A study published on bioRxiv successfully formulated the complex combinatorial problem of fixed-backbone computational protein design as a quadratic Hamiltonian, naturally mapping it onto the hybrid photonic entropy computing platform, Dirac-3. Benchmarking results on protein instances ranging from 493 to 943 variables confirmed that the photonic solver derives high-precision solutions, reaching within 0.16–2.47% of optimal energies. By overcoming the sharp super-polynomial runtime growth of existing classical solvers and implementing near-linear polynomial scaling, it established a scalable framework capable of integrating quantum-inspired hardware into practical protein engineering pipelines. [Quantum Biology Society] Customized computational protein design is considered a core challenge in next-generation drug discovery and biotechnology. However, due to the enormous combinatorial complexity of amino acid sequence combinations, existing classical optimization methods have faced fatal computational bottlenecks. Recently, a groundbreaking study breaking through this massive computational barrier using quantum-inspired hardware based on the physics of light has been published, drawing significant attention. The paper "Entropy Quantum Computing for Fixed-Backbone Protein Design," published on the preprint repository bioRxiv, presents a pioneering demonstration of applying photon-based entropy computing to biotechnology. A joint research team led by Babak Emami from Quantum Computing Inc. (QCi) and Professor David J. Huggins from Weill Cornell Medicine spearheaded this innovative study. ■ Mapping Protein Design to a Physical Model of Light The research team newly formulated the amino acid rotamer variables of a protein with a fixed backbone into a physical energy function known as a quadratic Hamiltonian. This transformed optimization problem maps naturally onto Dirac-3, a hybrid photonic entropy computing platform. Instead of existing computers repeating complex numerical calculations via transistors, this is an innovative approach where an optical system utilizes thermodynamic entropy to guide itself toward the most stable ground energy state (the optimal solution). ■ Overwhelming Scalability Beyond Classical Limits The true value of this photonic solver was clearly revealed in benchmarking experiments on large protein instances ranging from 493 to 943 variables. While the existing classical solver (CFN-based) reached a practical operational limit with a sharp super-polynomial runtime growth beyond approximately 1,000 variables, the Dirac-3 platform firmly maintained a near-linear polynomial scaling. What is even more surprising is that while enjoying this computational speed advantage, the final derived solutions recorded a highly precise level, within 0.16–2.47% of the perfect optimal energy. ■ A Quantum Framework for Next-Generation Protein Engineering This demonstration proves the overwhelming viability that entropy-based quantum optimization can surpass the limits of existing supercomputers in highly dense sequence spaces. The framework established by the research team lays a physical foundation to seamlessly integrate quantum-inspired hardware into practical protein engineering and large-scale screening pipelines, dramatically eliminating the massive friction of conventional computational processes. In the near future, utilizing light and entropy computing, humanity may witness a revolutionary leap in designing novel enzymes and therapeutic antibodies—which do not exist in nature—in real time. https://www.biorxiv.org/content/10.64898/2026.02.20.706589v1
A study published on bioRxiv successfully formulated the complex combinatorial problem of fixed-backbone computational protein design as a quadratic Hamiltonian, naturally mapping it onto the hybrid photonic entropy computing platform, Dirac-3. Benchmarking results on protein instances ranging from 493 to 943 variables confirmed that the photonic solver derives high-precision solutions, reaching within 0.16–2.47% of optimal energies. By overcoming the sharp super-polynomial runtime growth of existing classical solvers and implementing near-linear polynomial scaling, it established a scalable framework capable of integrating quantum-inspired hardware into practical protein engineering pipelines. [Quantum Biology Society] Customized computational