2,050 research outputs found
The quantum measurement problem and physical reality: a computation theoretic perspective
Is the universe computable? If yes, is it computationally a polynomial place?
In standard quantum mechanics, which permits infinite parallelism and the
infinitely precise specification of states, a negative answer to both questions
is not ruled out. On the other hand, empirical evidence suggests that
NP-complete problems are intractable in the physical world. Likewise,
computational problems known to be algorithmically uncomputable do not seem to
be computable by any physical means. We suggest that this close correspondence
between the efficiency and power of abstract algorithms on the one hand, and
physical computers on the other, finds a natural explanation if the universe is
assumed to be algorithmic; that is, that physical reality is the product of
discrete sub-physical information processing equivalent to the actions of a
probabilistic Turing machine. This assumption can be reconciled with the
observed exponentiality of quantum systems at microscopic scales, and the
consequent possibility of implementing Shor's quantum polynomial time algorithm
at that scale, provided the degree of superposition is intrinsically, finitely
upper-bounded. If this bound is associated with the quantum-classical divide
(the Heisenberg cut), a natural resolution to the quantum measurement problem
arises. From this viewpoint, macroscopic classicality is an evidence that the
universe is in BPP, and both questions raised above receive affirmative
answers. A recently proposed computational model of quantum measurement, which
relates the Heisenberg cut to the discreteness of Hilbert space, is briefly
discussed. A connection to quantum gravity is noted. Our results are compatible
with the philosophy that mathematical truths are independent of the laws of
physics.Comment: Talk presented at "Quantum Computing: Back Action 2006", IIT Kanpur,
India, March 200
On the possible Computational Power of the Human Mind
The aim of this paper is to address the question: Can an artificial neural
network (ANN) model be used as a possible characterization of the power of the
human mind? We will discuss what might be the relationship between such a model
and its natural counterpart. A possible characterization of the different power
capabilities of the mind is suggested in terms of the information contained (in
its computational complexity) or achievable by it. Such characterization takes
advantage of recent results based on natural neural networks (NNN) and the
computational power of arbitrary artificial neural networks (ANN). The possible
acceptance of neural networks as the model of the human mind's operation makes
the aforementioned quite relevant.Comment: Complexity, Science and Society Conference, 2005, University of
Liverpool, UK. 23 page
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