• Published in AIP Publishing's APL Quantum, fully applying digital quantum simulation (DQS) to spin biology. • Successfully implemented the quantum spin dynamics of the radical pair mechanism utilizing the 'Trotterization' technique. • Analyzed the noise limits of actual quantum hardware and demonstrated the potential of utilizing quantum computing for the future development of new drugs targeting complex biomolecules. [Quantum Biology Society] In the emerging field of quantum biology, simulating spin-chemistry systems with actual biological complexity has posed computational limitations that are difficult to overcome with conventional classical computers. This is because as the number of elements constituting the spin system increases, the dimension of the Hilbert space required for computation grows exponentially. Recently, a study simulating biological phenomena using an actual digital quantum computer was published to resolve this problem, drawing the attention of the academic community. A paper titled "Simulating spin biology using a digital quantum computer: Prospects on a near-term quantum hardware emulator" was published on August 19 in APL Quantum, a journal of AIP Publishing. This research was led by a multinational joint research team including Pedro H. Alvarez and Clarice D. Aiello. ■ Mapping the Radical Pair Mechanism via Trotterization The radical pair mechanism (RPM), represented by the magnetic field sensing (magnetoreception) ability of migratory birds, centers on the quantum entanglement and coherence phenomena of electron spins in vivo. The research team introduced the Trotterization technique to directly apply gate-based digital quantum simulation (DQS) to biological systems. Through this technique, they successfully mapped the coherent spin dynamics of radical pairs onto quantum circuits. In ideal, noise-free quantum circuit simulations, they obtained accurate spin dynamics results consistent with the conventional classical Haberkorn master equation approach using only about 15 Trotter steps. ■ Analysis of Quantum Hardware Noise Limits and Future Prospects Furthermore, going beyond ideal environments, the research team conducted an in-depth analysis of the impact of realistic error models on simulations using a near-term (NISQ) quantum hardware emulator that mimics the noise profile of an IBM quantum device (e.g., ibmq_jakarta). The analysis revealed that realistic limitations were observed due to the constrained performance and noise (such as phase damping and amplitude damping) of current quantum hardware, including a shift in singlet yield and a decreased ability to sense magnetic field anisotropy. However, the researchers showed that by fitting an analytical model to the measured noisy data, the overall dynamical shape depending on the magnetic field angle could be successfully derived. In addition, they presented a practical upper limit for the number of Trotter steps that can be directly run on quantum devices if error mitigation techniques are introduced in the future. Unlike conventional classical computing methods that face exponential limits, the DQS approach possesses the potential to efficiently model biological spin systems simply through linear resource scaling by adding qubits. This study is evaluated as a groundbreaking first step demonstrating that digital quantum simulation can be utilized as an innovative tool for new drug development targeting complex biomolecules and ultra-precise biosensing modeling by directly calculating the entanglement and coherence of biological phenomena.
• Published in AIP Publishing's APL Quantum, fully applying digital quantum simulation (DQS) to spin biology. • Successfully implemented the quantum spin dynamics of the radical pair mechanism utilizing the 'Trotterization' technique. • Analyzed the noise limits of actual quantum hardware and demonstrated the potential of utilizing quantum computing for the future development of new drugs targeting complex biomolecules. [Quantum Biology Society] In the emerging field of quantum biology, simulating spin-chemistry systems with actual biological complexity has posed computational limitations that are difficult to overcome with conventional classical computers. This is because as the number of elements constituting the spin system increases, the dimension of the Hilbert space required for computation grows exponentially. Recently, a study simulating biological phenomena using an actual digital quantum computer was published to resolve this problem, drawing the attention of the academic community. A paper titled "Simulating spin biology using a digital quantum computer: Prospects on a near-term quantum hardware emulator" was published on August 19 in APL Quantum, a journal of AIP Publishing. This research was led by a multinational joint research team including Pedro H. Alvarez and Clarice D. Aiello. ■ Mapping the Radical Pair Mechanism via Trotterization The radical pair mechanism (RPM), represented by the magnetic field sensing (magnetoreception) ability of migratory birds, centers on the quantum entanglement and coherence phenomena of electron spins in vivo. The research team introduced the Trotterization technique to directly apply gate-based digital quantum simulation (DQS) to biological systems. Through this technique, they successfully mapped the coherent spin dynamics of radical pairs onto quantum circuits. In ideal, noise-free quantum circuit simulations, they obtained accurate spin dynamics results consistent with the conventional classical Haberkorn master equation approach using only about 15 Trotter steps. ■ Analysis of Quantum Hardware Noise Limits and Future Prospects Furthermore, going beyond ideal environments, the research team conducted an in-depth analysis of the impact of realistic error models on simulations using a near-term (NISQ) quantum hardware emulator that mimics the noise profile of an IBM quantum device (e.g., ibmq_jakarta). The analysis revealed that realistic limitations were observed due to the constrained performance and noise (such as phase damping and amplitude damping) of current quantum hardware, including a shift in singlet yield and a decreased ability to sense magnetic field anisotropy. However, the researchers showed that by fitting an analytical model to the measured noisy data, the overall dynamical shape depending on the magnetic field angle could be successfully derived. In addition, they presented a practical upper limit for the number of Trotter steps that can be directly run on quantum devices if error mitigation techniques are introduced in the future. Unlike conventional classical computing methods that face exponential limits, the DQS approach possesses the potential to efficiently model biological spin systems simply through linear resource scaling by adding qubits. This study is evaluated as a groundbreaking first step demonstrating that digital quantum simulation can be utilized as an innovative tool for new drug development targeting complex biomolecules and ultra-precise biosensing modeling by directly calculating the entanglement and coherence of biological phenomena.
