Polymer physics by quantum computing
Open accessAbstract
Sampling equilibrium ensembles of dense polymer mixtures is a paradigmatically hard problem in computational physics, even in lattice-based models. Here, we develop a formalism based on interacting binary tensors that allows for tackling this problem using quantum annealing machines. Our approach is general in that properties such as self-avoidance, branching, and looping can all be specified in terms of quadratic interactions of the tensors. Microstates' realizations of different lattice polymer ensembles are then seamlessly generated by solving suitable discrete energy-minimization problems. This approach enables us to capitalize on the strengths of quantum annealing machines, as we demonstrate by sampling polymer mixtures from low to high densities, using the D-Wave quantum annealer. Our systematic approach offers a promising avenue to harness the rapid development of quantum machines for sampling discrete models of filamentous soft-matter systems.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Quantum annealing has been applied to the protein folding problem using simplified lattice models for the polypeptide chain.
“Quantum annealing has been applied to the protein folding problem [71, 72], using simplified lattice models for the polypeptide chain.”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [72]
It is currently estimated that quantum advantage will be achieved by quantum computers with around 10^6-10^7 physical qubits.
“it is currently estimated that quantum advantage, i.e. runtimes faster than those of classical computations for specific problems (such as the ones described below), will be achieved by quantum computers with around 106–107 physical qubits [71, 72]”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [72]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 59 · Cited by 48
Research works printed but not linked: 1
- [21]Salvador E. Venegas-Andraca, William Cruz-Santos, Catherine McGeoch, and Marco Lanzagorta, 'A crossdisciplinary introduction to quantum annealing-based algorithms,' Contemporary Physics 59 , 174-197 (2018), arXiv:1803.03372. · arXiv
- Authors
- Micheletti, C., Hauke, P., & Faccioli, P.
- Affiliations
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- University of Trento (IT)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2021-08-19
- Volume
- 127
- Issue
- 8
- Pages
- 080501-080501
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W3155212525
- Cited by
- 48
- References
- 59
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 7 · 2023: 9 · 2022: 6 · 2021: 1
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- SDL (submittedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- INO Open Portal (submittedVersion)
- Iris (University of Trento) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Provincia Autonoma di Trento
- Bundesministerium für Wirtschaft und Energie (03EI1025C)
- H2020 European Research Council (804305)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- polymer blends, quantum annealing, lattice polymers, self-avoidance, quadratic interaction, D-Wave quantum annealer, soft matter, energy minimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
