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.
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.
We introduce RinQ, a hybrid quantum-classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Protein structures are modeled as residue interaction networks (RINs), and the QUBO formulations are solved using D-Wave’s simulated annealing. Applied to a diverse set of proteins, RinQ consistently identifies central residues that closely align with classical benchmarks, demonstrating both the accuracy and robustness of the approach.
We present RinQ, a hybrid quantum-classical approach for identifying functionally critical residues in proteins by leveraging quantum computing techniques for optimization. Proteins, modeled as residue interaction networks, alongside the task of selecting top central residues, is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is then solved using D-Wave’s simulated annealing. Applied across a diverse set of proteins, the presented approach consistently identifies central residues aligned with classically calculated central sites. RinQ thus exemplifies the potential of near-term quantum and quantum-inspired methods to accelerate protein analysis and inform drug discovery. Our findings match classical benchmarks for small peptides and sets the stage for extending QUBO-based centrality detection to larger proteins using real quantum hardware.
We present a quantum-dynamical framework for identifying structurally important residues in proteins based on continuous time quantum walks (CTQWs) on weighted residue interaction networks constructed from experimentally resolved structures. By mapping the weighted adjacency matrix to a Hamiltonian, residue importance emerges from the long-time averaged occupation probability, confirmed analytically through its spectral decomposition. Across a dataset of approximately 150 proteins spanning diverse structural and functional classes, CTQW centrality exhibits consistently strong agreement with classical eigenvector centrality in identifying central residues, while extending beyond it through incorporating signatures of quantum interference. Analyzing the time-averaged quantum transition matrix reveals consistently larger spectral gaps than the classical random-walk operator. Furthermore, biological relevance is confirmed through recovery of experimentally established functional residues in proteins kinase A and oxytocin. CTQW-derived centrality rankings are accessible on near-term intermediate-scale quantum hardware, as we demonstrate through a proof-of-principle implementation on IBM superconducting quantum hardware. These results establish continuous-time quantum walks as a computationally tractable framework for protein network analysis, that connects network theoretical treatments of protein structural biology to continuous-time quantum walk dynamics.
We present a quantum-dynamical framework for identifying structurally and functionally important residues in proteins based on continuous-time quantum walks (CTQWs) on weighted residue-interaction networks constructed from experimentally resolved structures. By mapping the weighted adjacency matrix to a Hamiltonian, residue importance emerges from the long-time averaged occupation probability, confirmed analytically through its spectral decomposition. Across a data set of 150 proteins spanning diverse structural and functional classes, CTQW centrality exhibits consistently strong agreement with classical eigenvector centrality in identifying central residues, while extending beyond it through incorporating signatures of quantum interference. Analyzing the time-averaged quantum transition matrix reveals consistently larger spectral gaps than the classical random-walk operator. Furthermore, biological relevance is confirmed through recovery of experimentally established functional residues in proteins kinase A and oxytocin. CTQW-derived centrality rankings are accessible on near-term intermediate-scale quantum hardware, as we demonstrate through a proof-of-principle implementation on IBM superconducting quantum hardware. These results establish continuous-time quantum walks as a computationally tractable framework for protein network analysis, that connects network theoretical treatments of protein structural biology to continuous-time quantum walk dynamics.
Demonstrating the practical utility of Noisy Intermediate-Scale Quantum (NISQ) hardware for recurrent tasks in Computer-Aided Drug Discovery is of paramount importance. We tackle this challenge by performing three-dimensional protein pockets hydration-site prediction on a quantum computer. Formulating the water placement problem as a Quadratic Unconstrained Binary Optimization (QUBO), we use a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) to an efficient quantum optimization solver, to run various hardware experiments up to 123 qubits. Matching the precision of classical approaches, our results reproduced experimental predictions on real-life protein-ligand complexes. Furthermore, through a detailed resource estimation analysis, we show that accuracy can be systematically improved with increasing number of qubits, indicating that full quantum utility is in reach. Finally, we provide evidence that advantageous situations could be found for systems where classical optimization struggles to provide optimal solutions. The method has potential for assisting simulations of protein-ligand complexes for drug lead optimization and setup of docking calculations.
Biomolecules and their interactions form the material and processual basis underlying all biological phenomena, from photosynthesis to Alzheimer’s disease. Studying these systems is therefore central to the purview of all biological sciences. Computational biomolecular structure prediction (CBSP) supports this effort by leveraging computers to determine, model, and engineer biomolecular structures, properties, and processes—offering a powerful complement to laboratory-based methods. However, many core CBSP problems—such as finding minimum free energy or conformationally-stable structures given sequence information—are computationally challenging. These problems are typically NP-hard in their general form, while their corresponding decision variants are NP-complete. As a result, both formulations are resistant to efficient exact solution at large scales. Quantum computing, a developing computational paradigm leveraging quantum mechanics, offers a potential path forward, given recent evidence suggesting that certain quantum approaches may reduce resource demands for certain NP-hard problem families. Approaches include fully quantum algorithms, quantum-inspired classical heuristics, and hybrid quantum-classical frameworks, all of which may help address long-standing computational bottlenecks in CBSP. This dissertation offers a preliminary investigation of the practical potential of quantum computing for three core CBSP challenges—RNA folding, multi-body molecular docking, and protein design—that, despite their diverse applications, share structural features well suited to exploration by quantum optimization methods. Specifically, we cast each problem as a cost function network (CFN), and develop transformations of these CFNs to quadratic unconstrained binary optimization (QUBO) models in order to render them compatible with current quantum and quantum-inspired hardware. We argue that these transformations not only broaden the range of solvable CFNs across quantum platforms, but in some cases possess intrinsic features which may offer optimization advantages over native CFN formulations. Using a current‑generation superconducting flux‑qubit quantum annealer, we: (a) demonstrate its use for tuning free QUBO parameters against biomolecular structure data, and (b) benchmark solution quality and resource usage against optimized classical Monte Carlo methods, finding comparable performance. Finally, we package these methods into the Masala Quantum Computing Plugins library, an open‑source, modular CBSP platform that supports CFN construction, multiple QUBO encodings (one‑hot, domain‑wall, approximate‑binary, hybrid), and execution on both classical and quantum backends. Our contribution lays the groundwork for extensible, state‑of‑the‑art, quantum-compatible CBSP workflows.