research

Quantum biology

There has been recent experimental evidence suggesting the existence of quantum phenomenon effects on living organisms. For instance, birds, insects and other animals appear to use quantum coherent entanglement navigation by sensing Earth’s magnetic field; plants use a form of quantum coherent superposition to find the most efficient pathway to direct energy through their photosynthetic reaction centre in the form of coherent excitons; quantum tunnelling is used by some enzymes - the molecules responsible for metabolic reactions in cells - to accelerate chemical reactions; or the sense of smell, which involves electron tunnelling between the odour molecule and a receptor in the nose. One of the most critical problems in quantum biology is the explanation of how quantum effects that take place in a microscopic world can give rise to the macroscopic world of living beings.

In this research [28] conducted with M. Alfonseca, A. Ortega and M. de la Cruz we introduced a hybrid cellular automata named QvN, an abbreviation for Quantum von Neumann ‘hybrid’ automata, capable of performing either as a quantum cellular automata (QCA) or as a classical von Neumann automata (CA), simulating the transition or reduction from a quantum microscopic level with superposition of several quantum states, to a macroscopic level with a single stable state. We report the modelling and simulation of quantum coherent superposition in cytoskeletal microtubules including decoherence, thus the effect of the collapse of the microtubule coherent state wave function that is supposed to occur in cytoskeletal microtubules and in which rely the 'Orch OR model' of human mind proposed by Hameroff and Penrose. In the future quantum biology will have a deep effect on the design of new devices, e.g. quantum hardware, in electrical engineering.

Right caption .- Microtubule coherent state simulation (conducted with LabVIEW by M. de la Cruz [28])

Do genetic algorithms really emulate Darwin's principle of natural selection? Design of quantum cellular automata (QCA) circuits with genetic algorithms

In the paper [30] we  introduce  a  discussion  about  evolutionary  search  methods  based  on  Hamming  oracle.  In  many  optimization  problems,  the  design  of  the  fitness  function  includes  the  Hamming  distance being referred this kind of functions as Hamming oracle. In this paper we adopt a critical look and ask ourselves to what extent genetic algorithms and other related evolutionary methods truly mimic evolution.  We  tested  three  evolutionary  search  methods  taken  as  a  case  study  the  evolutionary  synthesis  of  quantum-dot  cellular  automata  circuits.  Our  main  conclusion  is  that  evolutionary  search  methods do not mimetic Darwinian evolution because knowledge is not obtained from the evolutionary surface  exploration:  evolution  is  the  result  of  the  ‘knowledge’  embedded  by  the  researcher  or  human  expert  into  the  fitness  function.  Maybe  a  more  appropriate  denomination  would  be  “combinatorial  search algorithms"such as Minimax, Alpha-beta pruning, etc.

Figure (right) QCA circuits designed and tested with QCADesigner: (a) XOR gate (b) inverted XOR.

Quantum genetic algorithms

Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs). In this review [29], we present a discussion, future potential, pros and cons of this new class of GAs.