• Published on bioRxiv, presenting the first quantum algorithm to dramatically accelerate Flux Balance Analysis (FBA), a core technique in cellular metabolism research. • Overcomes the computational limits of existing classical computing by introducing the Quantum Interior Point Method (QIPM) utilizing Quantum Singular Value Transformation (QSVT) technology. • Proves computational advantage by demonstrating accurate convergence to optimal metabolic fluxes through simulations of the glycolysis and TCA cycle networks. [Quantum Biology Society] Biological systems in living organisms, such as cellular metabolism, are intricately entangled with thousands of biochemical reactions. Modeling and analyzing this network, which determines how a cell grows, responds to its environment, and produces energy, requires massive mathematical computations. As the scale increases, existing classical computers easily hit computational limits. Recently, an innovative study solving the optimization problem of such complex metabolic networks at high speed using quantum computing technology has been published, drawing the academic community's attention. A paper titled "Quantum Algorithm for Metabolic Network Analysis" was released on the preprint repository bioRxiv. Through this paper, a research team comprising Ashish Joshi and Takahiko Koyama from Keio University in Japan presented the first instance of reconfiguring Flux Balance Analysis (FBA)—considered a core and standard technique in cellular metabolism research—to be processed on quantum hardware. ■ Overcoming the Wall of Massive Matrices with Quantum Singular Value Transformation (QSVT) Flux Balance Analysis (FBA) is equivalent to the process of solving a massive Linear Programming problem. To overcome the vast matrix inversions occurring in this process, the researchers newly introduced the Quantum Interior Point Method (QIPM) consisting of quantum subroutines. In particular, by utilizing Quantum Singular Value Transformation (QSVT) technology, a state-of-the-art quantum algorithmic technique, they succeeded in transforming the metabolic optimization problem to run efficiently on a quantum computer. Through this, they designed it to dramatically reduce the bottleneck of classical algorithms, where computation time increases exponentially as the network scale grows and constraints become more complex. ■ Successful Simulation of Glycolysis and the TCA Cycle... Proving Computational Advantage This quantum approach provides a firm computational advantage over the existing classical interior point methods in computing large-scale and highly constrained biological networks. Not stopping at a theoretical proposal, the researchers directly demonstrated the practical applicability of this quantum algorithm by conducting numerical simulations on the glycolysis and TCA cycle networks, the core energy metabolic pathways of cells. As a result of the analysis, it was mathematically proven that the quantum solution accurately converges to the optimal metabolic flux, the correct biological target, without error. This study is evaluated as a significant milestone showing that quantum computing technology can be utilized as a practical and powerful tool to solve challenges in complex systems biology and metabolic engineering, going beyond the realms of physics or chemistry. If this algorithm is advanced and applied to actual large-scale quantum hardware in the future, it is expected to revolutionarily accelerate the production of useful substances using microbial engineering or the discovery of therapeutic targets for intractable metabolic diseases.
• Published on bioRxiv, presenting the first quantum algorithm to dramatically accelerate Flux Balance Analysis (FBA), a core technique in cellular metabolism research. • Overcomes the computational limits of existing classical computing by introducing the Quantum Interior Point Method (QIPM) utilizing Quantum Singular Value Transformation (QSVT) technology. • Proves computational advantage by demonstrating accurate convergence to optimal metabolic fluxes through simulations of the glycolysis and TCA cycle networks. [Quantum Biology Society] Biological systems in living organisms, such as cellular metabolism, are intricately entangled with thousands of biochemical reactions. Modeling and analyzing this network, which determines how a cell grows, responds to its environment, and produces energy, requires massive mathematical computations. As the scale increases, existing classical computers easily hit computational limits. Recently, an innovative study solving the optimization problem of such complex metabolic networks at high speed using quantum computing technology has been published, drawing the academic community's attention. A paper titled "Quantum Algorithm for Metabolic Network Analysis" was released on the preprint repository bioRxiv. Through this paper, a research team comprising Ashish Joshi and Takahiko Koyama from Keio University in Japan presented the first instance of reconfiguring Flux Balance Analysis (FBA)—considered a core and standard technique in cellular metabolism research—to be processed on quantum hardware. ■ Overcoming the Wall of Massive Matrices with Quantum Singular Value Transformation (QSVT) Flux Balance Analysis (FBA) is equivalent to the process of solving a massive Linear Programming problem. To overcome the vast matrix inversions occurring in this process, the researchers newly introduced the Quantum Interior Point Method (QIPM) consisting of quantum subroutines. In particular, by utilizing Quantum Singular Value Transformation (QSVT) technology, a state-of-the-art quantum algorithmic technique, they succeeded in transforming the metabolic optimization problem to run efficiently on a quantum computer. Through this, they designed it to dramatically reduce the bottleneck of classical algorithms, where computation time increases exponentially as the network scale grows and constraints become more complex. ■ Successful Simulation of Glycolysis and the TCA Cycle... Proving Computational Advantage This quantum approach provides a firm computational advantage over the existing classical interior point methods in computing large-scale and highly constrained biological networks. Not stopping at a theoretical proposal, the researchers directly demonstrated the practical applicability of this quantum algorithm by conducting numerical simulations on the glycolysis and TCA cycle networks, the core energy metabolic pathways of cells. As a result of the analysis, it was mathematically proven that the quantum solution accurately converges to the optimal metabolic flux, the correct biological target, without error. This study is evaluated as a significant milestone showing that quantum computing technology can be utilized as a practical and powerful tool to solve challenges in complex systems biology and metabolic engineering, going beyond the realms of physics or chemistry. If this algorithm is advanced and applied to actual large-scale quantum hardware in the future, it is expected to revolutionarily accelerate the production of useful substances using microbial engineering or the discovery of therapeutic targets for intractable metabolic diseases.
