There is growing awareness that the success of pharmacologic interventions on living organisms is significantly impacted by context and timing of exposure. In turn, this complexity is leading to an increasing focus on regulatory network dynamics in biology and our ability to represent these at a high level of fidelity, in silico. Logic network models continue to show great promise in this domain and their parameter estimation can be formulated as a constraint satisfaction problem (CSP) that is especially well-suited for the often sparse and incomplete data associated with biology. Unfortunately, even in the case of Boolean logic, the combinatorial complexity of these problems grows rapidly, challenging our ability to create such models at physiologically-relevant scales. That said, quantum computing, while still nascent, facilitates novel information-processing paradigms with the potential for transformative impact in problems such as this one. In this work, we take a first step at actualizing this potential by identifying the structure and Boolean decisional logic of a well-studied regulatory network linking five proteins involved in the neural development of the mammalian cortical area of the brain. We identify the protein-protein connectivity and binary decisional logic governing this network by formulating it as a Boolean Satisfiability (B-SAT) problem. We then employ Grover's algorithm to solve the NP-hard problem faster than the exponential time complexity required by deterministic classical algorithms. Using approaches deployed on both quantum simulators and actual noisy intermediate scale quantum (NISQ) hardware, we accurately recover several highlikelihood models from very sparse protein expression data. The results highlight the differential roles of various data types in supporting accurate models; the impact of quantum algorithm design as it pertains to the mutability of quantum hardware, and the opportunities for accelerated discovery enabled by this approach.
There is growing awareness that the success of pharmacologic interventions on living organisms is significantly impacted by context and timing of exposure. In turn, this complexity is leading to an increasing focus on regulatory network dynamics in biology and our ability to represent these at a high level of fidelity, in silico. Logic network models continue to show great promise in this domain and their parameter estimation can be formulated as a constraint satisfaction problem (CSP) that is especially well-suited for the often sparse and incomplete data associated with biology. Unfortunately, even in the case of Boolean logic, the combinatorial complexity of these problems grows rapidly, challenging our ability to create such models at physiologically-relevant scales. That said, quantum computing, while still nascent, facilitates novel information-processing paradigms with the potential for transformative impact in problems such as this one. In this work, we take a first step at actualizing this potential by identifying the structure and Boolean decisional logic of a well-studied regulatory network linking five proteins involved in the neural development of the mammalian cortical area of the brain. We identify the protein-protein connectivity and binary decisional logic governing this network by formulating it as a Boolean Satisfiability (B-SAT) problem. We then employ Grover's algorithm to solve the NP-hard problem faster than the exponential time complexity required by deterministic classical algorithms. Using approaches deployed on both quantum simulators and actual noisy intermediate scale quantum (NISQ) hardware, we accurately recover several highlikelihood models from very sparse protein expression data. The results highlight the differential roles of various data types in supporting accurate models; the impact of quantum algorithm design as it pertains to the mutability of quantum hardware, and the opportunities for accelerated discovery enabled by this approach.
Describing gene interactions in cells is challenging due to their complexity and the limited microscopic detail available. Boolean networks offer a powerful, coarse-grained approach to modeling these dynamics using binary agents and their interactions. In this context, attractors-stable states of the system-are associated with biological phenotypes, making their identification biologically important. However, traditional computing struggles with the exponential growth of the state space in such models. Here, we present a novel quantum search algorithm for identifying attractors in synchronous Boolean networks, specifically designed for use on quantum computers. The algorithm iteratively suppresses known attractor basins, increasing the probability of detecting new ones. Unlike classical methods, it guarantees the discovery of a new attractor in each run. Early tests demonstrate strong resilience to noise on current NISQ (noisy intermediate-scale quantum) devices, marking a promising advance toward practical quantum-enhanced biological modeling.
The dynamics of cellular mechanisms can be investigated through the analysis of networks. One of the simplest but most popular modeling strategies involves logic-based models. However, these models still face exponential growth in simulation complexity compared with a linear increase in nodes. We transfer this modeling approach to quantum computing and use the upcoming technique in the field to simulate the resulting networks. Leveraging logic modeling in quantum computing has many benefits, including complexity reduction and quantum algorithms for systems biology tasks. To showcase the applicability of our approach to systems biology tasks, we implemented a model of mammalian cortical development. Here, we applied a quantum algorithm to estimate the tendency of the model to reach particular stable conditions and further revert dynamics. Results from two actual quantum processing units and a noisy simulator are presented, and current technical challenges are discussed.
Computational methods in drug discovery significantly reduce both time and experimental costs. Nonetheless, certain computational tasks in drug discovery can be daunting with classical computing techniques which can be potentially overcome using quantum computing. A crucial task within this domain involves the functional classification of proteins. However, a challenge lies in adequately representing lengthy protein sequences given the limited number of qubits available in existing noisy quantum computers. We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various proteinrelated machine-learning problems. We classify proteins based on their sub-cellular locations—a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. It’s noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.
Computational methods in drug discovery significantly reduce both time and experimental costs. Nonetheless, certain computational tasks in drug discovery can be daunting with classical computing techniques which can be potentially overcome using quantum computing. A crucial task within this domain involves the functional classification of proteins. However, a challenge lies in adequately representing lengthy protein sequences given the limited number of qubits available in existing noisy quantum computers. We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various protein-related machine-learning problems. We classify proteins based on their sub-cellular locations—a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. It’s noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.
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.
• 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.