3,834 research outputs found
A Quantum to Classical Phase Transition in Noisy Quantum Computers
The fundamental problem of the transition from quantum to classical physics
is usually explained by decoherence, and viewed as a gradual process. The study
of entanglement, or quantum correlations, in noisy quantum computers implies
that in some cases the transition from quantum to classical is actually a phase
transition. We define the notion of entanglement length in -dimensional
noisy quantum computers, and show that a phase transition in entanglement
occurs at a critical noise rate, where the entanglement length transforms from
infinite to finite. Above the critical noise rate, macroscopic classical
behavior is expected, whereas below the critical noise rate, subsystems which
are macroscopically distant one from another can be entangled.
The macroscopic classical behavior in the super-critical phase is shown to
hold not only for quantum computers, but for any quantum system composed of
macroscopically many finite state particles, with local interactions and local
decoherence, subjected to some additional conditions.
This phenomenon provides a possible explanation to the emergence of classical
behavior in such systems. A simple formula for an upper bound on the
entanglement length of any such system in the super-critical phase is given,
which can be tested experimentally.Comment: 15 pages. Latex2e plus one figure in eps fil
Neural network computation by in vitro transcriptional circuits
The structural similarity of neural networks and genetic regulatory networks
to digital circuits, and hence to each other, was noted from the
very beginning of their study [1, 2]. In this work, we propose a simple
biochemical system whose architecture mimics that of genetic regulation
and whose components allow for in vitro implementation of arbitrary
circuits. We use only two enzymes in addition to DNA and RNA
molecules: RNA polymerase (RNAP) and ribonuclease (RNase). We
develop a rate equation for in vitro transcriptional networks, and derive
a correspondence with general neural network rate equations [3].
As proof-of-principle demonstrations, an associative memory task and a
feedforward network computation are shown by simulation. A difference
between the neural network and biochemical models is also highlighted:
global coupling of rate equations through enzyme saturation can lead
to global feedback regulation, thus allowing a simple network without
explicit mutual inhibition to perform the winner-take-all computation.
Thus, the full complexity of the cell is not necessary for biochemical
computation: a wide range of functional behaviors can be achieved with
a small set of biochemical components
Evolving Gene Regulatory Networks with Mobile DNA Mechanisms
This paper uses a recently presented abstract, tuneable Boolean regulatory
network model extended to consider aspects of mobile DNA, such as transposons.
The significant role of mobile DNA in the evolution of natural systems is
becoming increasingly clear. This paper shows how dynamically controlling
network node connectivity and function via transposon-inspired mechanisms can
be selected for in computational intelligence tasks to give improved
performance. The designs of dynamical networks intended for implementation
within the slime mould Physarum polycephalum and for the distributed control of
a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1303.722
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Coding approaches to fault tolerance in dynamic systems
Also issued as Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 189-196).Sponsored through a contract with Sanders, A Lockheed Martin Company.Christoforos N. Hadjicostis
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