# PlanetPhysics/Quantum Automaton
### Quantum automaton and quantum computation

A quantum automaton can be simply defined as an extension of an automaton with quantum states instead of the sequentially determined states, inputs and outputs of a sequential, or state machine . The precise mathematical definitions of quantum automaton*, variable automaton, and quantum computation were first introduced formally in refs. and in relation to relational models in Quantum relational biology (loc.cit ).
Note: Quantum computation and quantum `machines' (or nanobots) were much publicized in the early 1980's by Richard Feynman (Nobel Laureate in Physics: QED),and, subsequently, a very large number of papers- too many to cite all of them here- were published on this topic by a rapidly growing number of quantum theoreticians and some applied mathematicians.
Remark.
Quantum `computation' becomes possible only when macroscopic blocks of quantum states can be controlled via quantum preparation and subsequent, classical observation. Obstructions to `building', or constructing quantum computers are known to exist in dimensions greater than $2$ as a result of the standard K-S theorem. Subsequent definitions of quantum computers reflect attempts to either avoid or surmount such difficulties often without seeking solutions through quantum operator algebras and their representations related to extended quantum symmetries which define fundamental invariants that are key
    Alternatively, as a Quantum Algebraic Topology object, a quantum automaton is defined by the
triplet Failed to parse (unknown function "\grp"): {\displaystyle (\grp,''H'' -\Re_{\grp}, Aut(\grp)} ), where Failed to parse (unknown function "\grp"): {\displaystyle \grp} is a locally compact quantum groupoid , H -Failed to parse (unknown function "\grp"): {\displaystyle \Re_{\grp}} are the unitary representations of Failed to parse (unknown function "\grp"): {\displaystyle \grp} on rigged Hilbert spaces Failed to parse (unknown function "\grp"): {\displaystyle \Re_\grp} of quantum states and quantum operators on H , and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "http://localhost:6011/en.wikiversity.org/v1/":): {\displaystyle Aut(\grp)} is the transformation, or automorphism, groupoid of quantum transitions.
Remark. Other definitions of quantum automata and quantum computations have also been reported that are closely related to recent experimental attempts at constructing quantum computing devices.
Two examples of such definitions are briefly considered next.
    quantum automata were defined in refs. and as generalized, probabilistic automata with quantum state spaces. Their next-state functions operate through transitions between quantum states defined by the quantum equations of motions in the Schr\"{o}dinger representation, with both initial and boundary conditions in space-time.
A new theorem was proven which states that the \htmladdnormallink{category of quantum automata {http://planetphysics.us/encyclopedia/CategoryOfQuantumAutomata.html} and automata--homomorphisms has both limits and colimits.} Therefore, both categories of quantum automata and classical automata (sequential machines) are bicomplete. A second new theorem established that the standard automata category is a subcategory of the quantum automata category.
Related Results: Quantum Automata Applications to Modeling Complex Systems. The quantum automata category has a faithful representation in the category of Generalized $(M,R)$ -systems which are open, dynamic bio-networks with defined biological relations that represent physiological functions of primordial(s), single cells and the simpler organisms. A new category of quantum computers is also defined in terms of reversible quantum automata with quantum state spaces represented by topological groupoids that admit a local characterization through unique `quantum' Lie algebroids. On the other hand, the category of $n$-\L ukasiewicz algebras has a subcategory of centered $n$- \Lukasiewicz algebras (which can be employed to design and construct subcategories of quantum automata based on $n$-\L ukasiewicz diagrams of existing VLSI. Furthermore, as shown in ref.( the category of centered $n$-\L ukasiewicz algebras and the category of Boolean algebras are naturally equivalent.
Variable machines with a varying transition function were previously discussed informally by Norbert Wiener as a possible model for complex biological systems although how this might be achieved in Biocybernetics has not been specifcally, or mathematically presented by Wiener.
A `no-go' conjecture was also proposed which states that Generalized (M,R )--Systems complexity prevents their complete computability by either standard or quantum automata. The concepts of quantum automata and quantum computation were initially studied and are also currently further investigated in the contexts of quantum genetics, genetic networks with nonlinear dynamics, and bioinformatics. In a previous publication (ICB71a)-- after introducing the formal concept of quantum automaton--the possible implications of this concept for correctly modeling genetic and metabolic activities in living cells and organisms were also considered. This was followed by a formal report on quantum and abstract, symbolic computation based on the theory of categories, functors and natural transformations. The notions of topological semigroup, quantum automaton,or quantum computer, were then suggested with a view to their potential applications to the analogous simulation of biological systems, and especially genetic activities and nonlinear dynamics in genetic networks. Further, detailed studies of nonlinear dynamics in genetic networks were carried out in categories of $n$-valued, \L ukasiewicz Logic Algebras that showed significant dissimilarities from the widespread Bolean models of human neural networks that may have begun with the early publication of. Molecular models in terms of categories, functors and natural transformations were then formulated for uni-molecular chemical transformations, multi-molecular chemical and biochemical transformations. Previous applications of computer modeling, classical automata theory, and relational biology to molecular biology, oncogenesis and medicine were extensively reviewed and several important conclusions were reached regarding both the potential and limitations of the computation-assisted modeling of biological systems, and especially complex organisms such as Homo sapiens sapiens. Novel approaches to solving the realization problems of Relational Biology models in Complex System Biology are introduced in terms of natural transformations between functors of such molecular categories. Several applications of such natural transformations of functors were then presented to protein biosynthesis, embryogenesis and nuclear transplant experiments. Other possible realizations in Molecular Biology and Relational Biology of Organisms were then suggested in terms of quantum automata models of Quantum Genetics and Interactomics. Future developments of this novel approach are likely to also include: Fuzzy Relations in Biology and Epigenomics, Relational Biology modeling of Complex Immunological and Hormonal regulatory systems, $n$-categories and generalized $LM$ --Topoi of \Lukasiewicz Logic Algebras and intuitionistic logic (Heyting) algebras for modeling nonlinear dynamics and cognitive processes in complex neural networks that are present in the human brain, as well as stochastic modeling of genetic networks in \Lukasiewicz Logic Algebras (LLA).

### Quantum Automata, Quantum Computation and Quantum Dynamics Represented by Categories, Functors and Natural Transformations.

Molecular models were previously defined in terms of categories, functors and natural transformations were formulated for unimolecular chemical transformations, multi-molecular chemical and biochemical transformations . Dynamic similarities or analogies between categories of classical, quantum or complex systems and their transformations were then naturally represented in terms of adjoint functors and the corresponding natural equivalences.
Remark . Previous applications of computer modeling, classical automata theory, and relational biology to molecular biology, neural networks, oncogenesis and medicine were extensively reviewed in a previous monograph and several important conclusions were reached regarding both the potential and the severe limitations of the algorithm driven, recursive computation-assisted modeling of complex biological systems.