br>of Hilbert space.</p> <p>Unlike standard Variational Quantum Eigensolvers (VQE), which directly minimize the <br>expectation value of the Hamiltonian, QTRO iteratively maximizes the fidelity of a variational <br>state with respect to a discrete set of physically motivated reference templates.</p> <p>We demonstrate QTRO on the transverse-field Ising model (TFIM) with periodic boundary <br>conditions, showing that QTRO consistently converges to the physically correct physical sector <br>in both field-dominated and coupling-dominated regimes, and can dramatically outperform <br>VQE in difficult energy landscapes.</p> <p>All results are obtained via classical simulation. QTRO can be used either as a standalone <br>heuristic optimizer or as a preconditioner for hybrid QTRO→VQE schemes.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18348923 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Quantum Template Resonance Optimization (QTRO) A Template-Driven Variational Strategy for Structured Convergence in the TFIM Quinto, Antonio quantum computing, variational algorithms, VQE, TFIM, quantum optimization, template-based optimization, quantum heuristics <p>We introduce Quantum Template Resonance Optimization (QTRO), a template-driven variational <br>optimization strategy designed to guide quantum circuits toward physically structured regions <br>of Hilbert space.</p> <p>Unlike standard Variational Quantum Eigensolvers (VQE), which directly minimize the <br>expectation value of the Hamiltonian, QTRO iteratively maximizes the fidelity of a variational <br>state with respect to a discrete set of physically motivated reference templates.</p> <p>We demonstrate QTRO on the transverse-field Ising model (TFIM) with periodic boundary <br>conditions, showing that QTRO consistently converges to the physically correct physical sector <br>in both field-dominated and coupling-dominated regimes, and can dramatically outperform <br>VQE in difficult energy landscapes.</p> <p>All results are obtained via classical simulation. QTRO can be used either as a standalone <br>heuristic optimizer or as a preconditioner for hybrid QTRO→VQE schemes.</p> |
| title | Quantum Template Resonance Optimization (QTRO) A Template-Driven Variational Strategy for Structured Convergence in the TFIM |
| topic | quantum computing, variational algorithms, VQE, TFIM, quantum optimization, template-based optimization, quantum heuristics |
| url | https://doi.org/10.5281/zenodo.18348923 |