Designing Peptides on a Quantum Computer
Open accessAbstract
Although a wide variety of quantum computers are currently being developed, actual computational results have been largely restricted to contrived, artificial tasks. Finding ways to apply quantum computers to useful, real-world computational tasks remains an active research area. Here we describe our mapping of the protein design problem to the D-Wave quantum annealer. We present a system whereby Rosetta, a state-of-the-art protein design software suite, interfaces with the D-Wave quantum processing unit to find amino acid side chain identities and conformations to stabilize a fixed protein backbone. Our approach, which we call the QPacker , uses a large side-chain rotamer library and the full Rosetta energy function, and in no way reduces the design task to a simpler format. We demonstrate that quantum annealer-based design can be applied to complex real-world design tasks, producing designed molecules comparable to those produced by widely adopted classical design approaches. We also show through large-scale classical folding simulations that the results produced on the quantum annealer can inform wet-lab experiments. For design tasks that scale exponentially on classical computers, the QPacker achieves nearly constant runtime performance over the range of problem sizes that could be tested. We anticipate better than classical performance scaling as quantum computers mature.
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The authors found a scaling that seemed almost constant in comparison with classical simulated annealing.
“The authors used the D-Wave 2000Q processor finding a scaling that seemed almost constant in comparison with classical simulated annealing.”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [210]
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Marchioli, V., Boggio, M., Volpe, D., Massotti, L., & Novara, C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-77432-4_16 · Cited by 8 · see citing works
FastHare: Fast Hamiltonian Reduction for Large-scale Quantum Annealing
Thai, P. D., Thai, M. T., Vu, T. N., & Dinh, T. N. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00030 · Cited by 10 · see citing works
Computational Design of Peptide-Based Binders to Therapeutic Targets
Mulligan, V. K., & Hosseinzadeh, P. (2022) · ACS symposium series · doi:10.1021/bk-2022-1417.ch003 · Cited by 7 · see citing works
Self-citationQuantum AI in Healthcare : Revolutionizing Diagnosis, Treatment and Drug Discovery
Banerjee, N., & Chatterjee, K. (2024) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst2411351 · Cited by 6 · see citing works
Early quantum computing applications on the path towards precision medicine
Flöther, F. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-98808-7.00001-1 · Cited by 7 · see citing works
EQUAL: Improving the Fidelity of Quantum Annealers by Injecting Controlled Perturbations
Ayanzadeh, R., Das, P., Tannu, S. S., & Qureshi, M. K. (2021) · International Conference on Quantum Computing and Engineering · doi:10.1109/qce53715.2022.00074 · Cited by 11 · see citing works
Quantum annealing-based clustering of single cell RNA-seq data
Kubacki, M. L., & Niranjan, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad377 · Cited by 5 · see citing works
Quantum radio astronomy: Quantum linear solvers for redundant baseline calibration
Renaud, N., Rodr'iguez-S'anchez, P., Hidding, J., & Broekema, P. C. (2023) · Astronomy and Computing · doi:10.1016/j.ascom.2024.100803 · Cited by 6 · see citing works
Investigating the potential for a limited quantum speedup on protein lattice problems
Outeiral, C., Morris, G. M., Shi, J., Strahm, M., Benjamin, S. C., & Deane, C. M. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac29ff · Cited by 6 · see citing works
Talluri, S. (2022) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2022.01.003 · Cited by 5 · see citing works
Quantum computing applications in biology
Ghamsari, M. S. (2025) · Discover Computing · doi:10.1007/s10791-025-09803-y · Cited by 4 · see citing works
Computational Methods for Peptide Macrocycle Drug Design
Mulligan, V. K. (2022) · AAPS advances in the pharmaceutical sciences series · doi:10.1007/978-3-031-04544-8_3 · Cited by 4 · see citing works
Self-citationSandeep, S., Gupta, V., & Keenan, T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.20.000950 · Cited by 4 · see citing works
Uttarkar, A., & Niranjan, V. (2025) · IEEE Transactions on Molecular Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2025.3600516 · Cited by 3 · see citing works
