RinQ: Predicting central sites in proteins on current quantum computers

• URL: http://arxiv.org/abs/2508.01501v1
• Date: Sat, 02 Aug 2025 21:53:09 GMT
• Title: RinQ: Predicting central sites in proteins on current quantum computers
• Authors: Shah Ishmam Mohtashim,
• Abstract summary: RinQ is a hybrid quantum-classical framework for identifying functionally critical residues in proteins.<n>This work highlights the promise of near-term quantum and quantum-inspired methods for advancing protein network analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce RinQ, a hybrid quantum-classical framework for identifying functionally critical residues in proteins, utilizing techniques in quantum optimization. To that end, protein structures are modeled as residue interaction networks (RINs), and the centrality detection task is cast as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Solved using D-Wave's simulated annealing, this approach is applied to a diverse set of proteins, including small peptides and biologically significant regulatory proteins. RinQ consistently finds residues that align with classical centrality benchmarks, underscoring the accuracy and reliability of the approach. This work highlights the promise of near-term quantum and quantum-inspired methods for advancing protein network analysis and lays the groundwork for future extensions to larger systems using real quantum hardware.

Related papers

• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Bias-field digitized counterdiabatic quantum optimization [39.58317527488534]
  We call this protocol bias-field digitizeddiabatic quantum optimization (BF-DCQO) Our purely quantum approach eliminates the dependency on classical variational quantum algorithms. It achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude.
  arXiv  Detail & Related papers  (2024-05-22T18:11:42Z)
• Projective Quantum Eigensolver via Adiabatically Decoupled Subsystem Evolution: a Resource Efficient Approach to Molecular Energetics in Noisy Quantum Computers [0.0]
  We develop a projective formalism that aims to compute ground-state energies of molecular systems accurately using Noisy Intermediate Scale Quantum (NISQ) hardware. We demonstrate the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems.
  arXiv  Detail & Related papers  (2024-03-13T13:27:40Z)
• Neutron-nucleus dynamics simulations for quantum computers [49.369935809497214]
  We develop a novel quantum algorithm for neutron-nucleus simulations with general potentials. It provides acceptable bound-state energies even in the presence of noise, through the noise-resilient training method. We introduce a new commutativity scheme called distance-grouped commutativity (DGC) and compare its performance with the well-known qubit-commutativity scheme.
  arXiv  Detail & Related papers  (2024-02-22T16:33:48Z)
• Peptide Binding Classification on Quantum Computers [3.9540968630765643]
  We conduct an extensive study on using near-term quantum computers for a task in the domain of computational biology. We perform sequence classification on a task relevant to the design of therapeutic proteins, and find competitive performance with classical baselines of similar scale. This work constitutes the first proof-of-concept application of near-term quantum computing to a task critical to the design of therapeutic proteins.
  arXiv  Detail & Related papers  (2023-11-27T10:32:31Z)
• Variational Quantum Approximated Spectral Clustering [0.6718184400443239]
  We propose Variational Quantum Approximated Spectral Clustering (VQASC), which extends quantum distance-based classifier models to the clustering framework.<n>Our approach uses efficient quantum circuit designs whose depth scales sub-quadratically with dataset size, enabling the computation of weighted sums over various matrix representations of an undirected graph.
  arXiv  Detail & Related papers  (2023-09-08T17:54:42Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm [1.439946676159516]
  In this work, we investigate the verification of ReLU networks, which involves solving a robustness many-variable mixed-integer programs (MIPs) To alleviate this issue, we propose to use QC for neural network verification and introduce a hybrid quantum procedure to compute provable certificates. We show that, in a simulated environment, our certificate is sound, and provide bounds on the minimum number of qubits necessary to approximate the problem.
  arXiv  Detail & Related papers  (2022-05-02T13:23:56Z)
• Peptide conformational sampling using the Quantum Approximate Optimization Algorithm [0.03499870393443267]
  We numerically investigate the performance of a variational quantum algorithm in sampling low-energy conformations of short peptides. Results cast serious doubt on the ability of QAOA to address the protein folding problem in the near term.
  arXiv  Detail & Related papers  (2022-04-04T20:09:50Z)
• Quantum Computational Quantification of Protein-Ligand Interactions [0.0]
  This is the first application of real quantum computers to the calculation of protein-ligand binding energies. The results shed light on hardware and software requirements which would enable the application of NISQ algorithms in drug design.
  arXiv  Detail & Related papers  (2021-10-15T15:48:40Z)

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.