3,651 research outputs found
Comparative Monte Carlo Efficiency by Monte Carlo Analysis
We propose a modified power method for computing the subdominant eigenvalue
of a matrix or continuous operator. Here we focus on defining
simple Monte Carlo methods for its application. The methods presented use
random walkers of mixed signs to represent the subdominant eigenfuction.
Accordingly, the methods must cancel these signs properly in order to sample
this eigenfunction faithfully. We present a simple procedure to solve this sign
problem and then test our Monte Carlo methods by computing the of
various Markov chain transition matrices. We first computed for
several one and two dimensional Ising models, which have a discrete phase
space, and compared the relative efficiencies of the Metropolis and heat-bath
algorithms as a function of temperature and applied magnetic field. Next, we
computed for a model of an interacting gas trapped by a harmonic
potential, which has a mutidimensional continuous phase space, and studied the
efficiency of the Metropolis algorithm as a function of temperature and the
maximum allowable step size . Based on the criterion, we
found for the Ising models that small lattices appear to give an adequate
picture of comparative efficiency and that the heat-bath algorithm is more
efficient than the Metropolis algorithm only at low temperatures where both
algorithms are inefficient. For the harmonic trap problem, we found that the
traditional rule-of-thumb of adjusting so the Metropolis acceptance
rate is around 50% range is often sub-optimal. In general, as a function of
temperature or , for this model displayed trends defining
optimal efficiency that the acceptance ratio does not. The cases studied also
suggested that Monte Carlo simulations for a continuum model are likely more
efficient than those for a discretized version of the model.Comment: 23 pages, 8 figure
Neural-Network Quantum States, String-Bond States, and Chiral Topological States
Neural-Network Quantum States have been recently introduced as an Ansatz for
describing the wave function of quantum many-body systems. We show that there
are strong connections between Neural-Network Quantum States in the form of
Restricted Boltzmann Machines and some classes of Tensor-Network states in
arbitrary dimensions. In particular we demonstrate that short-range Restricted
Boltzmann Machines are Entangled Plaquette States, while fully connected
Restricted Boltzmann Machines are String-Bond States with a nonlocal geometry
and low bond dimension. These results shed light on the underlying architecture
of Restricted Boltzmann Machines and their efficiency at representing many-body
quantum states. String-Bond States also provide a generic way of enhancing the
power of Neural-Network Quantum States and a natural generalization to systems
with larger local Hilbert space. We compare the advantages and drawbacks of
these different classes of states and present a method to combine them
together. This allows us to benefit from both the entanglement structure of
Tensor Networks and the efficiency of Neural-Network Quantum States into a
single Ansatz capable of targeting the wave function of strongly correlated
systems. While it remains a challenge to describe states with chiral
topological order using traditional Tensor Networks, we show that
Neural-Network Quantum States and their String-Bond States extension can
describe a lattice Fractional Quantum Hall state exactly. In addition, we
provide numerical evidence that Neural-Network Quantum States can approximate a
chiral spin liquid with better accuracy than Entangled Plaquette States and
local String-Bond States. Our results demonstrate the efficiency of neural
networks to describe complex quantum wave functions and pave the way towards
the use of String-Bond States as a tool in more traditional machine-learning
applications.Comment: 15 pages, 7 figure
Growth Algorithms for Lattice Heteropolymers at Low Temperatures
Two improved versions of the pruned-enriched-Rosenbluth method (PERM) are
proposed and tested on simple models of lattice heteropolymers. Both are found
to outperform not only the previous version of PERM, but also all other
stochastic algorithms which have been employed on this problem, except for the
core directed chain growth method (CG) of Beutler & Dill. In nearly all test
cases they are faster in finding low-energy states, and in many cases they
found new lowest energy states missed in previous papers. The CG method is
superior to our method in some cases, but less efficient in others. On the
other hand, the CG method uses heavily heuristics based on presumptions about
the hydrophobic core and does not give thermodynamic properties, while the
present method is a fully blind general purpose algorithm giving correct
Boltzmann-Gibbs weights, and can be applied in principle to any stochastic
sampling problem.Comment: 9 pages, 9 figures. J. Chem. Phys., in pres
Multicanonical Recursions
The problem of calculating multicanonical parameters recursively is
discussed. I describe in detail a computational implementation which has worked
reasonably well in practice.Comment: 23 pages, latex, 4 postscript figures included (uuencoded
Z-compressed .tar file created by uufiles), figure file corrected
Simulating the scalar glueball on the lattice
Techniques for efficient computation of the scalar glueball mass on the
lattice are described. Directions and physics goals of proposed future
calculations will be outlined.Comment: 9 pages, 3 figures, submitted to the proceedings of the SUNYIT Scalar
Mesons workshop (May 2003
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