613 research outputs found
Quantum Metropolis-Hastings algorithm with the target distribution calculated by quantum Monte Carlo integration
The Markov chain Monte Carlo method (MCMC), especially the
Metropolis-Hastings (MH) algorithm, is a widely used technique for sampling
from a target probability distribution on a state space and
applied to various problems such as estimation of parameters in statistical
models in the Bayesian approach. Quantum algorithms for MCMC have been
proposed, yielding the quadratic speedup with respect to the spectral gap
compered to classical counterparts. In this paper, we consider the
quantum version of the MH algorithm in the case that calculating is costly
because the log-likelihood for a state is obtained via
computing the sum of many terms . We
propose calculating by quantum Monte Carlo integration and combine it with
the existing method called quantum simulated annealing (QSA) to generate the
quantum state that encodes in amplitudes. We consider not only state
generation but also finding a credible interval for a parameter, a common task
in Bayesian inference. In the proposed method for credible interval
calculation, the number of queries to the quantum circuit to compute
scales on , the required accuracy and the standard deviation
of as , in contrast
to for QSA with calculated exactly.
Therefore, the proposed method is advantageous if scales on
sublinearly. As one such example, we consider parameter estimation in a
gravitational wave experiment, where
Extracting a function encoded in amplitudes of a quantum state by tensor network and orthogonal function expansion
There are quantum algorithms for finding a function satisfying a set of
conditions, such as solving partial differential equations, and these achieve
exponential quantum speedup compared to existing classical methods, especially
when the number of the variables of is large. In general, however,
these algorithms output the quantum state which encodes in the amplitudes,
and reading out the values of as classical data from such a state can be so
time-consuming that the quantum speedup is ruined. In this study, we propose a
general method for this function readout task. Based on the function
approximation by a combination of tensor network and orthogonal function
expansion, we present a quantum circuit and its optimization procedure to
obtain an approximating function of that has a polynomial number of degrees
of freedom with respect to and is efficiently evaluable on a classical
computer. We also conducted a numerical experiment to approximate a
finance-motivated function to demonstrate that our method works.Comment: 16 pages, 8 figure
B-mode polarization induced by gravitational waves from kinks on infinite cosmic strings
We investigate the effect of the stochastic gravitational wave (GW)
background produced by kinks on infinite cosmic strings, whose spectrum was
derived in our previous work, on the B-mode power spectrum of the cosmic
microwave background (CMB) anisotropy. We find that the B-mode polarization due
to kinks is comparable to that induced by the motion of the string network and
hence the contribution of GWs from kinks is important for estimating the B-mode
power spectrum originating from cosmic strings. If the tension of cosmic
strings \mu is large enough i.e., G\mu >~ 10^{-8}, B-mode polarization induced
by cosmic strings can be detected by future CMB experiments.Comment: 13 pages, 1 figur
Gravitational waves from kinks on infinite cosmic strings
Gravitational waves emitted by kinks on infinite strings are investigated
using detailed estimations of the kink distribution on infinite strings. We
find that gravitational waves from kinks can be detected by future pulsar
timing experiments such as SKA for an appropriate value of the the string
tension, if the typical size of string loops is much smaller than the horizon
at their formation. Moreover, the gravitational wave spectrum depends on the
thermal history of the Universe and hence it can be used as a probe into the
early evolution of the Universe.Comment: 29 pages, 4figure
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