protein design is considered a core challenge in next-generation drug discovery and biotechnology. However, due to the enormous combinatorial complexity of amino acid sequence combinations, existing classical optimization methods have faced fatal computational bottlenecks. Recently, a groundbreaking study breaking through this massive computational barrier using quantum-inspired hardware based on the physics of light has been published, drawing significant attention. The paper "Entropy Quantum Computing for Fixed-Backbone Protein Design," published on the preprint repository bioRxiv, presents a pioneering demonstration of applying photon-based entropy computing to biotechnology. A joint research team led by Babak Emami from Quantum Computing Inc. (QCi) and Professor David J. Huggins from Weill Cornell Medicine spearheaded this innovative study. ■ Mapping Protein Design to a Physical Model of Light The research team newly formulated the amino acid rotamer variables of a protein with a fixed backbone into a physical energy function known as a quadratic Hamiltonian. This transformed optimization problem maps naturally onto Dirac-3, a hybrid photonic entropy computing platform. Instead of existing computers repeating complex numerical calculations via transistors, this is an innovative approach where an optical system utilizes thermodynamic entropy to guide itself toward the most stable ground energy state (the optimal solution). ■ Overwhelming Scalability Beyond Classical Limits The true value of this photonic solver was clearly revealed in benchmarking experiments on large protein instances ranging from 493 to 943 variables. While the existing classical solver (CFN-based) reached a practical operational limit with a sharp super-polynomial runtime growth beyond approximately 1,000 variables, the Dirac-3 platform firmly maintained a near-linear polynomial scaling. What is even more surprising is that while enjoying this computational speed advantage, the final derived solutions recorded a highly precise level, within 0.16–2.47% of the perfect optimal energy. ■ A Quantum Framework for Next-Generation Protein Engineering This demonstration proves the overwhelming viability that entropy-based quantum optimization can surpass the limits of existing supercomputers in highly dense sequence spaces. The framework established by the research team lays a physical foundation to seamlessly integrate quantum-inspired hardware into practical protein engineering and large-scale screening pipelines, dramatically eliminating the massive friction of conventional computational processes. In the near future, utilizing light and entropy computing, humanity may witness a revolutionary leap in designing novel enzymes and therapeutic antibodies—which do not exist in nature—in real time. https://www.biorxiv.org/content/10.64898/2026.02.20.706589v1
Computational protein design (CPD) is a central problem in biotechnology, with applications in enzyme engineering and therapeutic design, but its combinatorial complexity poses a significant challenge for classical optimization methods. In this work, we formulate fixed-backbone CPD as a quadratic Hamiltonian over rotamer variables, enabling solution using Quantum Computing Inc.'s photonic entropy computing platform, Dirac-3. We evaluate solution quality by benchmarking against an exact classical cost function network (CFN) solver, which provides provably optimal baselines. On a set of standard benchmark proteins ranging from 493 to 943 variables, Dirac-3 produces best-observed solutions (over 100 samples per instance) within 0.16-2.47% of the optimal energies. These results show that the proposed formulation can identify low-energy configurations on directly solvable CPD instances, while sample-level energy distributions vary by instance. Runtime behavior is reported over the tested regime, where CFN remains faster in absolute terms, while Dirac-3 exhibits moderate growth in runtime with problem size. This study focuses on optimization performance as measured by energy relative to exact baselines under a pairwise fixed-backbone energy model. Exploratory experiments on larger instances using decomposition-based approaches are provided in the Supplementary Material. Overall, the results establish a benchmark for entropy-based optimization on CPD formulations within the directly solvable regime.