Abstract Biological systems, such as cellular metabolism, involve thousands of reactions that together determine how cells grow, respond to their environment, and produce energy. Modeling and analyzing these systems require solving very large mathematical problems that can quickly become computationally prohibitive. To address this challenge, we present a quantum algorithm to analyze metabolic networks, focusing on flux balance analysis as a representative case. We use a quantum interior point method consisting of a quantum subroutine for matrix inversion. Specifically, we reformulate the metabolic optimization problem for efficient execution on a quantum computer using quantum singular value transformation, enabling a rapid solution of complex systems that arise in flux balance analysis. This quantum approach offers a potential computational advantage over classical interior point methods for large and well-conditioned networks. We demonstrate the practical applicability of our method with numerical simulations on the glycolysis and tricarboxylic acid (TCA) cycle network and show that the quantum solution converges to the correct biological objective. This work represents the first application of quantum algorithms to metabolic pathway analysis, establishing a new direction for quantum computational biology and paving the way for quantum approaches to large-scale biological optimization.
• 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 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
• Published in ACS Physical Chemistry Au, introducing an SVD quantum algorithm for open quantum system approaches. • Applied to key quantum biological systems, including excitonic energy transport in the photosynthetic FMO complex and the avian compass radical pair mechanism. • Captures both short- and long-time dynamics accurately... Demonstrating potential as a future tool for simulating complex in vivo nonunitary quantum dynamics. [Quantum Biology Society] A new algorithmic approach capable of modeling and predicting complex nonunitary quantum dynamics inside living organisms using quantum computers has been successfully validated. A research paper titled "Singular Value Decomposition Quantum Algorithm for Quantum Biology" was published in the international journal ACS Physical Chemistry Au. A joint research team led by Professor Kade Head-Marsden from Washington University in St. Louis presented a groundbreaking mathematical framework to solve modeling challenges in quantum biology. ■ Breaking Through Simulation Limits with 'Open Quantum Systems' There has been growing academic interest in algorithms for predicting nonunitary quantum dynamics using current quantum computers. In the field of quantum biology, it is virtually impossible to treat large and complex biological systems entirely as quantum states. However, an open quantum systems approach, which considers continuous interaction with the environment and dissipation, can make these quantum algorithms highly useful. Accordingly, the researchers introduced the Singular Value Decomposition (SVD) quantum algorithm, which helps implement complex nonunitary operators on quantum circuits, and applied it to two major challenges in quantum biology. ■ Secrets of Photosynthesis and Avian Compass Captured by SVD Algorithm The systems the research team applied the SVD algorithm to are: 1) excitonic energy transport through the Fenna-Matthews-Olson (FMO) complex in the photosynthetic process of green sulfur bacteria, and 2) the radical pair mechanism (RPM) of avian navigation, where migratory birds find their way by sensing the Earth's magnetic field. The researchers implemented and analyzed this using the IBM QASM quantum simulator. The results confirmed that the SVD algorithm very accurately captures and reproduces the environment-driven dynamical processes appearing in both biological systems across both short- and long-time quantum dynamics, regardless of the time duration. This study holds great significance in that it demonstrated the potential of the SVD quantum algorithm to become a powerful and effective tool for simulating complex in vivo quantum mechanics as quantum computing advances, even though such complex biological systems face physical limitations for perfect implementation at the current level of noisy intermediate-scale quantum (NISQ) hardware. Experts anticipate that this algorithm will accelerate quantum biology research and play a key role in designing biomimetic quantum technologies and new drug discovery platforms.
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
• 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.