Understanding the intricate quantum spin dynamics of radical pair reactions is crucial for unraveling the underlying nature of chemical processes across diverse scientific domains. In this work, we leverage Trotterization to map coherent radical pair spin dynamics onto a digital gate-based quantum simulation. Our results demonstrated an agreement between the idealized noiseless quantum circuit simulations and established master equation approaches for homogeneous radical pair recombination, identifying ∼15 Trotter steps to be sufficient for faithfully reproducing the coupled spin dynamics of a prototypical system. By utilizing this computational technique to study the dynamics of spin systems of biological relevance, our findings underscore the potential of digital quantum simulation (DQS) of complex radical pair reactions and builds the groundwork toward more utilitarian investigations into their intricate reaction dynamics. We further investigate the effect of realistic error models on our DQS approach and provide an upper limit for the number of Trotter steps that can currently be applied in the absence of error mitigation techniques before losing simulation accuracy to deleterious noise effects.
• 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.
• 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.
The Radical Pair Mechanism, a process by which magnetic fields modulate the kinetics of chemical reactions by influencing spin dynamics, is thought to underlie a variety of biological mechanisms and is inherently both quantum and noisy. Model simulations with limited electron-nuclear interactions have been conducted on classical devices resulting in experimentally verified final state results, however these simulations become too complex to simulate as the systems scale in size. This motivates us to propound the simulation of the relevant spin dynamics in a noisy quantum device to probe the currently unexplored open quantum systems aspects of the radical pair mechanism and to provide a novel theoretical tool for us to be able to tackle bottlenecks in simulating larger and more physically relevant biological spin systems. We employ a hybrid quantum machine learning approach where we evaluate a parametrized quantum circuit representing the evolution of the system on the quantum device and then update the parameters according to our objective cost function on a classical computer using a gradient optimizer and a noisy gradient free (SPSA) optimizer. We show that a 3 qubit circuit with a series of parametrized rotation gates representing the Hyperfine and Zeeman interactions can be optimized, resulting in a circuit that evolves a singlet state under different magnetic field angles. Moreover, we show that the noisy gradient free optimizer is better suited for optimizing on a noisy quantum device. However, the precision in the quantum circuit outputs is only sub-par at present, leaving room for further improvements.
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 Chemical Science of the Royal Society of Chemistry, successfully calculating the electron transfer process in the cryptochrome protein (ErCRY4) using quantum-classical hybrid computing. • Overcame the noise limits of current quantum hardware (NISQ) by introducing the VQE-PDFT method and a shallow-depth tailored quantum circuit (HEA). • Demonstrated that systematic errors caused by quantum noise are cancelled out when applying Marcus theory to calculate energy differences, presenting a practical algorithmic breakthrough. [Quantum Biology Society] Simulating strongly correlated electronic structure changes occurring inside complex biological proteins has been a daunting challenge, not only for conventional classical computers but also for current error-prone quantum computer (NISQ) devices. Recently, an innovative hybrid modeling technology combining only the strengths of quantum hardware and classical computers has been demonstrated, drawing attention from the academic community. A paper titled "Quantum-classical hybrid computation of electron transfer in a cryptochrome protein via VQE-PDFT and multiscale modeling" was published in the international journal Chemical Science, published by the Royal Society of Chemistry (RSC). A joint research team, including Dr. Jun-Han Huang of BGI Research and Professor Weitang Li of The Chinese University of Hong Kong, Shenzhen, presented a practical quantum algorithm for simulating biological electron transfer through this study. ■ The Meeting of Quantum and Classical, 'VQE-PDFT' Multiscale Architecture The research team targeted the electron transfer process between two tryptophan amino acids (TrpB, TrpC) within the cryptochrome (ErCRY4) of the European robin, known as the magnetic field-sensing protein of migratory birds. To compute this, they developed a hybrid framework called 'VQE-PDFT' (variational quantum eigensolver-pair-density functional theory). This approach is an efficient strategy that solves only the most complex multiconfigurational electronic correlations (static correlation) using the VQE algorithm on a quantum computer, while handling the remaining dynamic correlations with pair-density functional theory (PDFT) on a classical computer. Furthermore, they integrated multiscale QM/MM modeling, where the core region where the reaction occurs is calculated using quantum mechanics (QM), and the surrounding protein and solvent environment are treated with classical molecular mechanics (MM). ■ The Magic of Shallow-Depth Quantum Circuits and Error Cancellation To overcome the noise limitations of current quantum hardware, the researchers specially designed a shallow hardware-efficient ansatz (HEA) circuit with a depth of 4 to 6, optimized for the tryptophan structure, instead of the existing heavy quantum circuit (ROUCCSD) that reaches a depth of 35. In particular, the most groundbreaking discovery of this study emerged during the error analysis process. Although systematic errors occur in single-point energies calculated on noisy quantum devices, they demonstrated that these errors are dramatically cancelled out (error cancellation) during the process of calculating the energy difference between two states to obtain electron transfer parameters (reorganization energy and driving force) according to Marcus theory. ■ Successful Demonstration on a Superconducting Quantum Processor Thanks to this error cancellation effect, the electron transfer rate predictions obtained by the research team running the actual circuit on a 13-qubit superconducting quantum hardware matched very well with ideal noise-free simulator values and actual experimental ultrafast spectroscopy measurement results. This achievement is a practical milestone showing that the complex spin dynamics and electron transfer phenomena of biomolecules can be reliably calculated even with highly constrained quantum computers in the NISQ era. It is expected to provide a clear algorithmic breakthrough on how to utilize quantum technology in actual biological and chemical platforms, such as new drug discovery and massive enzyme complex analysis based on quantum computing in the future.