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
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
A study published on bioRxiv demonstrates the first hybrid quantum computing framework combining classical Hamming distance filtering with the Grover quantum search algorithm to overcome bottlenecks in massive genomic data analysis. Utilizing the IBM Qiskit 27-qubit simulator, researchers rapidly and flawlessly identified a 50-nucleotide target sequence of a Cas9-like nuclease within a Brazilian biome metagenome database, even under conditions allowing up to a 30% mismatch. The study lays a revolutionary foundation for synthetic genome design by completing simulations that precisely insert a "BioBloQu" (quantum biological block)—composed of a promoter, an RBS, an enzyme, and a terminator—into the explored scar regions of the minimal genome M. mycoides JCVI-Syn3B. [Quantum Biology Society] Modern life sciences are pouring out genomic sequencing data at an exponential rate. However, due to the immense complexity of biological data, existing classical computing methods are facing severe computational bottlenecks in analyzing and manipulating it. To break through these limitations, a disruptive study recently published on the preprint repository bioRxiv, titled "Genetic Engineering with Quantum Circuits: creating codes and studying BioBloQu genetic elements," has brought quantum computers to the forefront of genetic engineering. A joint research team led by Professor Elibio Rech from the Brazilian Agricultural Research Corporation (Embrapa) Genetic Resources and Biotechnology and the Federal University of Rio Grande do Sul (UFRGS) presented this innovative research. ■ Scanning Massive Genomic Databases with Qubits Using the core quantum mechanical principles of superposition and entanglement as the foundation for information processing, the research team developed a hybrid quantum framework that combines classical Hamming distance filtering with the Grover quantum search algorithm. Powered by IBM's Qiskit 27-qubit simulator, this algorithm was used to search for a 50-nucleotide target sequence of a Cas9-like nuclease within a Brazilian biome metagenome database. As a result, the team successfully and swiftly identified the massive genetic data through amplitude amplification, filtering out the sequence perfectly even under conditions allowing up to a 30% mismatch rate. This proves that vast amounts of genetic data, which are unmanageable for classical computers, can be analyzed in a flash through quantum parallel processing. ■ The Era of Synthetic Genome Design Opened by BioBloQu Furthermore, the researchers successfully completed a quantum circuit simulation that accurately inserts a synthetic genetic construct called "BioBloQu" (quantum biological block) into the identified target regions. In the genome of M. mycoides JCVI-Syn3B, an artificially synthesized minimal genome model, the quantum algorithm first identified two 20-nucleotide "scar" regions—which are traces of gene editing. Then, it precisely integrated a tandem genetic block (BioBloQu) composed of a promoter, a ribosome binding site (RBS), an enzyme sequence, and a terminator into that location. This innovative approach goes beyond simply cutting and pasting existing genes physicochemically; it opens up the possibility of designing and assembling novel synthetic genomes from the ground up under the control of quantum algorithms equipped with overwhelming computational power. By directly applying the computational power of quantum mechanics to biotechnology, this research is expected to serve as the starting point for a massive revolution in next-generation quantum-bio data manipulation, customized gene therapy, and synthetic biology. #QuantumComputing #GeneticRecombination #BioBloQu #QuantumAlgorithm #GroverAlgorithm #Metagenome #SyntheticBiology #GenomeDesign #QuantumBiology #KoreanQuantumBiologySociety https://www.biorxiv.org/content/10.1101/2025.05.02.651535v2
Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.
• Developed quantum codes based on Grover's algorithm using an IBM 127-qubit quantum simulator for large-scale genetic sequence analysis and optimization. • Successfully searched a 3,022-nucleotide database for a 50-nucleotide target sequence at exponential speeds with high flexibility (allowing up to a 30% mismatch rate). • Successfully constructed a synthetic structure named "Quantum Biological Block (BioBloQu)," which combines components such as a promoter and an enzyme, within a minimal JCVI-syn3.0 cell. [Quantum Biology Society] To process the exponentially growing vast amounts of biological data, research results applying quantum algorithms to genomics in earnest have been released for the first time. Recently, a paper titled "GENETIC ENGINEERING THROUGH QUANTUM CIRCUITS: CONSTRUCTION OF CODES AND ANALYSIS OF GENETIC ELEMENTS BIOBLOQU" was published on bioRxiv, a preprint repository for biology. ■ Breaking Through the Limits of Genetic Analysis with a 127-Qubit Quantum Simulator To analyze and optimize large-scale genetic sequences, the research team newly developed two quantum codes applying Grover's algorithm based on IBM's 127-qubit quantum simulator. As a result of testing this quantum algorithm, they succeeded in finding a specific target sequence consisting of 50 nucleotides within a DNA database composed of up to 3,022 nucleotides. In particular, considering natural genetic variations, it demonstrated overwhelmingly fast and flexible search capabilities compared to existing classical computing exploration methods, even allowing a mismatch rate of up to 30%. ■ 'Quantum Biological Block (BioBloQu)' Planted Inside an Artificial Cell The team's achievements did not stop at simple quantum code development and sequence exploration. Based on these quantum computing-based search results, the researchers focused on the interior of 'JCVI-syn3.0', an artificial cell model that maintains life phenomena with only minimal genes. Here, they successfully constructed a synthetic structure named the 'Quantum Biological Block (BioBloQu)', composed of a promoter, a ribosome binding site (RBS), an enzyme, and a terminator. This achievement is highly significant in that it proved quantum computing technology is not merely at a theoretical level, but is a powerful tool capable of innovatively accelerating the speed of actual genetic exploration and manipulation.
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.
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
Abstract Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. An adiabatic quantum computer (AQC) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The AQC is found to be competitive in identifying optimal solutions and future generations of AQCs may be able to outperform classical GAs. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.