Understanding how living cells move information and energy so quickly and efficiently is a key question in modern biophysics. In this project, I use quantum-inspired computational models to study cellular signaling networks and energy transfer pathways. The main idea is to treat signaling molecules like “walkers” on a graph and compare how signals spread under classical rules versus quantum-inspired rules. Using Python, I built simple graph-based simulations of signaling and energy transfer. In the quantum-inspired version, nodes can be in superposed states and transitions are probabilistic, similar to quantum walks. I then compared how fast and how reliably signals reach target nodes in the two cases. The results suggest that quantum-inspired dynamics can support faster and more robust signal propagation, echoing coherence effects reported in photosynthetic complexes. I also reviewed recent work in quantum biology, biophotonics, and molecular computation to relate the simulations to real biological systems. Finally, I briefly reflect on ethical and societal questions that arise when quantum ideas are applied to biology and cognition. Overall, the project shows how quantum-inspired models can deepen our understanding of biological communication and open new directions in biomedical engineering and computational science.
Understanding how living cells move information and energy so quickly and efficiently is a key question in modern biophysics. In this project, I use quantum-inspired computational models to study cellular signaling networks and energy transfer pathways. The main idea is to treat signaling molecules like “walkers” on a graph and compare how signals spread under classical rules versus quantum-inspired rules. Using Python, I built simple graph-based simulations of signaling and energy transfer. In the quantum-inspired version, nodes can be in superposed states and transitions are probabilistic, similar to quantum walks. I then compared how fast and how reliably signals reach target nodes in the two cases. The results suggest that quantum-inspired dynamics can support faster and more robust signal propagation, echoing coherence effects reported in photosynthetic complexes. I also reviewed recent work in quantum biology, biophotonics, and molecular computation to relate the simulations to real biological systems. Finally, I briefly reflect on ethical and societal questions that arise when quantum ideas are applied to biology and cognition. Overall, the project shows how quantum-inspired models can deepen our understanding of biological communication and open new directions in biomedical engineering and computational science.
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.
The quantum walk is a subset of classical random walks.Considerable research has lately been conducted on quantum walks in relation to quantum computing.The walk may be thought of as a type of quantum cellular automata.In a broader sense, there is a one-to-one link between them.Furthermore, we look at another type of quantum cellular automata that may be thought of as a quantum version of stochastic cellular automata.The quantum walk formalism is a widely used and quite practical paradigm for modelling quantum systems such as Dirac equation simulations, discrete dynamics in both the low and high energy regimes, and the development of a multitude of quantum algorithms.The walker in a QW moves in a quantum superposition of pathways, and the ensuing interference serves as the foundation for a broad range of quantum algorithms, including quantum search, graph isomorphism issues, and rating nodes in a network.To generate a block representation of n-dimensional quantum cellular automata, we apply the following general result to them.As a result, we have demonstrated that their generic, axiomatic formulation nevertheless produces a unifying, operational representation of them.Some non-trivial ramifications are investigated, such as the fact that bijective non-reversible CA are not physical as closed systems, or that quantum information can move faster than classical informationwithin some specified dynamics.Finally, we present n-dimensional quantum cellular automata that can simulate anything else.This means that the initial configuration and local transition rule of any one-dimensional QCA may be stored within the universal QCA's initial configuration.The simulated QCA will then correspond to many phases of the universal QCA.In the sense that each cell in the simulated QCA is represented as a group of nearby cells in the universal QCA, the simulation preserves the topology.Because the encoding is linear, it bears no cost of the calculation.
For many years, the fields of quantum optics and biology have rarely shared a common path. In quantum optics, most of the concepts and techniques developed over the years stand for systems where only a few degrees of freedom are considered and, more importantly, where the systems under study are assumed to be completely isolated from their surrounding environment. This situation is far from what we can find in nature. Biological complexes are, by definition, warm, wet and noisy systems subjected to environmental fluctuations, where quantum phenomena are unlikely to be observed. Notwithstanding, in recent years, this paradigm has begun to be questioned by several works where quantum-mechanical concepts have been introduced in order to describe the dynamics of important biological processes, such as energy transport in photosynthetic light-harvesting complexes. The goal of this thesis is twofold. Firstly, we will investigate how ideas and techniques routinely used in quantum optics can be exploited in order to develop new quantum-based spectroscopy techniques and, secondly, we will examine to what extent microscopic quantum phenomena could impact on the efficient transport behavior of photosynthetic light-havesting complexes. This problem is particularly relevant, because the understanding of the fundamental mechanisms that enable the highly efficient transport of energy in photosynthetic systems could lead us to the design of future quantum-inspired light-harvesting technologies, such as high-efficiency organic solar cells.
