arXiv subjects

Chris Adami

Publications and source records attributed to Chris Adami.

11 recordsLinked to original sources

arXiv2004

Evolution of Robust Developmental Neural Networks

We present the first evolved solutions to a computational task within the Neuronal Organism Evolution model (Norgev) of artificial neural network development. These networks display a remarkable robustness to external noise sources, and can regrow to functionality when severely damaged. In this framework, we evolved a doubling of network functionality (double-NAND circuit). The network structure of these evolved solutions does not follow the logic of human coding, and instead more resembles the decentralized dendritic connection pattern of more biological networks such as the 'C. elegans' brain.

nlin.AO↗

arXiv2002

Quantum Computation--The Ultimate Frontier

The discovery of an algorithm for factoring which runs in polynomial time on a quantum computer has given rise to a concerted effort to understand the principles, advantages, and limitations of quantum computing. At the same time, many different quantum systems are being explored for their suitability to serve as a physical substrate for the quantum computer of the future. I discuss some of the theoretical foundations of quantum computer science, including algorithms and error correction, and present a few physical systems that have shown promise as a quantum computing platform. Finally, we discuss a spin-off of the quantum computing revolution: quantum technologies.

quant-ph↗

arXiv2000

Complex Langevin Equation and the Many-Fermion Problem

We study the utility of a complex Langevin (CL) equation as an alternative for the Monte Carlo (MC) procedure in the evaluation of expectation values occurring in fermionic many-body problems. We find that a CL approach is natural in cases where non-positive definite probability measures occur, and remains accurate even when the corresponding MC calculation develops a severe ``sign problem''. While the convergence of CL averages cannot be guaranteed in principle, we show how convergent results can be obtained in three examples ranging from simple one-dimensional integrals over quantum mechanical models to a schematic shell model path integral.

nucl-th↗

arXiv2000

A Simple Explanation for Taxon Abundance Patterns

For taxonomic levels higher than species, the abundance distributions of number of subtaxa per taxon tend to approximate power laws, but often show strong deviationns from such a law. Previously, these deviations were attributed to finite-time effects in a continuous time branching process at the generic level. Instead, we describe here a simple discrete branching process which generates the observed distributions and find that the distribution's deviation from power-law form is not caused by disequilibration, but rather that it is time-independent and determined by the evolutionary properties of the taxa of interest. Our model predicts-with no free parameters-the rank-frequency distribution of number of families in fossil marine animal orders obtained from the fossil record. We find that near power-law distributions are statistically almost inevitable for taxa higher than species. The branching model also sheds light on species abundance patterns, as well as on links between evolutionary processes, self-organized criticality and fractals.

physics.bio-ph↗

arXiv1998

Critical Exponent of Species-Size Distribution in Evolution

We analyze the geometry of the species- and genotype-size distribution in evolving and adapting populations of single-stranded self-replicating genomes: here programs in the Avida world. We find that a scale-free distribution (power law) emerges in complex landscapes that achieve a separation of two fundamental time scales: the relaxation time (time for population to return to equilibrium after a perturbation) and the time between mutations that produce fitter genotypes. The latter can be dialed by changing the mutation rate. In the scaling regime, we determine the critical exponent of the distribution of sizes and strengths of avalanches in a system without coevolution, described by first-order phase transitions in single finite niches.

adap-org↗

arXiv1997

Quantum Information Theory of Entanglement and Measurement

We present a quantum information theory that allows for a consistent description of entanglement. It parallels classical (Shannon) information theory but is based entirely on density matrices (rather than probability distributions) for the description of quantum ensembles. We find that quantum conditional entropies can be negative for entangled systems, which leads to a violation of well-known bounds in Shannon information theory. Such a unified information-theoretic description of classical correlation and quantum entanglement clarifies the link between them: the latter can be viewed as ``super-correlation'' which can induce classical correlation when considering a tripartite or larger system. Furthermore, negative entropy and the associated clarification of entanglement paves the way to a natural information-theoretic description of the measurement process. This model, while unitary and causal, implies the well-known probabilistic results of conventional quantum mechanics. It also results in a simple interpretation of the Kholevo theorem limiting the accessible information in a quantum measurement.

quant-ph↗

arXiv1996

Negative entropy in quantum information theory

We present a quantum information theory that allows for the consistent description of quantum entanglement. It parallels classical (Shannon) information theory but is based entirely on density matrices, rather than probability distributions, for the description of quantum ensembles. We find that, unlike in Shannon theory, conditional entropies can be negative when considering quantum entangled systems such as an Einstein-Podolsky-Rosen pair, which leads to a violation of well-known bounds of classical information theory. Negative quantum entropy can be traced back to ``conditional'' density matrices which admit eigenvalues larger than unity. A straightforward definition of mutual quantum entropy, or ``mutual entanglement'', can also be constructed using a ``mutual'' density matrix. Such a unified information-theoretic description of classical correlation and quantum entanglement clarifies the link between them: the latter can be viewed as ``super-correlation'' which can induce classical correlation when considering a ternary or larger system.

quant-ph↗

arXiv1996

Propagation of Information in Populations of Self-Replicating Code

We observe the propagation of information in a system of self-replicating strings of code (``Artificial Life'') as a function of fitness and mutation rate. Comparison with theoretical predictions based on the reaction-diffusion equation shows that the response of the artificial system to fluctuations (\eg velocity of the information wave as a function of relative fitness) closely follows that of natural systems. We find that the relaxation time of the system depends on the speed of propagation of information and the size of the system. This analysis offers the possibility of determining the minimal system size for observation of non-equilibrium effects at fixed mutation rate.

adap-org↗

arXiv1994

On Modelling Life

We present a theoretical as well as experimental investigation of a population of self-replicating segments of code subject to random mutation and survival of the fittest. Under the assumption that such a system constitutes a minimal system with characteristics of life, we obtain a number of statements on the evolution of complexity and the trade-off between entropy and information.

adap-org↗

arXiv1994

Evolutionary Learning in the 2D Artificial Life System "Avida"

We present a new tierra-inspired artificial life system with local interactions and two-dimensional geometry, based on an update mechanism akin to that of 2D cellular automata. We find that the spatial geometry is conducive to the development of diversity and thus improves adaptive capabilities. We also demonstrate the adaptive strength of the system by breeding cells with simple computational abilities, and study the dependence of this adaptability on mutation rate and population size.

adap-org↗

arXiv1994

Learning and Complexity in Genetic Auto-Adaptive Systems

We describe and investigate the learning capablities displayed by a population of self-replicating segments of computer-code subject to random mutation: the tierra environment. We find that learning is achieved through phase transitions that adapt the population to whichever environment it encounters, with a learning rate characterized by the environmental variables. Our results suggest that most effective learning is achieved close to the edge of chaos.

adap-org↗