• 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.
• 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.
• Development of the quantum algorithm 'QuBio.py' for searching and analyzing genetic information. • Successful detection of a 50-nucleotide target sequence within a 3,022-nucleotide database using a 127-qubit simulator on the IBM quantum platform. • Securing flexibility that allows up to a 30% mismatch rate... Heralding a new horizon for synthetic biology and new drug development. [Quantum Biology Society] A "quantum computing"-based gene search algorithm has emerged, capable of analyzing exponentially increasing biological data at ultra-high speeds, surpassing the limitations of conventional computers. A study titled "GENETIC ENGINEERING THROUGH QUANTUM CIRCUITS: CONSTRUCTION OF CODES AND ANALYSIS OF GENETIC ELEMENTS BIOBLOQU" was recently published on bioRxiv, a preprint repository for biological sciences. The research team, including Patrícia Verdugo Pascoal, presented an innovative model that explores and manipulates genetic information utilizing quantum mechanics. ■ Translating the Four DNA Bases into Quantum Language... The Evolution of 'Grover's Algorithm' Modern biology generates approximately 1 Gb of sequencing data per hour, but analyzing this data using classical computing methods faces significant limitations. Existing database search methods require computation time proportional to the data size (O(N)), but the quantum-based 'Grover's algorithm' applied by the researchers can significantly reduce this to the O(√N}) level. In particular, a key feature of this study is that it adapted the algorithm to process the four nucleotides (A, T, G, C) that make up DNA, rather than simple binary symbols, thereby enabling it to operate efficiently on more complex datasets. ■ Detecting the Cas9 Target Sequence with 127 Qubits... Creation of Synthetic Genome (BioBloQu) Based on the 127-qubit simulator of the IBM cloud platform, the research team developed its own quantum algorithm code named 'QuBio.py'. Through this algorithm, they succeeded in finding a specific 50-nucleotide target DNA sequence within a genetic database consisting of up to 3,022 nucleotides. A conserved functional domain of the Cas9-like nucleotide sequence found in Brazilian biomes was used as the target sequence. Notably, this quantum algorithm permits a mismatch rate of up to 30% during the sequence comparison process. This enables flexible and rapid analysis in situations where sequence variations are expected, such as homologous gene analysis or evolutionary studies. Furthermore, based on these search results, the team succeeded in constructing a synthetic structure called a 'Quantum Biological Block (BioBloQu)' inside minimal cells (JCVI-syn3.0). The researchers stated that this algorithm "constitutes a unique starting point for developing a more sophisticated model for the manipulation of data". They also predicted that "the integration of quantum computing with artificial intelligence represents an attractive avenue... By optimizing genetic algorithms and facilitating real-time data analyses, quantum computing can revolutionize trait discovery processes in agriculture and biotechnology". This achievement is evaluated as an important example demonstrating how quantum computing can contribute to next-generation synthetic biology technologies that analyze large-scale genetic big data and design genomes from the bottom up.
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
Quantum biology is an emergent field that investigates quantum-mechanical phenomena, such as superposition, tunneling, and entanglement, in the context of data manipulation from living systems. The exploration and engineering of nucleotide sequences rely on quantum mechanical principles, particularly the use of qubit states for the development of quantum codes. Biological sequencing data is produced at about 1 Gb/h, but analysis lags due to complexity and the limitations of classical computing. Despite these challenges, quantum computing offers a potential tool for analyzing and assembling biological data. Here, we developed quantum codes for genetic engineering. The developed quantum computational framework identifies sequences of interest within a genomic database. It locates the left and right boundaries of the scar region in the JCVI-Syn3B genome and detects 20 nucleotides flanking each boundary. After confirming the left and right ends of the scar, a secondary computational routine performs the targeted insertion of the BioBloQu structure, composed of genetic elements, into the previously characterized scar region. Our algorithms constitute a unique starting point for advanced genetic data manipulation. This tool could accelerate the exploration of large volumes of genetic data and enable the developmental design and assembly of synthetic genomes enhanced by quantum computing. Further improvements in algorithms and codes, along with the expanded availability of devices, will accelerate the search for data for applied genetic research.
