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| Formato: | Preprint |
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2026
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| Acceso en línea: | https://arxiv.org/abs/2604.26861 |
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| author | Cadavid, Alejandro Gomez Nikačević, Pavle Chandarana, Pranav Romero, Sebastián V. Solano, Enrique Hegade, Narendra N. Lopez-Ruiz, Miguel Angel Girotto, Claudio Linn, Hanna Doga, Hakan Epifanovsky, Evgeny Barkoutsos, Panagiotis Kl. Kaushik, Ananth Roetteler, Martin |
| author_facet | Cadavid, Alejandro Gomez Nikačević, Pavle Chandarana, Pranav Romero, Sebastián V. Solano, Enrique Hegade, Narendra N. Lopez-Ruiz, Miguel Angel Girotto, Claudio Linn, Hanna Doga, Hakan Epifanovsky, Evgeny Barkoutsos, Panagiotis Kl. Kaushik, Ananth Roetteler, Martin |
| contents | We report the largest trapped-ion hardware demonstration of lattice protein-folding optimization to date, using bias-field digitized counterdiabatic quantum optimization (BF-DCQO) on a fully connected 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. Six peptide sequences with 14-16 amino-acid residues are encoded using a coarse-grained tetrahedral lattice model, yielding higher-order spin-glass Hamiltonians with long-range interactions involving up to five-body terms and mapped to 46-61 qubits. The resulting instances are demanding for near-term quantum hardware because low-energy configurations must satisfy backbone-geometry constraints while optimizing dense residue-contact interactions. BF-DCQO uses a non-variational bias-feedback mechanism, where low-energy samples from each round define longitudinal fields that guide subsequent quantum evolutions. Across the studied instances, BF-DCQO shifts raw sampled energy distributions toward lower energies than uniform random sampling, with the strongest improvements appearing in residue-contact variables. To preserve this signal, we introduce a consensus-based post-processing pipeline that combines quantum-learned contact information with feasible backbone geometries. The resulting hybrid workflow reaches the classical reference energy in multiple instances and improves over the corresponding random-seeded pipeline. These results show that BF-DCQO can generate structured samples for dense protein-folding Hamiltonians at previously unexplored trapped-ion scales. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26861 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Protein folding on a 64 qubit trapped-ion hardware via counterdiabatic quantum optimization Cadavid, Alejandro Gomez Nikačević, Pavle Chandarana, Pranav Romero, Sebastián V. Solano, Enrique Hegade, Narendra N. Lopez-Ruiz, Miguel Angel Girotto, Claudio Linn, Hanna Doga, Hakan Epifanovsky, Evgeny Barkoutsos, Panagiotis Kl. Kaushik, Ananth Roetteler, Martin Quantum Physics We report the largest trapped-ion hardware demonstration of lattice protein-folding optimization to date, using bias-field digitized counterdiabatic quantum optimization (BF-DCQO) on a fully connected 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. Six peptide sequences with 14-16 amino-acid residues are encoded using a coarse-grained tetrahedral lattice model, yielding higher-order spin-glass Hamiltonians with long-range interactions involving up to five-body terms and mapped to 46-61 qubits. The resulting instances are demanding for near-term quantum hardware because low-energy configurations must satisfy backbone-geometry constraints while optimizing dense residue-contact interactions. BF-DCQO uses a non-variational bias-feedback mechanism, where low-energy samples from each round define longitudinal fields that guide subsequent quantum evolutions. Across the studied instances, BF-DCQO shifts raw sampled energy distributions toward lower energies than uniform random sampling, with the strongest improvements appearing in residue-contact variables. To preserve this signal, we introduce a consensus-based post-processing pipeline that combines quantum-learned contact information with feasible backbone geometries. The resulting hybrid workflow reaches the classical reference energy in multiple instances and improves over the corresponding random-seeded pipeline. These results show that BF-DCQO can generate structured samples for dense protein-folding Hamiltonians at previously unexplored trapped-ion scales. |
| title | Protein folding on a 64 qubit trapped-ion hardware via counterdiabatic quantum optimization |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2604.26861 |