Debate exists over whether or not adherence to quantum statistical mechanics and emulation of quantum information properties are sufficient criteria for biocomputations to be classified as quantum processes. A noteworthy example at the scale of intact life forms making social decisions can be found in “lower” eukaryotes. Ciliates learn to group Ca2+-dependent behavioral strategies into heuristics which they then use to signal mating status after physical contacts from presumed suitors and rivals. The time taken by ciliates to find appropriate strategies stored within behavioral repertoires diminishes with experience. Improvements in strategy search speeds by experts resemble the root-rate performance of Grover’s quantum search algorithm over classical processes. Ciliates putatively implement fast strategy search algorithms by learning to change the reaction kinetics of mechanically activated Ca2+-induced Ca2+ waves that travel through different cellular compartments to preferentially trigger over-learned behavioral sequences. Fire-diffuse-fire models demonstrate wave-conduction velocities most suited for quadratic increases in strategy search speeds are sensitive to limitations imposed by Ca2+ release times and distances between Ca2+ storage sites. These reaction-diffusion computations provide an interesting contrast to physicochemical quantum events described by equations containing quantum diffusion terms, as a modifiable classical diffusion coefficient solely accounts for root-rate processing efficiency. Fast chemical events underlying quantum information processing schemes in live biological systems can thus counter-intuitively exert their effects via thermodynamically vulnerable reaction parameters. The ubiquity and biological importance of intracellular Ca2+-induced Ca2+ cascades across taxa suggests bacteria to mammals might likewise learn to use quantum-level processing when planning and executing behavioral strategies.
NeuroQuantology | December 2012 | Volume 10 | Issue 4 | Page 646-654
Debate exists over whether or not adherence to quantum statistical mechanics and emulation of quantum information properties are sufficient criteria for biocomputations to be classified as quantum processes. A noteworthy example at the scale of intact life forms making social decisions can be found in “lower” eukaryotes. Ciliates learn to group Ca2+-dependent behavioral strategies into heuristics which they then use to signal mating status after physical contacts from presumed suitors and rivals. The time taken by ciliates to find appropriate strategies stored within behavioral repertoires diminishes with experience. Improvements in strategy search speeds by experts resemble the root-rate performance of Grover’s quantum search algorithm over classical processes. Ciliates putatively implement fast strategy search algorithms by learning to change the reaction kinetics of mechanically activated Ca2+-induced Ca2+ waves that travel through different cellular compartments to preferentially trigger over-learned behavioral sequences. Fire-diffuse-fire models demonstrate wave-conduction velocities most suited for quadratic increases in strategy search speeds are sensitive to limitations imposed by Ca2+ release times and distances between Ca2+ storage sites. These reaction-diffusion computations provide an interesting contrast to physicochemical quantum events described by equations containing quantum diffusion terms, as a modifiable classical diffusion coefficient solely accounts for root-rate processing efficiency. Fast chemical events underlying quantum information processing schemes in live biological systems can thus counter-intuitively exert their effects via thermodynamically vulnerable reaction parameters. The ubiquity and biological importance of intracellular Ca2+-induced Ca2+ cascades across taxa suggests bacteria to mammals might likewise learn to use quantum-level processing when planning and executing behavioral strategies.
Grover's quantum (search) algorithm exploits principles of quantum information theory and computation to surpass the strong Church-Turing limit governing classical computers. The algorithm initializes a search field into superposed N (eigen)states to later execute nonclassical "subroutines" involving unitary phase shifts of measured states and to produce root-rate or quadratic gain in the algorithmic time (O(N (1/2))) needed to find some "target" solution m. Akin to this fast technological search algorithm, single eukaryotic cells, such as differentiated neurons, perform natural quadratic speed-up in the search for appropriate store-operated Ca(2+) response regulation of, among other processes, protein and lipid biosynthesis, cell energetics, stress responses, cell fate and death, synaptic plasticity, and immunoprotection. Such speed-up in cellular decision making results from spatiotemporal dynamics of networked intracellular Ca(2+)-induced Ca(2+) release and the search (or signaling) velocity of Ca(2+) wave propagation. As chemical processes, such as the duration of Ca(2+) mobilization, become rate-limiting over interstore distances, Ca(2+) waves quadratically decrease interstore-travel time from slow saltatory to fast continuous gradients proportional to the square-root of the classical Ca(2+) diffusion coefficient, D (1/2), matching the computing efficiency of Grover's quantum algorithm. In this Hypothesis and Theory article, I elaborate on these traits using a fire-diffuse-fire model of store-operated cytosolic Ca(2+) signaling valid for glutamatergic neurons. Salient model features corresponding to Grover's quantum algorithm are parameterized to meet requirements for the Oracle Hadamard transform and Grover's iteration. A neuronal version of Grover's quantum algorithm figures to benefit signal coincidence detection and integration, bidirectional synaptic plasticity, and other vital cell functions by rapidly selecting, ordering, and/or counting optional response regulation choices.