Replication of DNA and synthesis of proteins are studied from the view-point of quantum database search. Identification of a base-pairing with a quantum query gives a natural (and first ever!) explanation of why living organisms have 4 nucleotide bases and 20 amino acids. It is amazing that these numbers arise as solutions to an optimisation problem. Components of the DNA structure which implement Grovers algorithm are identified, and a physical scenario is presented for the execution of the quantum algorithm. It is proposed that enzymes play a crucial role in maintaining quantum coherence of the process. Experimental tests that can verify this scenario are pointed out.
• Published in the international journal Patterns, successfully implementing the dynamics of biological Boolean networks (BNs) on a real quantum processing unit (QPU). • Demonstrated the efficiency of utilizing quantum superposition states to compute gene knockout and overexpression perturbation experiments in a single ensemble operation. • Traced back the predecessors of an attractor using Grover's search algorithm and successfully estimated the basin size through quantum counting techniques. [Quantum Biology Society] When analyzing complex biological mechanisms such as intracellular gene expression or metabolic pathways, classical computers face the limitation of exponentially expanding state spaces to compute as the number of components (nodes) increases. Recently, an empirical study that groundbreakingly resolved this fundamental computational bottleneck in systems biology through quantum computing has been published, drawing significant attention. A paper titled "Leveraging quantum computing for dynamic analyses of logical networks in systems biology" was published in Patterns, a data science journal published by Cell Press. A joint research team, including Felix M. Weidner and Professor Hans A. Kestler from Ulm University in Germany, presented a groundbreaking proof of principle for implementing the dynamics of biological logical networks using qubits. ■ 'Quantum Superposition' Taming Exponential Expansion In biological Boolean networks (BNs), when there are n components, the number of states the entire system can have increases exponentially to 2ⁿ. Focusing on the fact that the dimension of the computational space of an entangled system of qubits also expands exponentially, the research team successfully matched the exponential growth of network states to a system of multiple qubits. In a 5-gene network experiment (Emx2, Pax6, Coup-tfi, Sp8, Fgf8) modeling the development of the mammalian cerebral cortex, the research team created a uniform superposition of all possible initial states using Hadamard gates. With just a single run of the quantum state transition circuit, they were able to simultaneously evaluate multiple classical state transitions and ultimately converge the system into an attractor state, which signifies a stable phenotype. ■ 'Quantum Perturbation' Experiments Instead of Genetic Scissors In traditional biological research, an enormous number of classical simulations are required to understand the impact of specific gene knockouts or overexpression. However, in a quantum network model, biologically meaningful stable state information can be efficiently extracted through algorithms that utilize quantum properties such as superposition. The research team processed combinations of perturbations of multiple genes in a single ensemble operation using superposition states, and successfully derived a more realistic basin size by weighting specific genes to reflect prior biological knowledge. ■ Tracing the Origins of Disease with Grover's Algorithm What is particularly notable is that by combining the characteristics of quantum circuits with Grover's search algorithm, they traced back the initial predecessors that caused a specific final phenotype (attractor). Furthermore, they successfully estimated the total number of states leading to that attractor using quantum counting techniques. Such quantum backtracking techniques can serve as highly powerful and practical analytical tools for inversely identifying the pathogenic pathways of complex metabolic diseases at the molecular network level and selecting key target proteins. ■ Going Beyond Simulators to Successfully Run on Real Quantum Hardware (QPU) This research did not stop at a simple theoretical proposal. The research team directly ran and demonstrated this biological quantum circuit while comparing the performance of two real quantum computers with classical simulators. This is evaluated as a major breakthrough proving that algorithms impossible to implement on classical hardware can be introduced to real quantum computers for the dynamic analysis of biological systems.
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