432 research outputs found
Quantum algorithm for tree size estimation, with applications to backtracking and 2-player games
We study quantum algorithms on search trees of unknown structure, in a model
where the tree can be discovered by local exploration. That is, we are given
the root of the tree and access to a black box which, given a vertex ,
outputs the children of .
We construct a quantum algorithm which, given such access to a search tree of
depth at most , estimates the size of the tree within a factor of in steps. More generally, the same algorithm can
be used to estimate size of directed acyclic graphs (DAGs) in a similar model.
We then show two applications of this result:
a) We show how to transform a classical backtracking search algorithm which
examines nodes of a search tree into an time
quantum algorithm, improving over an earlier quantum backtracking algorithm of
Montanaro (arXiv:1509.02374).
b) We give a quantum algorithm for evaluating AND-OR formulas in a model
where the formula can be discovered by local exploration (modeling position
trees in 2-player games). We show that, in this setting, formulas of size
and depth can be evaluated in quantum time . Thus,
the quantum speedup is essentially the same as in the case when the formula is
known in advance.Comment: Fixed some typo
Quantum-accelerated constraint programming
Constraint programming (CP) is a paradigm used to model and solve constraint
satisfaction and combinatorial optimization problems. In CP, problems are
modeled with constraints that describe acceptable solutions and solved with
backtracking tree search augmented with logical inference. In this paper, we
show how quantum algorithms can accelerate CP, at both the levels of inference
and search. Leveraging existing quantum algorithms, we introduce a
quantum-accelerated filtering algorithm for the global
constraint and discuss its applicability to a broader family of global
constraints with similar structure. We propose frameworks for the integration
of quantum filtering algorithms within both classical and quantum backtracking
search schemes, including a novel hybrid classical-quantum backtracking search
method. This work suggests that CP is a promising candidate application for
early fault-tolerant quantum computers and beyond.Comment: published in Quantu
A Unified Framework of Quantum Walk Search
Many quantum algorithms critically rely on quantum walk search, or the use of quantum walks to speed up search problems on graphs. However, the main results on quantum walk search are scattered over different, incomparable frameworks, such as the hitting time framework, the MNRS framework, and the electric network framework. As a consequence, a number of pieces are currently missing. For example, recent work by Ambainis et al. (STOC\u2720) shows how quantum walks starting from the stationary distribution can always find elements quadratically faster. In contrast, the electric network framework allows quantum walks to start from an arbitrary initial state, but it only detects marked elements.
We present a new quantum walk search framework that unifies and strengthens these frameworks, leading to a number of new results. For example, the new framework effectively finds marked elements in the electric network setting. The new framework also allows to interpolate between the hitting time framework, minimizing the number of walk steps, and the MNRS framework, minimizing the number of times elements are checked for being marked. This allows for a more natural tradeoff between resources. In addition to quantum walks and phase estimation, our new algorithm makes use of quantum fast-forwarding, similar to the recent results by Ambainis et al. This perspective also enables us to derive more general complexity bounds on the quantum walk algorithms, e.g., based on Monte Carlo type bounds of the corresponding classical walk. As a final result, we show how in certain cases we can avoid the use of phase estimation and quantum fast-forwarding, answering an open question of Ambainis et al
A unified framework of quantum walk search
Many quantum algorithms critically rely on quantum walk search, or the use of quantum walks
to speed up search problems on graphs. However, the main results on quantum walk search are
scattered over different, incomparable frameworks, such as the hitting time framework, the MNRS
framework, and the electric network framework. As a consequence, a number of pieces are currently
missing. For example, recent work by Ambainis et al. (STOC’20) shows how quantum walks starting
from the stationary distribution can always find elements quadratically faster. In contrast, the
electric network framework allows quantum walks to start from an arbitrary initial state, but it only
detects marked elements.
We present a new quantum walk search framework that unifies and strengthens these frameworks,
leading to a number of new results. For example, the new framework effectively finds marked
elements in the electric network setting. The new framework also allows to interpolate between the
hitting time framework, minimizing the number of walk steps, and the MNRS framework, minimizing
the number of times elements are checked for being marked. This allows for a more natural tradeoff
between resources. In addition to quantum walks and phase estimation, our new algorithm makes
use of quantum fast-forwarding, similar to the recent results by Ambainis et al. This perspective also
enables us to derive more general complexity bounds on the quantum walk algorithms, e.g., based
on Monte Carlo type bounds of the corresponding classical walk. As a final result, we show how in
certain cases we can avoid the use of phase estimation and quantum fast-forwarding, answering an
open question of Ambainis et al.</p
Phase Transitions in Semidefinite Relaxations
Statistical inference problems arising within signal processing, data mining,
and machine learning naturally give rise to hard combinatorial optimization
problems. These problems become intractable when the dimensionality of the data
is large, as is often the case for modern datasets. A popular idea is to
construct convex relaxations of these combinatorial problems, which can be
solved efficiently for large scale datasets.
Semidefinite programming (SDP) relaxations are among the most powerful
methods in this family, and are surprisingly well-suited for a broad range of
problems where data take the form of matrices or graphs. It has been observed
several times that, when the `statistical noise' is small enough, SDP
relaxations correctly detect the underlying combinatorial structures.
In this paper we develop asymptotic predictions for several `detection
thresholds,' as well as for the estimation error above these thresholds. We
study some classical SDP relaxations for statistical problems motivated by
graph synchronization and community detection in networks. We map these
optimization problems to statistical mechanics models with vector spins, and
use non-rigorous techniques from statistical mechanics to characterize the
corresponding phase transitions. Our results clarify the effectiveness of SDP
relaxations in solving high-dimensional statistical problems.Comment: 71 pages, 24 pdf figure
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