198 research outputs found
Is there a physically universal cellular automaton or Hamiltonian?
It is known that both quantum and classical cellular automata (CA) exist that
are computationally universal in the sense that they can simulate, after
appropriate initialization, any quantum or classical computation, respectively.
Here we introduce a different notion of universality: a CA is called physically
universal if every transformation on any finite region can be (approximately)
implemented by the autonomous time evolution of the system after the complement
of the region has been initialized in an appropriate way. We pose the question
of whether physically universal CAs exist. Such CAs would provide a model of
the world where the boundary between a physical system and its controller can
be consistently shifted, in analogy to the Heisenberg cut for the quantum
measurement problem. We propose to study the thermodynamic cost of computation
and control within such a model because implementing a cyclic process on a
microsystem may require a non-cyclic process for its controller, whereas
implementing a cyclic process on system and controller may require the
implementation of a non-cyclic process on a "meta"-controller, and so on.
Physically universal CAs avoid this infinite hierarchy of controllers and the
cost of implementing cycles on a subsystem can be described by mixing
properties of the CA dynamics. We define a physical prior on the CA
configurations by applying the dynamics to an initial state where half of the
CA is in the maximum entropy state and half of it is in the all-zero state
(thus reflecting the fact that life requires non-equilibrium states like the
boundary between a hold and a cold reservoir). As opposed to Solomonoff's
prior, our prior does not only account for the Kolmogorov complexity but also
for the cost of isolating the system during the state preparation if the
preparation process is not robust.Comment: 27 pages, 1 figur
Ergodic quantum computing
We propose a (theoretical ;-) model for quantum computation where the result
can be read out from the time average of the Hamiltonian dynamics of a
2-dimensional crystal on a cylinder. The Hamiltonian is a spatially local
interaction among Wigner-Seitz cells containing 6 qubits. The quantum circuit
that is simulated is specified by the initialization of program qubits. As in
Margolus' Hamiltonian cellular automaton (implementing classical circuits), a
propagating wave in a clock register controls asynchronously the application of
the gates. However, in our approach all required initializations are basis
states. After a while the synchronizing wave is essentially spread around the
whole crystal. The circuit is designed such that the result is available with
probability about 1/4 despite of the completely undefined computation step.
This model reduces quantum computing to preparing basis states for some qubits,
waiting, and measuring in the computational basis. Even though it may be
unlikely to find our specific Hamiltonian in real solids, it is possible that
also more natural interactions allow ergodic quantum computing.Comment: latex, 25 pages, 10 figures (colored
Detecting confounding in multivariate linear models via spectral analysis
We study a model where one target variable Y is correlated with a vector
X:=(X_1,...,X_d) of predictor variables being potential causes of Y. We
describe a method that infers to what extent the statistical dependences
between X and Y are due to the influence of X on Y and to what extent due to a
hidden common cause (confounder) of X and Y. The method relies on concentration
of measure results for large dimensions d and an independence assumption
stating that, in the absence of confounding, the vector of regression
coefficients describing the influence of each X on Y typically has `generic
orientation' relative to the eigenspaces of the covariance matrix of X. For the
special case of a scalar confounder we show that confounding typically spoils
this generic orientation in a characteristic way that can be used to
quantitatively estimate the amount of confounding.Comment: 27 pages, 16 figure
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