A study published on the preprint repository arXiv comprehensively outlines empirical applications of quantum algorithms (such as VQE, QAOA) that can be executed on near-term noisy intermediate-scale quantum (NISQ) hardware to solve key challenges in molecular biology and drug discovery, such as protein folding and molecular docking. Through quantum simulations that overcome the limits of classical computing, it was demonstrated that by directly simulating the Hamiltonian of a chemical system, the 3D energy-minimized state of peptides or small molecule compounds can be accurately predicted with significantly fewer computing resources than before. It provides a practical roadmap for combining quantum machine learning (QML) and molecular biology to dramatically reduce massive computational costs and achieve overwhelming efficiency in the next-generation in silico drug discovery process. [Quantum Biology Society] Problems such as protein folding and molecular docking, which cause the biggest bottlenecks in the drug discovery process, become exponentially more complex as the amino acid sequence gets longer. The number of cases to explore explodes, making it difficult for classical computers to handle their NP-hard complexity. Recently, a groundbreaking demonstration framework aiming to introduce the latest quantum computing algorithms into the pharmaceutical and biotechnology fields to overcome these limitations has been presented, drawing attention from the related industry. The paper titled "Quantum computing for molecular biology," published on the preprint repository arXiv, comprehensively covers the application of practical quantum algorithms (e.g., VQE, QAOA) capable of solving drug discovery challenges that have hit the limits of classical computing, using noisy intermediate-scale quantum (NISQ) hardware. A research team including Alberto Baiardi and Markus Reiher from ETH Zurich in Switzerland, along with Matthias Christandl from the University of Copenhagen in Denmark, led this innovative convergent research. ■ Quantum Simulation: Overcoming the Limits of Classical Computing The research team demonstrated that by utilizing the characteristic of quantum computers to efficiently map and directly simulate the Hamiltonian of chemical systems, the complex 3D energy-minimized states of peptides or small molecule compounds can be accurately found with significantly fewer computing resources than before. This is because quantum computers can fundamentally bypass the "curse of dimensionality," where the computational amount expands exponentially, by naturally simulating the quantum mechanical properties of molecules. ■ A Roadmap for Next-Generation Drug Discovery This research goes beyond simply presenting algorithms; it provides a specific roadmap for next-generation in silico drug discovery processes by combining quantum machine learning (QML) and molecular biology. Through this, it has opened a path to achieve overwhelming efficiency by drastically reducing the massive computational costs and time required to screen drug candidates and optimize their most stable binding structures. This practical guide strongly suggests that quantum computing will become a core foundational technology bringing substantial innovation to the life sciences and pharmaceutical industries in the future. https://arxiv.org/abs/2212.12220
A study published on bioRxiv proposes for the first time a hybrid approach combining quantum annealing and the Lagrange multiplier method by formulating the codon optimization problem as a constrained quadratic binary problem. It demonstrates the overwhelming performance of this quantum-classical hybrid protocol by applying it to practical scenarios: optimizing the SARS-CoV-2 spike protein sequence in a human host and the insulin sequence in an E. coli host. As a result of evaluating biological metrics, it established a robust workflow through which quantum computing can practically contribute to next-generation genetic engineering and new drug development by successfully optimizing codon usage patterns that govern protein translation efficiency and stability. [Quantum Biology Society] To efficiently produce specific proteins in heterologous hosts, a codon optimization process that adjusts the genetic code and codon usage frequencies to suit the host's characteristics is essential. While this is a core task in biotechnology to maximize the expression level and stability of therapeutic proteins like vaccines or insulin, the exponentially large number of possible gene sequence combinations has incurred massive computational costs to find optimal solutions using conventional classical computing methods. Recently, the achievements of a domestic research team that achieved groundbreaking optimization by directly applying quantum computing to this complex biological problem have been published. The paper titled "Quantum-classical hybrid approach for codon optimization and its practical applications," published on the preprint repository bioRxiv, presents a new protocol that will change the paradigm of gene sequence design utilizing quantum annealing. A joint domestic research team led by Professor Joonsuk Huh and researcher You Kyoung Chung from the Department of Chemistry at Sungkyunkwan University, Professor Daniel K Park from the Department of Applied Statistics at Yonsei University, and researcher Dongkeun Lee from the Korea Institute of Science and Technology Information (KISTI) spearheaded this innovative research. ■ Transformation into a Constrained Quadratic Binary Problem and Quantum Computation The research team newly formulated the complex codon optimization process into a mathematical constrained quadratic binary problem. In particular, they adopted a quantum-classical hybrid approach that organically integrates the parallel computing capabilities of quantum annealing hardware with the Lagrange multiplier method, a classical numerical analysis technique. Through this, they overcame the computational bottleneck occurring in the vast sequence search space and rapidly and accurately derived the optimal gene sequence that can maximize protein translation efficiency while simultaneously considering the host's codon usage bias. ■ Demonstrated Achievements Applied to COVID-19 Vaccine and Insulin The true value of this hybrid protocol was clearly proven through two practical biological scenarios. The research team successfully applied this algorithm to models for optimizing the codon sequence of the SARS-CoV-2 spike protein tailored to a human host environment and optimizing the insulin sequence within an E. coli host. As a result of rigorous biological metric evaluations, the sequences designed by the quantum algorithm demonstrated an overwhelming ability to perfectly optimize the codon usage patterns that govern protein translation efficiency. This demonstration is an important milestone declaring that quantum computing technology has moved beyond mere theoretical possibility to establish itself as a powerful and practical tool that can immediately contribute to next-generation gene therapeutics and customized new drug design pipelines. https://www.biorxiv.org/content/10.1101/2024.06.08.598046v1
• Constructed an integrated multi-stage drug design framework utilizing QBoson-CPQC-3Gen, a 2000-node coherent Ising machine (CIM). • Demonstrated superior speed and accuracy over existing heuristic algorithms in core calculations, including allosteric site detection, protein-peptide docking, and intermolecular similarity calculation. • Successfully identified and validated 2 novel druggable sites and bioactive compounds for 6 targets. [Quantum Biology Society] A recent study that overcomes the massive time and computational costs of drug discovery through quantum computing has captured the attention of the pharmaceutical and biotechnology industries. On February 11th, a paper titled "Large-scale quantum computing framework enhances drug discovery in multiple stages" was posted on bioRxiv, a preprint repository for the biological sciences. ■ Stable Computation for Over an Hour... Solving the CADD Bottleneck Traditional computer-aided drug discovery (CADD) consumes astronomical computational costs and resources when processing large-scale combinatorial optimization problems (COPs). To resolve this bottleneck, the research team improved the fiber vibration isolation and temperature control systems to introduce "QBoson-CPQC-3Gen," a coherent Ising machine (CIM) equipped with 2000 nodes. This system showcased groundbreaking performance by achieving stable optimal solutions on large-scale problems for over one hour. Utilizing this improved CIM, they successfully constructed a graph-based quantum computing framework capable of simultaneously handling three major challenges in drug discovery: allosteric site detection, protein-peptide docking, and intermolecular similarity calculation. ■ Overwhelming Speed and Accuracy, Complete with Real-World Wet-Lab Validation The experimental results were highly successful. To solve optimization problems, the newly developed CIM-based methods demonstrated superior computational speed and enhanced accuracy compared to the heuristic algorithms traditionally used on classical computers. Most notably, this technology did not stop at mere theoretical simulations. By applying this system to actual pharmaceutical research, the team successfully identified 2 novel druggable sites and bioactive compounds for 6 targets. Furthermore, these predictive results were comprehensively cross-validated in vitro, in-cell, and by crystal structures. By proving that quantum computing can be integrated into the entire multi-stage drug design process, this research represents a significant advancement in both quantum computing applications and pharmaceutical research.
- A study published in the international journal npj Quantum Information (Nature Portfolio) presents a resource-efficient quantum algorithm framework based on the Variational Quantum Eigensolver (VQE) for peptide and protein folding. - By translating protein folding into a tetrahedral lattice model and combining Conditional Value-at-Risk (CVaR) VQE with a classical evolutionary algorithm, the number of required qubits was drastically reduced. - It successfully folded a 7-amino acid neuropeptide using only 9 qubits on actual IBM 20-qubit quantum hardware, demonstrating its practical potential for next-generation drug discovery in the NISQ (Noisy Intermediate-Scale Quantum) era. [Quantum Biology Society] The protein folding problem, which involves predicting the three-dimensional structure of proteins and peptides from their primary amino acid sequence, plays a central role in various applications such as chemistry, biology, and medicine. However, as the sequence lengthens, the number of possible structures to explore explodes exponentially, making it difficult for classical computers to handle its intrinsic NP-hard complexity. Recently, a new quantum algorithm that can