Every process of our universe is based on the fundamental elements of nature: information and computation.The basic motivation behind the study of quantum cellular automata (QCA) is the wish to analyze the processes of nature.QCA provide a natural framework within which to describe many classically undescribable and uncomputable physical phenomena, such as the properties of quantum physical systems and the complex background of quantum dynamics.Quantum cellular automata models are based on the working mechanism of classical cellular automata models and use the power of reversible quantum computation.Cellular automata can be used in many fields of science, such as parallel computation, artificial intelligence, image processing, biological systems, simulation of physics systems, hardware design, algorithm theory, and many more.In the first part of this chapter, we give a brief overview of the basic properties of quantum information processing and analyze the quantum versions of classical cellular automata models.We present all materials in a clear, perspicuous, and comprehensible manner, without using a complex mathematical background.After reviewing physical QCA implementations, we sketch future directions, and then conclude the first part.In the second part of the chapter, we examine one possible application of QCA, which uses quantum computing to realize real-life based, truly random network organization.This abstract machine is called a Quantum Cellular Machine (QCM), and we design it for controlling a truly random biologically-inspired network, and to integrate quantum learning algorithms and quantum searching into a controlled, self-organizing system.The selforganizing processes in classical systems cannot be truly random.Using our quantum probabilistic QCM unit, we can add truly random behavior to the self-organizing processes of biological networks.A quantum mechanical-based quantum cellular machine (QCM) controls the self-organizing processes of the network and uses a closed, non-classical quantum mechanical-based language inside the QCM.The proposed QCM solution has deep relevance in the evolution of truly random quantum probabilistic self-organizing network structures.The QCM controls the evolution of the system, changes its environment and creates plans without any human interaction, using truly random quantum probabilistic decisions.In a classical system, the classical circuits can only exhibit deterministic behavior.In a quantum probabilistic control system, the quantum circuits can follow both deterministic and quantum probabilistic quantum cellular machine control behaviors.The QCM has classical and quantum communication layers, it uses the classical layer to detect www.intechopen.comCellular Automata -Innovative Modelling for Science and Engineering 114 the network environment.The lower layer of the quantum cellular machine contains the non-deterministic quantum probabilistic decisions and interacts with the classical level.The quantum cellular machine model with the power of quantum computing can be used for the development of a real-life based network organism.We show, that a real, biologically inspired, non-deterministic, truly random network model can be achieved by the discussed QCM model.In the third part of this chapter, we show that a very efficient quantum searching algorithm can be integrated into the QCM, to find the best solution to a given network input command.We present a quantum searching based method specially designed for quantum probabilistic self-organizing networks, to reduce the complexity of the classical traditional search in the network.The proposed QCM can process both quantum and classical information, and accomplish both deterministic and quantum probabilistic tasks.The information unit is a quantum bit, which can lie in a coherent superposition state of logical states zero and one, and can thus simultaneously store zero and one.Using quantum bits, we can speed up the solutions of classical problems, and even solve some hard problems that classical computers can't solve.The key aspect behind the optimal decisions of a QCM is to design a high-efficiency searching algorithm.A QCM updates the probability amplitudes of its quantum register, according to a given reward value, derived from the network environment.The QCM repeatedly applies a unitary transformation to the quantum states, thus it can enhance, for example, the probability amplitude of the optimal path in the self-organizing network environment, while suppressing the amplitude of all other solutions.The QCM's quantum searching algorithm can help resolve many hard tasks, for example it could be applied to find an optimal logical path, using the effects of quantum mechanics.In the numerical analysis we will show that the quantum communication layer could improve the performance of classical systems.
Computational modeling of cellular systems, where reactants are governed by biochemical equations and physical representations, requires extensive classical computing resources. These limitations significantly constrain the system size and spatiotemporal scales of simulations. A key challenge lies in the "curse of dimensionality", where the number of possible reaction terms grows exponentially with the number of species, and the computation of reaction rates involving many-body interactions becomes intractable in polynomial time on classical computers. In this work, we introduce a quantum algorithmic framework designed to overcome these challenges, leveraging the architecture of quantum computing to simultaneously compute reaction rates and track the spatiotemporal dynamical evolutions of subcellular systems. We generalize the reaction-diffusion equation (RDE) for multiscale systems with arbitrary species count, encompassing higher-order interactions. Our approach achieves two principal quantum advantages: (i) an exponential quantum speedup in reaction-rate computation, contingent on the efficient preparation of polynomially accurate ground states on a quantum computer, and (ii)) a quadratic scaling in spatial grid points and polynomial scaling in the number of species for solving nonlinear RDEs, contrasting sharply with classical methods that scale exponentially with the system's degrees of freedom. To our knowledge, this represents the first efficient quantum algorithm for solving multiscale reaction-diffusion systems. This framework opens the door to simulations of biologically relevant subcellular processes across previously inaccessible spatial and temporal scales, with profound implications for computational biology, soft matter physics, and biophysical modeling.
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.
Quantum mechanical models have been proposed for biological processes and for cognition and decision in domains that appear to be beyond the de Broglie wavelength. The basis of such quantum behavior is seen variously as quantum fields and virtual and entangled particles, and the determination that the behavior is quantum is made on coherence, order and interference effects, and non-local behavior. This paper proposes that biological memories and cognitive agents are collectives of quantum objects. Statistical and informational properties of the collectives that need to be taken into consideration are identified. Issues related to mapping of collectives into various energy states and resistance to noise are examined.
NeuroQuantology | September 2013 | Volume 11 | Issue 3 | Page 391-398
The dramatic increase in the efficiency of a quantum computer over a classical computer, raises a natural question asking, how much of this success could be attributed to its quantum nature and how much to its probabilistic content. To highlight this issue, we put forward the novel idea of a possible chemical computer driven by reaction-diffusion (RD) processes based on a probabilistic but classical approach. Such computers, obeying non-equilibrium statistical mechanics, can describe superpositions of empty and filled states with certain probabilities. With these {probit} states serving as computational basis states, such RD computers with operations satisfying a necessary semi-group property could mimic some well known quantum logic gates and carry out teleportation like procedure using entangled states, believed to be a prerogative of the quantum world. Moreover, assuming a nonlinear extension the RD computers could be used for cloning of arbitrary states, which is a famous forbidden operation in standard quantum computation.