Abstract Quantum biology seeks to explain biological phenomena via quantum mechanisms, such as enzyme reaction rates via tunneling and photosynthesis energy efficiency via coherent superposition of states. However, less effort has been devoted to study the role of quantum mechanisms in biological evolution. In this paper, we used transcription factor networks with two and four different phenotypes, and used classical random walks (CRW) and quantum walks (QW) to compare network search behavior and efficiency at finding novel phenotypes between CRW and QW. In the network with two phenotypes, at temporal scales comparable to decoherence time T D , QW are as efficient as CRW at finding new phenotypes. In the case of the network with four phenotypes, the QW had a higher probability of mutating to a novel phenotype than the CRW, regardless of the number of mutational steps (i.e., 1, 2 or 3) away from the new phenotype. Before quantum decoherence, the QW probabilities become higher turning the QW effectively more efficient than CRW at finding novel phenotypes under different starting conditions. Thus, our results warrant further exploration of the QW under more realistic network scenarios (i.e., larger genotype networks) in both closed and open systems (e.g., by considering Lindblad terms).
Abstract Quantum biology seeks to explain biological phenomena via quantum mechanisms, such as enzyme reaction rates via tunneling and photosynthesis energy efficiency via coherent superposition of states. However, less effort has been devoted to study the role of quantum mechanisms in biological evolution. In this paper, we used transcription factor networks with two and four different phenotypes, and used classical random walks (CRW) and quantum walks (QW) to compare network search behavior and efficiency at finding novel phenotypes between CRW and QW. In the network with two phenotypes, at temporal scales comparable to decoherence time T D , QW are as efficient as CRW at finding new phenotypes. In the case of the network with four phenotypes, the QW had a higher probability of mutating to a novel phenotype than the CRW, regardless of the number of mutational steps (i.e., 1, 2 or 3) away from the new phenotype. Before quantum decoherence, the QW probabilities become higher turning the QW effectively more efficient than CRW at finding novel phenotypes under different starting conditions. Thus, our results warrant further exploration of the QW under more realistic network scenarios (i.e., larger genotype networks) in both closed and open systems (e.g., by considering Lindblad terms).
We introduce the quantum stochastic walk (QSW), which determines the evolution of a generalized quantum-mechanical walk on a graph that obeys a quantum stochastic equation of motion. Using an axiomatic approach, we specify the rules for all possible quantum, classical, and quantum-stochastic transitions from a vertex as defined by its connectivity. We show how the family of possible QSWs encompasses both the classical random walk (CRW) and the quantum walk (QW) as special cases but also includes more general probability distributions. As an example, we study the QSW on a line and the glued tree of depth three to observe the behavior of the QW-to-CRW transition.
Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i) Markovian classical model, (ii) Markovian-like quantum model, and (iii) hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage) Markov chain-like models of aging, which are mutually coupled.
Abstract The lines separating Biology and Physics have started to blur in recent decades, leading to the emergence of the interdisciplinary field known as quantum Biology. This field looks into the possibility that basic quantum mechanical phenomena such as coherence, entanglement, tunneling, and superposition play crucial roles in biological processes that are beyond the scope of classical Physics. Biology has traditionally worked within the parameters of classical mechanics, presuming that delicate quantum effects cannot endure in the warm, humid, and cacophonous environments of living systems. But new findings in fields like photosynthesis, olfaction, avian magnetoreception, and enzyme catalysis indicate otherwise, suggesting that living things intentionally and possibly advantageously use quantum principles. From the earliest speculative theories to the most recent experimental confirmations, this book chapter methodically examines the evolution of quantum Biology. We investigate how the exceptionally high energy transfer efficiency in photosynthetic complexes particularly in the light-harvesting proteins of green sulfur bacteria and plants is supported by quantum coherence. The remarkable speed of proton transfers and enzymatic reactions during DNA replication is also investigated in relation to quantum tunneling. The chapter explores magnetoreception in migratory birds and suggests models in which sensitivity to Earth's geomagnetic field is made possible by radical pair mechanisms influenced by quantum entanglement. We also discuss the intriguing but controversial idea that quantum vibrations improve the human olfactory system's capacity for discrimination. In addition to examining particular occurrences, this research challenges more general