Quang, T. N., Matsuyama, K., Shimizu, K., Sugano, H., Kurimoto, E., Miki, M., Suzuki, J., Chen, Z., Waidyasooriya, H. M., Hariyama, M., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-28481-w · Cited by 6 · see citing works
CHARME: A Chain-based Reinforcement Learning Approach for the Minor Embedding Problem
Ngo, H. M., Nguyen, D., Vu, M. N., Jeter, T. R., Kahveci, T. M., & Thai, M. T. (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3763244 · Cited by 4 · see citing works
The Quantum Computing Paradigm
Ehmer, T., Karemore, G., & Melo, H. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch26 · Cited by 3 · see citing works
Self-citationSlongo, F., & Micheletti, C. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023116 · Cited by 2 · see citing works
EQUAL: Improving the Fidelity of Quantum Annealers by Injecting Controlled PerturbationsPreprint
Ayanzadeh, R., Das, P., Tannu, S., & Qureshi, M. K. (2022) · Research Square · doi:10.21203/rs.3.rs-1343360/v1 · Cited by 2 · see citing works
Skipper: Improving the Reach and Fidelity of Quantum Annealers by Skipping Long Chains
Ayanzadeh, R., & Qureshi, M. K. (2023) · International Conference on Quantum Computing and Engineering · doi:10.1109/qce60285.2024.00079 · Cited by 4 · see citing works
Antibody loop modelling on a quantum computerPreprint
Allcock, J., Vangone, A., Meyder, A., Adaszewski, S., Strahm, M., Hsieh, C., & Zhang, S. (2021) · arXiv (Cornell University) · Cited by 1 · see citing works
Quantum Approximated Graph Cutting: A Rapid Replacement for T-REMD?Preprint
Sandeep, S., Aramyan, S., Poghosyan, A. H., & Gupta, V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.11.420968 · Cited by 1 · see citing works
On quantum computing and geometry optimizationPreprint
Malik, A. J., & Verma, C. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.16.532929 · Cited by 1 · see citing works
A Quantum Annealing Approach to Reduce Covid-19 Spread on College CampusesPreprint
Sud, J., & Li, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.01220
Gate-based Quantum Computing for Protein DesignPreprint
Kim, P., Khatami, M. H., Mendes, U. C., & Wiebe, N. (2022) · Research Square · doi:10.21203/rs.3.rs-1976229/v1
Translating the Nearest Convex Hull Classifier from Classical to Quantum Computing
Cattan, G., Andreev, A., & Barthélemy, Q. (2025) · Quantum Reports · doi:10.3390/quantum7040051
Zaborniak, T., Azadvari, N., Zhu, Q., Turzo, S. B. A., Hosseinzadeh, P., Renfrew, P. D., & Mulligan, V. K. (2025) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2025.09.015
Self-citationApplications of Gaussian Boson Sampling to Solve Some Chemistry Problems
Novir, S. B. (2025) · Quantum Reports · doi:10.3390/quantum7040056
Peptide Discovery: Lead Generation
Angell, Y., Hartsock, W. J., Melo, H., & Reichart, T. M. (2025) · Peptide Science · doi:10.1002/9781119824701.ch06
Self-citationDrug Discovery with Quantum Machine Learning
Pyrkov, A., Aliper, A., Bezrukov, D. S., & Zhavoronkov, A. A. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_10
QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation
Booth, M., Berwald, J., Chukwu, U., Dawson, J., Dridi, R., Le, D., Wainger, M., & Reinhardt, S. P. (2020) · arXiv · doi:10.48550/arxiv.2005.11294 · Cited by 8 · see citing works
- Authors
- Mulligan, V. K., Melo, H., Merritt, H. I., Slocum, S., Weitzner, B. D., Watkins, A. M., Renfrew, P. D., Pelissier, C., Arora, P. S., & Bonneau, R.
- Affiliations
- Flatiron Institute (US)
- New York University (US)
- Johns Hopkins University (US)
- Lyell Immunopharma (United States) (US)
- Stanford University (US)
- Goddard Space Flight Center (US)
- Corresponding author
- Mulligan, Vikram Khipple
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2019-09-02
- OpenAlex
- W2970291663
- Cited by
- 67
- References
- 51
- Citations by year
- 2026: 1 · 2025: 11 · 2024: 14 · 2023: 8 · 2022: 17 · 2021: 10 · 2020: 6
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- National Aeronautics and Space Administration (NNH16ZDA001N-AIST, NNH16ZDA001N)
- Simons Foundation
- National Institute of Advanced Industrial Science and Technology
- National Institutes of Health
- Multidisciplinary University Research Initiative
- National Institute of General Medical Sciences (R35GM130333)
- Army Research Office
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA and Biological Computing
- Keywords
- quantum computing, protein design, quantum annealing, D-Wave quantum annealer, Rosetta, side-chain rotamers, protein folding, quantum advantage, combinatorial optimization
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