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.
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 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
Modelling and predicting protein configurations is crucial for advancing drug discovery, enabling the design of treatments for life-threatening diseases. A critical aspect of this challenge is rotamer optimisation - the determination of optimal side-chain conformations given a fixed protein backbone. This problem, involving the internal degrees of freedom of amino acid side-chains, significantly influences the protein's overall structure and function. In this work, we develop a resource-efficient optimisation algorithm to compute the ground state energy of protein structures, with a focus on side-chain configuration. We formulate the rotamer optimisation problem as a Quadratic Unconstrained Binary Optimisation problem and map it to an Ising model, enabling efficient quantum encoding. Building on this formulation, we propose a quantum algorithm based on the Quantum Approximate Optimisation Algorithm to explore the conformational space and identify low-energy configurations. To benchmark our approach, we conduct a classical study using custom-built libraries tailored for structural characterisation and energy optimisation. Our quantum method demonstrates a reduction in computational cost compared to classical simulated annealing techniques, offering a scalable and promising framework for protein structure optimisation in the quantum era.
Sidechain packing is a critical stage in protein folding, with direct implications for structure-based drug discovery. Google DeepMind's tool, AlphaFold2, predicts protein backbone reliably but sidechain positioning less accurately. Recovering the lowest-energy rotamer assignment over a fixed backbone is NP-hard. We present a hybrid quantum-classical pipeline that repacks sidechains on the AlphaFold backbone using the Quantum Approximate Optimisation Algorithm (QAOA), encoding the one- and two-body energies as a quadratic unconstrained binary optimisation (QUBO) problem. We introduce a constraint-preserving ansatz, pairing a W-state initialisation with a cyclic XY ring mixer, that enforces one-hot rotamer validity without penalty terms while keeping two-qubit gate scaling linear in the rotamer count. We also define an asymptotic shot-scaling metric, measured against the experimentally resolved conformation, that fixes optimiser quality independently of the baseline; its fitted growth stays below the classical exhaustive-search rate at moderate rotamer flexibility. Evaluated on bovine pancreatic trypsin inhibitor (5PTI) across high- and moderate AlphaFold-confidence regions, the pipeline lowers conformational energy against the AlphaFold baseline.
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.
We introduce a framework for combinatorial optimization using sum-constrained continuous quadratic programs solvable by QCi's Dirac-3S photonic entropy computer. This is enabled by the Motzkin-Straus theorem which provides a powerful bridge between discrete clique problems and optimization over the probability simplex. We demonstrate this framework's versatility by solving constraint satisfaction problems, providing extensive benchmarks on the DIMACS suite. The Dirac-3S platform matches or outright leads two independently implemented classical baselines on more than four-fifths of the benchmark instances, reaching the best known solution on nearly all structured graph families, even outperforming both classical solvers on several of the largest instances tested. On the other hand, well-tuned classical continuous optimizers retain an edge only on the hardest planted-clique instances. This work establishes a viable pathway for solving combinatorial optimization problems using natively analog unconventional computing platforms, while positioning entropy computing as a competitive approach for navigating non-convex landscapes and providing rigorous baselines for an emerging computational paradigm.
Codon optimization is a challenging combinatorial optimization problem with important applications in synthetic biology, protein expression, and biotechnology. While quantum annealing has previously been explored for this problem, the application of gate-based quantum algorithms remains largely unexplored. In this work, we present a gate-based quantum computing framework for codon optimization by formulating the optimization objective as an Ising Hamiltonian and expressing it in terms of Pauli operators suitable for gate-based quantum processors. The proposed framework is investigated using the Variational Quantum Eigensolver (VQE), Sampling Variational Quantum Eigensolver (SVQE), and the Quantum Approximate Optimization Algorithm (QAOA), with benchmarking against exact diagonalization, classical optimization, and quantum annealing. Using a three-amino-acid benchmark, the proposed implementations successfully reproduce the exact ground-state solutions while identifying optimized ansatz selections, optimization strategies, initialization schemes, and hardware execution settings that provide reproducible performance on current noisy quantum hardware. Hardware demonstrations are performed on IBM Quantum processors to evaluate the practical implementation of the proposed workflow. As a work in progress, this study establishes a reproducible computational framework for gate-based quantum codon optimization and provides the foundation for future investigations of larger protein sequences as quantum hardware and algorithms continue to advance.