efficiently solve this long-standing challenge even on noisy intermediate-scale quantum (NISQ) computers has been developed, drawing the attention of the academic community. The paper titled "Resource-efficient quantum algorithm for protein folding," published in the international journal npj Quantum Information, presents an innovative computing framework for the discovery of next-generation peptide drugs. The research team of Anton Robert and Ivano Tavernelli from IBM Quantum, IBM Research - Zurich in Switzerland successfully led this groundbreaking study. ■ The Meeting of Lattice Models and Qubit Optimization Until now, attempts to introduce quantum computing to protein folding have required an unattainably high number of qubits that current, near-term quantum hardware cannot handle. To solve this, the research team converted protein folding into a tetrahedral lattice model. This model can efficiently depict the growth of a polymer chain while maintaining chemically plausible bond angles. In particular, they adopted a method of adding penalty terms to the Hamiltonian to prevent unphysical amino acid overlaps (e.g., beads occupying the same lattice site). Through this, they succeeded in drastically reducing the number of qubits required for calculation while suppressing the complexity of the algorithm to the level of O(N⁴). ■ Demonstration of Folding on Actual Quantum Hardware This algorithm maximized efficiency by combining CVaR-VQE, a variation of the Variational Quantum Eigensolver (VQE), with a classical Differential Evolution optimizer. Applying this technique, the researchers simulated the folding of the 10-amino acid Angiotensin peptide using only 22 qubits, successfully finding the most stable energy state (native fold). Furthermore, they successfully demonstrated the folding of a 7-amino acid neuropeptide (APRLRFY) using only 9 qubits on IBM's 20-qubit quantum computer hardware. This study is a groundbreaking achievement proving that the protein folding problem, which previously required massive computing resources, can be successfully solved even in a noisy real quantum processor environment. It is expected to bring tremendous innovation to next-generation /de novo/ drug design and disease treatment research utilizing quantum computing in the future.
• Published on arXiv, successfully utilized actual quantum hardware with up to 123 qubits to predict hydration sites in three-dimensional protein pockets. • Formulated water placement as a Quadratic Unconstrained Binary Optimization (QUBO) problem, achieving precision matching classical computing by combining a classical 3D-RISM model with a quantum solver. • Confirmed that prediction accuracy systematically improves as the number of qubits increases, proving the practical utility of quantum computing in next-generation lead optimization and docking calculations. [Quantum Biology Society] In Computer-Aided Drug Discovery (CADD), accurately predicting how drugs and water molecules interact and are placed inside a target protein pocket—that is, the hydration site—plays a crucial role in evaluating drug binding affinity. However, long-standing questions have remained regarding whether current Noisy Intermediate-Scale Quantum (NISQ) hardware can solve such complex and recurrent biological optimization tasks at a practical level. Recently, a study tackling this limitation head-on by mobilizing an actual 123-qubit quantum computer was published on the preprint repository arXiv, drawing significant attention. This paper, titled "Practical protein-pocket hydration-site prediction for drug discovery on a quantum computer," shows that quantum computing can be deployed in earnest in the field of drug design. A joint research team including Dr. Daniele Loco from Qubit Pharmaceuticals in France, Professor Jean-Philip Piquemal from Sorbonne Université, and the quantum control company Q-CTRL achieved this groundbreaking result. ■ Replacing Water Placement with a Quantum Optimization Problem The research team newly formulated the optimal water placement problem inside a protein as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Instead of leaving the entire computation solely to quantum hardware, they adopted a hybrid approach coupling a classical three-dimensional reference-interaction site model (3D-RISM) with an efficient quantum optimization solver. Based on this, they performed various hardware experiments utilizing up to 123 qubits in an actual quantum hardware environment. As a result, they succeeded in reproducing a precision equivalent to state-of-the-art classical approaches in predicting the hydration sites of real-life protein-ligand complexes. ■ Evolving Accuracy as Qubits Increase Another significant achievement of this study is proving a distinct correlation between the increase in quantum resources (number of qubits) and prediction accuracy. Through a detailed resource estimation analysis, they confirmed that the accuracy of hydration-site prediction systematically improves as the number of mobilized qubits increases. This means that even in massive and complex biomolecular systems where classical optimization algorithms hit computational limits, quantum computers can reach full quantum utility as devices scale in the near term. This achievement strongly suggests that the quantum-classical hybrid hydration prediction model will become a groundbreaking tool that changes the landscape of next-generation lead optimization processes and precise drug docking calculations.