4,230 research outputs found
Quantum query complexity of entropy estimation
Estimation of Shannon and R\'enyi entropies of unknown discrete distributions
is a fundamental problem in statistical property testing and an active research
topic in both theoretical computer science and information theory. Tight bounds
on the number of samples to estimate these entropies have been established in
the classical setting, while little is known about their quantum counterparts.
In this paper, we give the first quantum algorithms for estimating
-R\'enyi entropies (Shannon entropy being 1-Renyi entropy). In
particular, we demonstrate a quadratic quantum speedup for Shannon entropy
estimation and a generic quantum speedup for -R\'enyi entropy
estimation for all , including a tight bound for the
collision-entropy (2-R\'enyi entropy). We also provide quantum upper bounds for
extreme cases such as the Hartley entropy (i.e., the logarithm of the support
size of a distribution, corresponding to ) and the min-entropy case
(i.e., ), as well as the Kullback-Leibler divergence between
two distributions. Moreover, we complement our results with quantum lower
bounds on -R\'enyi entropy estimation for all .Comment: 43 pages, 1 figur
Distributional Property Testing in a Quantum World
A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. We also introduce a novel access model for quantum distributions, enabling the coherent preparation of quantum samples, and propose a general framework that can naturally handle both classical and quantum distributions in a unified manner. Our framework generalizes and improves previous quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. For classical distributions our algorithms significantly improve the precision dependence of some earlier results. We also show that in our framework procedures for classical distributions can be directly lifted to the more general case of quantum distributions, and thus obtain the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling
Quantum and Classical Tradeoffs
We propose an approach for quantifying a quantum circuit's quantumness as a
means to understand the nature of quantum algorithmic speedups. Since quantum
gates that do not preserve the computational basis are necessary for achieving
quantum speedups, it appears natural to define the quantumness of a quantum
circuit using the number of such gates. Intuitively, a reduction in the
quantumness requires an increase in the amount of classical computation, hence
giving a ``quantum and classical tradeoff''.
In this paper we present two results on this direction. The first gives an
asymptotic answer to the question: ``what is the minimum number of
non-basis-preserving gates required to generate a good approximation to a given
state''. This question is the quantum analogy of the following classical
question, ``how many fair coins are needed to generate a given probability
distribution'', which was studied and resolved by Knuth and Yao in 1976. Our
second result shows that any quantum algorithm that solves Grover's Problem of
size n using k queries and l levels of non-basis-preserving gates must have
k*l=\Omega(n)
Quantum algorithms for testing properties of distributions
Suppose one has access to oracles generating samples from two unknown
probability distributions P and Q on some N-element set. How many samples does
one need to test whether the two distributions are close or far from each other
in the L_1-norm ? This and related questions have been extensively studied
during the last years in the field of property testing. In the present paper we
study quantum algorithms for testing properties of distributions. It is shown
that the L_1-distance between P and Q can be estimated with a constant
precision using approximately N^{1/2} queries in the quantum settings, whereas
classical computers need \Omega(N) queries. We also describe quantum algorithms
for testing Uniformity and Orthogonality with query complexity O(N^{1/3}). The
classical query complexity of these problems is known to be \Omega(N^{1/2}).Comment: 20 page
Exponential Quantum Speed-ups are Generic
A central problem in quantum computation is to understand which quantum
circuits are useful for exponential speed-ups over classical computation. We
address this question in the setting of query complexity and show that for
almost any sufficiently long quantum circuit one can construct a black-box
problem which is solved by the circuit with a constant number of quantum
queries, but which requires exponentially many classical queries, even if the
classical machine has the ability to postselect.
We prove the result in two steps. In the first, we show that almost any
element of an approximate unitary 3-design is useful to solve a certain
black-box problem efficiently. The problem is based on a recent oracle
construction of Aaronson and gives an exponential separation between quantum
and classical bounded-error with postselection query complexities.
In the second step, which may be of independent interest, we prove that
linear-sized random quantum circuits give an approximate unitary 3-design. The
key ingredient in the proof is a technique from quantum many-body theory to
lower bound the spectral gap of local quantum Hamiltonians.Comment: 24 pages. v2 minor correction
New summing algorithm using ensemble computing
We propose an ensemble algorithm, which provides a new approach for
evaluating and summing up a set of function samples. The proposed algorithm is
not a quantum algorithm, insofar it does not involve quantum entanglement. The
query complexity of the algorithm depends only on the scaling of the
measurement sensitivity with the number of distinct spin sub-ensembles. From a
practical point of view, the proposed algorithm may result in an exponential
speedup, compared to known quantum and classical summing algorithms. However in
general, this advantage exists only if the total number of function samples is
below a threshold value which depends on the measurement sensitivity.Comment: 13 pages, 0 figures, VIth International Conference on Quantum
Communication, Measurement and Computing (Boston, 2002
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