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.
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 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.
Whole-cell simulation, modeling all of a cell's functional systems over its life cycle, is an outstanding challenge in computational biology. Even the simplest living cell contains thousands of interacting proteins and metabolites (on the order of trillions of atoms) whose full functional dynamics spans roughly five orders of magnitude in space (nm to μm) and nearly nineteen in time (fs to hours). Further, many of the governing physical and chemical properties remain incompletely characterized. Simulating such complex systems at fully atomistic resolution over a full cell cycle is computationally intractable on classical architectures, raising a central question: Can quantum computing offer a viable path to whole-cell simulations that integrate molecular- and systems-level complexity? This Perspective examines the potential of quantum computing across three hierarchical scales: atomistic-molecular modeling, metabolic and regulatory networks, and whole-cell spatial modeling. We present a complexity analysis comparing classical and quantum algorithms for representative biological problems, identifying regimes of substantial theoretical speedup under specified algorithmic assumptions. We highlight algorithmic developments designed to leverage both near-term exploratory and fault-tolerant quantum architectures, and discuss practical bottlenecks: data encoding overhead, system conditioning, measurement constraints, and hybrid quantum-HPC integration. Together, these results outline a roadmap for quantum-accelerated whole-cell modeling and the biological insights such multiscale frameworks may eventually enable.
Classical computational simulations of biological systems, particularly at the cellular level, face significant limitations due to the exponential growth in computational requirements with system complexity. This work proposes a novel approach to simulating cellular processes utilizing quantum-inspired algorithms, specifically tensor network methods, to overcome these limitations. These algorithms mimic key aspects of quantum computation – superposition and interference – to efficiently model intricate molecular interactions and resulting emergent phenomena. The core idea is to represent complex biological systems using tensor networks, enabling parallel processing and capturing correlations inherent in quantum systems. This approach provides a computationally feasible pathway for simulating dynamic cellular behavior, offering potential breakthroughs in areas such as drug discovery, disease modeling, and understanding fundamental biological mechanisms. The presented framework focuses on the theoretical foundations and algorithmic design, without necessitating access to quantum hardware, making it immediately applicable for high-performance computing environments. We explore the mathematical formalism underpinning the method, detailing the key equations and operations involved in simulating cellular processes. The ultimate goal is to establish a robust and scalable simulation technique capable of tackling the complexities of biological systems.
Quantum simulation offers a revolutionary approach to modeling complex biological systems, particularly metabolic pathways, which currently demand immense computational resources. This paper explores the application of quantum computing to simulate these pathways, focusing on the development of a novel method for dynamically adapting quantum states to mimic the intricate changes inherent within metabolic processes. We propose a quantum circuit design that mimics the key steps of a metabolic pathway, leveraging adaptive quantum states to represent and evolve the energy levels, thereby providing a pathway to significantly enhance the fidelity and accuracy of simulations. The core claim is that this method will enable a more thorough investigation of metabolic dynamics, offering unprecedented insights into biological function and potential therapeutic targets.
We evaluate the quantum resource requirements for ATP/metaphosphate hydrolysis, one of the most important reactions in all of biology with implications for metabolism, cellular signaling, and cancer therapeutics. In particular, we consider three algorithms for solving the ground state energy estimation problem: the variational quantum eigensolver, quantum Krylov, and quantum phase estimation. By utilizing exact classical simulation, numerical estimation, and analytical bounds, we provide a current and future outlook for using quantum computers to solve impactful biochemical and biological problems. Our results show that variational methods, while being the most heuristic, still require substantially fewer overall resources on quantum hardware, and could feasibly address such problems on current or near-future devices. We include our complete dataset of biomolecular Hamiltonians and code as benchmarks to improve upon with future techniques.
• 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.