theoretical models that seek to balance quantum mechanics with the decoherence commonly observed in biological settings. Recent developments in quantum information theory, biomimetic quantum technologies, and ultrafast spectroscopy are highlighted as essential instruments advancing empirical research. We also critically evaluate the field's methodological difficulties, the skepticism of overly optimistic interpretations, and the significant philosophical ramifications of acknowledging life as a quantum phenomenon. In conclusion, quantum Biology suggests that quantum effects are not uncommon aberrations but rather are an essential part of life, offering not only a fresh perspective for reinterpreting well-known biological processes but also the potential for a fundamental paradigm shift. Quantum biology has the potential to completely transform biotechnology, medicine, and our comprehension of consciousness and the origin of life as experimental methods advance. This chapter highlights the significance of interdisciplinary cooperation in order to completely comprehend the latest discoveries and possible future paths of quantum Biology, highlighting its revolutionary influence on our comprehension of life at the most basic levels. Key words: Quantum Biology, tunneling, avian magnetoreception, enzyme catalysis
The exponential growth of genomic data has transformed evolutionary biology, enabling large-scale analysis of genetic variation, phylogenetic relationships, and adaptive mechanisms across species. However, classical computational approaches increasingly struggle with the combinatorial complexity, high dimensionality, and nonlinear interactions inherent in genomic datasets. This study explores how quantum-enhanced data analysis can augment evolutionary biology by enabling more efficient modeling of genomic variation and species adaptation. By leveraging quantum computing principles such as superposition, entanglement, and quantum parallelism, quantum algorithms offer the potential to accelerate sequence alignment, genotype–phenotype mapping, evolutionary optimization, and adaptive landscape exploration. Through qualitative synthesis of research in quantum computing, computational genomics, and evolutionary theory, this paper examines how quantum-enhanced frameworks can improve the detection of adaptive patterns, epistatic interactions, and evolutionary constraints. The analysis highlights potential applications in phylogenomic reconstruction, population genetics, and predictive modeling of species responses to environmental pressures. While acknowledging current technological limitations, the study argues that quantum-enhanced genomic analysis represents a promising pathway toward more scalable, precise, and predictive evolutionary models. Ultimately, this work positions quantum computing as a transformative tool for advancing our understanding of biological evolution and species resilience in rapidly changing ecosystems.
Extreme events are the ones for which the magnitude of the event is much larger than its typical values. They have been extensively investigated on classical dynamical and stochastic processes on networks. In this work, in analogy with well-studied extreme events of random walks, the extreme events of quantum walks on regular lattices, a scale-free network, and Erdős-Rényi network are studied. Due to unitary quantum evolution, the quantum walk dynamics usually differs from that of the classical random walks, and hence we expect extreme event properties to be different in classical and quantum settings. In contrast to this expectation, we report that extreme events of phase coherent quantum walks are qualitatively similar to that of classical random walks. We show that this counterintuitive behavior originates from the dominance of nodal fluctuations over internode correlations in disordered network structures. In particular, the occurrence probability for extreme events on scale-free and Erdős-Rényi networks displays a decaying power law with the degree of nodes. That is, the extreme event probability is larger for small degree nodes compared to the hubs on the network, in qualitative agreement with corresponding classical random walk results. Further, it is shown that extreme event probability scales with the threshold used to define extreme events.
In the emerging field of quantum biology, we begin to see how biological systems exploit quantum mechanics as their efficiency and certain phenomena such as photosynthesis and enzymatic reactions cannot be explained only by classical physics. This paper will shortly review the foundations and axioms of quantum mechanics including wave particle duality, Heisenburg's uncertainty principle and the probabilistic interpretation of a wave function before exploring the first primary topic-quantum tunneling in enzymes. The paper will then provide deeper insights into quantum effects in photosynthesis including coherence, entanglement and the photoelectric effect. Additionally, quantum biology will have profound implications on the future of quantum technology. Leveraging the information scientists have gained about the quantum mechanical functions of biological systems, this paper will delve into the applications of this know-how in quantum technology.