Exploring Quantum Annealing for Coarse-Grained Protein Folding
- URL: http://arxiv.org/abs/2508.10660v1
- Date: Thu, 14 Aug 2025 14:00:30 GMT
- Title: Exploring Quantum Annealing for Coarse-Grained Protein Folding
- Authors: Timon Scheiber, Matthias Heller, Andreas Giebel,
- Abstract summary: We compare several folding protein models for quantum computers and analyze their scaling and performance for classical and quantum abs.<n>We introduce a novel encoding of coordinate based models on the tetrahedral lattice, based on interleaved grids.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We explore the potential application of quantum annealing to address the protein structure problem. To this end, we compare several proposed ab initio protein folding models for quantum computers and analyze their scaling and performance for classical and quantum heuristics. Furthermore, we introduce a novel encoding of coordinate based models on the tetrahedral lattice, based on interleaved grids. Our findings reveal significant variations in model performance, with one model yielding unphysical configurations within the feasible solution space. Furthermore, we conclude that current quantum annealing hardware is not yet suited for tackling problems beyond a proof-of-concept size, primarily due to challenges in the embedding. Nonetheless, we observe a scaling advantage over our in-house simulated annealing implementation, which, however, is only noticeable when comparing performance on the embedded problems.
Related papers
- Quantum Annealing for Combinatorial Optimization: Foundations, Architectures, Benchmarks, and Emerging Directions [0.0]
Critical decision-making issues in science, engineering, and industry are based on optimization.<n>We develop a unified framework, relating adiabatic quantum dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to modern flux-qubit annealers.<n>We find that overhead in embedding and encoding is the largest of the scalability and performance.
arXiv Detail & Related papers (2026-02-03T04:51:26Z) - Quantum Qualifiers for Neural Network Model Selection in Hadronic Physics [0.0]
We develop tools that guide model selection between classical and quantum deep neural networks based on intrinsic properties of the data.<n>We show how relative model performance follows systematic trends in complexity, noise, and dimensionality, and how these trends can be distilled into a predictive criterion.
arXiv Detail & Related papers (2026-01-19T23:37:31Z) - Minimal Quantum Reservoirs with Hamiltonian Encoding [72.27323884094953]
We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding.<n>This approach circumvents many of the experimental overheads typically associated with quantum machine learning.
arXiv Detail & Related papers (2025-05-28T16:50:05Z) - Optimized Quantum Embedding: A Universal Minor-Embedding Framework for Large Complete Bipartite Graph [0.5242869847419834]
Minor embedding is essential for mapping largescale problems onto quantum annealers, particularly in quantum machine learning and optimization.<n>This work presents an optimized, universal minor-embedding framework that efficiently complete bipartite graphs onto the hardware topology of quantum annealers.
arXiv Detail & Related papers (2025-04-29T18:44:12Z) - Nonperturbative features in the Lie-algebraic Kähler sigma model with fermions [0.0]
We investigate a quantum mechanical system originating from a Lie-algebraic K"ahler sigma model with multiple right-handed chiral fermions.<n>We identify and analyze saddle point solutions and examine their contributions within the perturbative expansions of the ground state energy.<n>We propose that the elongation parameter becomes relevant in shaping the system's quantum behavior from the three-loop level.
arXiv Detail & Related papers (2024-12-16T04:55:14Z) - Beyond-classical computation in quantum simulation [21.45294717016955]
We show that superconducting quantum annealing processors can generate samples in close agreement with solutions of the Schr"odinger equation.<n>We demonstrate area-law scaling of entanglement in the model quench dynamics of two-, three-, and infinite-dimensional spin glasses.<n>We show that several leading approximate methods based on tensor networks and neural networks cannot achieve the same accuracy as the quantum annealer.
arXiv Detail & Related papers (2024-03-01T19:00:04Z) - Contextual Subspace Variational Quantum Eigensolver Calculation of the Dissociation Curve of Molecular Nitrogen on a Superconducting Quantum Computer [0.06990493129893112]
We present an experimental demonstration of the Contextual Subspace Variational Quantum Eigensolver on superconducting quantum hardware.
In particular, we compute the potential energy curve for molecular nitrogen, where a dominance of static correlation in the dissociation limit proves challenging for many conventional quantum chemistry techniques.
Our quantum simulations retain good agreement with the full configuration interaction energy in the chosen STO-3G basis, outperforming all benchmarked single-reference wavefunction techniques in capturing the bond-breaking appropriately.
arXiv Detail & Related papers (2023-12-07T16:05:52Z) - Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization [58.720142291102135]
We introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware.
We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model.
arXiv Detail & Related papers (2022-09-05T07:23:03Z) - Fermionic approach to variational quantum simulation of Kitaev spin
models [50.92854230325576]
Kitaev spin models are well known for being exactly solvable in a certain parameter regime via a mapping to free fermions.
We use classical simulations to explore a novel variational ansatz that takes advantage of this fermionic representation.
We also comment on the implications of our results for simulating non-Abelian anyons on quantum computers.
arXiv Detail & Related papers (2022-04-11T18:00:01Z) - Generalization Metrics for Practical Quantum Advantage in Generative
Models [68.8204255655161]
Generative modeling is a widely accepted natural use case for quantum computers.
We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance.
Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
arXiv Detail & Related papers (2022-01-21T16:35:35Z) - Benchmarking variational quantum eigensolvers for the
square-octagon-lattice Kitaev model [3.6810704401578724]
Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest.
The variational quantum eigensolver (VQE) is a promising approach to finding energy eigenvalues on noisy quantum computers.
We demonstrate the implementation of HVA circuits on Rigetti's Aspen-9 chip with error mitigation.
arXiv Detail & Related papers (2021-08-30T16:58:43Z) - Polynomial unconstrained binary optimisation inspired by optical
simulation [52.11703556419582]
We propose an algorithm inspired by optical coherent Ising machines to solve the problem of unconstrained binary optimization.
We benchmark the proposed algorithm against existing PUBO algorithms, and observe its superior performance.
The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approxing the electronic structure problem by a PUBO problem.
arXiv Detail & Related papers (2021-06-24T16:39:31Z) - Quantum Machine Learning with SQUID [64.53556573827525]
We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems.
We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
arXiv Detail & Related papers (2021-04-30T21:34:11Z) - Investigating the potential for a limited quantum speedup on protein
lattice problems [0.0]
Protein folding is a central challenge in computational biology, with important applications in molecular biology, drug discovery and catalyst design.
quantum algorithms may well offer improvements for problems in the protein folding and structure prediction realm.
arXiv Detail & Related papers (2020-04-02T16:40:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.