Biological neuronal networks represent a fundamental component of complex systems, including the brain. Simulating these networks accurately and efficiently is a significant challenge, largely due to the intricate, non-linear interactions between neurons. Traditional simulation methods, often relying on numerical integration techniques like the Hodgkin-Huxley model, are computationally intensive, particularly when scaling to large networks. This paper proposes a novel approach to neuronal network simulation inspired by quantum mechanics, specifically leveraging concepts of superposition and entanglement. We introduce a framework that utilizes quantum-inspired algorithms to represent and evolve neuronal states, aiming to dramatically reduce computational overhead. The core idea involves mapping neuronal dynamics onto a quantum system, where neurons are represented by qubits, and synaptic connections are modeled as quantum gates. The simulation process then utilizes quantum algorithms to evolve the system, effectively capturing the parallel and interconnected nature of neuronal networks. We present a mathematical formulation of the simulation process, focusing on the evolution equation for neuronal states. We explore key concepts like wave function collapse and measurement in the context of neuronal network simulation, offering a potential pathway for more realistic and efficient modeling of biological neural systems. The ultimate goal is to provide a computationally tractable method for simulating large-scale neuronal networks, leading to improved understanding of brain function and potentially facilitating the development of advanced neurotechnologies. The presented framework offers a distinct alternative to conventional methods, promising significant advantages in terms of speed and scalability.
Many living organisms can exploit quantum mechanical effects to gain distinct biological advantages. In plants, photosynthesis uses quantum coherence to achieve near 100% efficiency in energy transfer. With advances in experimental techniques, two-dimensional electronic spectroscopy can reveal dynamic processes such as coherence and coupling within a system, and it plays an important role in studying energy transfer in photosynthesis. On the theory side, methods such as the generalized Bloch-Redfield theory and the hierarchical equations of motion are used to model photosynthetic systems. Quantum simulation, as a high-efficiency and low-complexity approach, has also made progress across various platforms in the study of photosynthesis. In recent years, a series of studies has introduced quantum coherence into artificial systems to enhance energy transfer efficiency, laying the groundwork for the design of coherent devices with efficient energy transport. Birds can use the weak geomagnetic field and spin-dependent chemical reactions to detect direction. Theoretical frameworks for animal navigation include magnetite-based mechanisms, magnetoreceptor genes, and the radical-pair mechanism. Quantum simulations of navigation have also advanced on multiple platforms. Inspired by animal navigation, diverse quantum effects have been applied to improve sensing and to support navigation tasks. This paper presents a comprehensive review of progress on quantum coherence in photosynthesis and avian navigation, along with related theoretical methods, quantum simulation approaches, and research on quantum coherent devices.
The tantalizing hypothesis that quantum phenomena underpin the brain’s remarkable computational abilities has sparked intense interdisciplinary interest. This study delves into quantum neuroscience by computationally exploring quantum coherence in neural systems, aiming to uncover whether quantum effects enhance information processing. We developed a sophisticated model simulating a 1,000-qubit neural network, with each qubit representing a neuron entangled under biologically relevant conditions (310 K, 0.15 µs coherence time). Using IBM’s Qiskit framework, we tested signal propagation efficiency, latency, and coherence duration across four conditions: quantum models with full, moderate, and high decoherence, and a classical benchmark. Our results reveal a striking 19.4% improvement in signal propagation efficiency in the full-coherence quantum model (95.8% ± 2.9%) compared to the classical model (76.4% ± 5.1%; p < 0.001). Latency was reduced by 31%, with the quantum model achieving 0.68 µs versus 0.98 µs for the classical model. Coherence persisted for up to 1.5 µs, sufficient for short-range neural signaling. Extensive sensitivity analyses, varying temperature (300–325 K), noise (0.01–0.12 µs^-1), and network size (500–1,500 qubits), confirmed robustness, with efficiency remaining above 90% under moderate perturbations. These findings suggest quantum coherence could complement classical neural mechanisms, potentially enhancing processes like sensory integration or consciousness. However, biological complexity, including biochemical interactions, warrants further exploration. We advocate for experimental validation using advanced quantum sensors, such as nitrogen-vacancy centers, to detect coherence in neural tissue. This study bridges quantum physics and neuroscience, offering a robust computational framework to probe the brain’s quantum potential and inspiring future interdisciplinary research into cognition’s mechanistic underpinnings.