Proteins and other macromolecules may be viewed as shape-based (or tactile) pattern recognizers. Biological cells exploit this inherent capability by transducing macroscopic signal patterns impinging on the external membrane to microscopic patterns at the molecular level, via second messengers. The parallelism inherent in the wave function description of these microscale processes in effect serves to increase the computing power of molecular computing systems as compared to macroscopic analogs. The conversion and recognition process is highly reminiscent of measurement. The linking role of second messengers allows macroscopic signals to set the state of the cell (in analogy to state preparation), while enzymatic readout and control of cellular behavior is an amplification process that corresponds to measurement of the microstate at a later point of time. Since the standard time evolution equations are reversible and unitary, while measurement is not, it is conceivable that the study of molecular computing will lead to new insights into the relation between the microworld and the macroworld. The model presented suggests that projection processes that are masked in ordinary laboratory systems are brought to macroscopic significance by the highly nonlinear chain of amplification events in the biological cell.
Proteins and other macromolecules may be viewed as shape-based (or tactile) pattern recognizers. Biological cells exploit this inherent capability by transducing macroscopic signal patterns impinging on the external membrane to microscopic patterns at the molecular level, via second messengers. The parallelism inherent in the wave function description of these microscale processes in effect serves to increase the computing power of molecular computing systems as compared to macroscopic analogs. The conversion and recognition process is highly reminiscent of measurement. The linking role of second messengers allows macroscopic signals to set the state of the cell (in analogy to state preparation), while enzymatic readout and control of cellular behavior is an amplification process that corresponds to measurement of the microstate at a later point of time. Since the standard time evolution equations are reversible and unitary, while measurement is not, it is conceivable that the study of molecular computing will lead to new insights into the relation between the microworld and the macroworld. The model presented suggests that projection processes that are masked in ordinary laboratory systems are brought to macroscopic significance by the highly nonlinear chain of amplification events in the biological cell.
Our recently proposed quantum approach to biomolecular recognition processes is hereby additionally supported by biomolecular Resonant Recognition Model and by quantum-chemical theory of biomolecular non-radiative resonant transitions. Previously developed general quantumdecoherence framework for biopolymer conformational changes in very selective ligandproteins/ target-receptors key/lock biomolecular recognition processes (with electron-conformational coupling, giving rise to dynamical modification of many-electron energy-state hypersurface of the cellular quantum-ensemble ligand-proteins/target-receptors biomolecular macroscopic quantum system, with revealed possibility to consider cellular biomolecular recognition as a Hopfield-like quantum-holographic associative neural network) is further extended from nonlocal macroscopicquantum level of biological cell to nonlocal macroscopic-quantum level of biological organism, based on long-range coherent microwave excitations (as supported by macroscopic quantum-like microwave resonance therapy of the acupuncture system) - which might be of fundamental importance in understanding of underlying macroscopic quantum (quantum-holographic Hopfieldlike) control mechanisms of embryogenesis/ontogenesis and morphogenesis, and their backward influence on the expression of genes.
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.
In the context of the macroscopic quantum phenomena of the second kind, we hereby seek for a solution-in-principle of the long standing problem of the polymer folding, which was considered by Levinthal as (semi)classically intractable. To illuminate it, we applied quantum-chemical and quantum decoherence approaches to conformational transitions. Our analyses imply the existence of novel macroscopic quantum biomolecular phenomena, with biomolecular chain folding in an open environment considered as a subtle interplay between energy and conformation eigenstates of this biomolecule, governed by quantum-chemical and quantum decoherence laws. On the other hand, within an open biological cell, a system of all identical (noninteracting and dynamically noncoupled) biomolecular proteins might be considered as corresponding spatial quantum ensemble of these identical biomolecular processors, providing spatially distributed quantum solution to a single corresponding biomolecular chain folding, whose density of conformational states might be represented as Hopfield-like quantum-holographic associative neural network too (providing an equivalent global quantum-informational alternative to standard molecular-biology local biochemical approach in biomolecules and cells and higher hierarchical levels of organism, as well).
Any computation is facilitated by some physical process, and the observable quantities of any physical process can be viewed as a computation. These close ties suggest that the study of what universal computers are capable of may lead to additional insight about the physical universe, and vice versa. In his thesis, we explore three lines of research that are linked to this central theme. The first partition shows how notions of non-computability and undecidability eventually led to evidence of emergence, the concept that even if a ‘theory of everything’ governing all microscopic interactions were discovered, the understanding of macroscopic order is likely to require additional insights. The second partition proposes a physically motivated model of computation that relates quantum complexity, quantum optimal control, and Riemannian geometry. Thus insights in any one of these disciplines could also lead to insights in the others. The remainder of this partition explores a simple application of these relations. The final partition proposes a model of quantum computation that generalizes measurement based computation to continuous variables. We outline its optical implementation, whereby any computation can be performed by single mode measurements on a resource state that can be prepared by passing a collection of squeezed states through a beamsplitter network.
The smallest details of living systems are molecular devices that operate between the classical and quantum levels, i.e. between the potential dimension (microscale) and the actual three-dimensional space (macroscale). They realize non-demolition quantum measurements in which time appears as a mesoscale dimension separating contradictory statements in the course of actualization. These smaller devices form larger devices (macromolecular complexes), up to living body. The quantum device possesses its own potential internal quantum state (IQS), which is maintained for prolonged time via error-correction being a reflection over this state. Decoherence-free IQS can exhibit itself by a creative generation of iteration limits in the real world. To avoid a collapse of the quantum information in the process of correcting errors, it is possible to make a partial measurement that extracts only the error-information and leaves the encoded state untouched. In natural quantum computers, which are living systems, the error-correction is internal. It is a result of reflection, given as a sort of a subjective process allotting optimal limits of iteration. The IQS resembles the properties of a quasi-particle, which interacts with the surround, applying decoherence commands to it. In this framework, enzymes are molecular automata of the extremal quantum computer, the set of which maintains stable highly ordered coherent state, and genome represents a concatenation of error-correcting codes into a single reflective set. Biological systems, being autopoietic in physical space, control quantum measurements in the physical universe. The biological evolution is really a functional evolution of measurement constraints in which limits of iteration are established possessing criteria of perfection and having selective values.