299 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Borel versions of the Local Lemma and LOCAL algorithms for graphs of finite asymptotic separation index
Asymptotic separation index is a parameter that measures how easily a Borel
graph can be approximated by its subgraphs with finite components. In contrast
to the more classical notion of hyperfiniteness, asymptotic separation index is
well-suited for combinatorial applications in the Borel setting. The main
result of this paper is a Borel version of the Lov\'asz Local Lemma -- a
powerful general-purpose tool in probabilistic combinatorics -- under a finite
asymptotic separation index assumption. As a consequence, we show that locally
checkable labeling problems that are solvable by efficient randomized
distributed algorithms admit Borel solutions on bounded degree Borel graphs
with finite asymptotic separation index. From this we derive a number of
corollaries, for example a Borel version of Brooks's theorem for graphs with
finite asymptotic separation index
Computing a partition function of a generalized pattern-based energy over a semiring
Valued constraint satisfaction problems with ordered variables (VCSPO) are a
special case of Valued CSPs in which variables are totally ordered and soft
constraints are imposed on tuples of variables that do not violate the order.
We study a restriction of VCSPO, in which soft constraints are imposed on a
segment of adjacent variables and a constraint language consists of
-valued characteristic functions of predicates. This kind of
potentials generalizes the so-called pattern-based potentials, which were
applied in many tasks of structured prediction.
For a constraint language we introduce a closure operator, , and give examples of constraint
languages for which is small. If all predicates in
are cartesian products, we show that the minimization of a generalized
pattern-based potential (or, the computation of its partition function) can be
made in
time, where is a set of variables, is a domain set. If, additionally,
only non-positive weights of constraints are allowed, the complexity of the
minimization task drops to where is the
arity of . For a general language and non-positive
weights, the minimization task can be carried out in time.
We argue that in many natural cases is of moderate
size, though in the worst case can blow up and
depend exponentially on
Geometric optimization problems in quantum computation and discrete mathematics: Stabilizer states and lattices
This thesis consists of two parts:
Part I deals with properties of stabilizer states and their convex
hull, the stabilizer polytope. Stabilizer states, Pauli measurements
and Clifford unitaries are the three building blocks of the stabilizer
formalism whose computational power is limited by the Gottesman-
Knill theorem. This model is usually enriched by a magic state to get
a universal model for quantum computation, referred to as quantum
computation with magic states (QCM). The first part of this thesis
will investigate the role of stabilizer states within QCM from three
different angles.
The first considered quantity is the stabilizer extent, which provides
a tool to measure the non-stabilizerness or magic of a quantum state.
It assigns a quantity to each state roughly measuring how many stabilizer
states are required to approximate the state. It has been shown
that the extent is multiplicative under taking tensor products when
the considered state is a product state whose components are composed
of maximally three qubits. In Chapter 2, we will prove that
this property does not hold in general, more precisely, that the stabilizer
extent is strictly submultiplicative. We obtain this result as
a consequence of rather general properties of stabilizer states. Informally
our result implies that one should not expect a dictionary to be
multiplicative under taking tensor products whenever the dictionary
size grows subexponentially in the dimension.
In Chapter 3, we consider QCM from a resource theoretic perspective.
The resource theory of magic is based on two types of quantum
channels, completely stabilizer preserving maps and stabilizer operations.
Both classes have the property that they cannot generate additional
magic resources. We will show that these two classes of quantum
channels do not coincide, specifically, that stabilizer operations are a
strict subset of the set of completely stabilizer preserving channels.
This might have the consequence that certain tasks which are usually
realized by stabilizer operations could in principle be performed better
by completely stabilizer preserving maps.
In Chapter 4, the last one of Part I, we consider QCM via the polar
dual stabilizer polytope (also called the Lambda-polytope). This polytope
is a superset of the quantum state space and every quantum state
can be written as a convex combination of its vertices. A way to
classically simulate quantum computing with magic states is based on
simulating Pauli measurements and Clifford unitaries on the vertices
of the Lambda-polytope. The complexity of classical simulation with respect
to the polytope is determined by classically simulating the updates
of vertices under Clifford unitaries and Pauli measurements. However,
a complete description of this polytope as a convex hull of its vertices is
only known in low dimensions (for up to two qubits or one qudit when
odd dimensional systems are considered). We make progress on this
question by characterizing a certain class of operators that live on the
boundary of the Lambda-polytope when the underlying dimension is an odd
prime. This class encompasses for instance Wigner operators, which
have been shown to be vertices of Lambda. We conjecture that this class
contains even more vertices of Lambda. Eventually, we will shortly sketch
why applying Clifford unitaries and Pauli measurements to this class
of operators can be efficiently classically simulated.
Part II of this thesis deals with lattices. Lattices are discrete subgroups
of the Euclidean space. They occur in various different areas of
mathematics, physics and computer science. We will investigate two
types of optimization problems related to lattices.
In Chapter 6 we are concerned with optimization within the space of
lattices. That is, we want to compare the Gaussian potential energy
of different lattices. To make the energy of lattices comparable we
focus on lattices with point density one. In particular, we focus on
even unimodular lattices and show that, up to dimension 24, they are
all critical for the Gaussian potential energy. Furthermore, we find
that all n-dimensional even unimodular lattices with n 24 are local
minima or saddle points. In contrast in dimension 32, there are even
unimodular lattices which are local maxima and others which are not
even critical.
In Chapter 7 we consider flat tori R^n/L, where L is an n-dimensional
lattice. A flat torus comes with a metric and our goal is to approximate
this metric with a Hilbert space metric. To achieve this, we
derive an infinite-dimensional semidefinite optimization program that
computes the least distortion embedding of the metric space R^n/L into
a Hilbert space. This program allows us to make several interesting
statements about the nature of least distortion embeddings of flat tori.
In particular, we give a simple proof for a lower bound which gives
a constant factor improvement over the previously best lower bound
on the minimal distortion of an embedding of an n-dimensional flat
torus. Furthermore, we show that there is always an optimal embedding
into a finite-dimensional Hilbert space. Finally, we construct
optimal least distortion embeddings for the standard torus R^n/Z^n and
all 2-dimensional flat tori
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions
We develop a novel, general and computationally efficient framework, called
Divide and Conquer Dynamic Programming (DCDP), for localizing change points in
time series data with high-dimensional features. DCDP deploys a class of greedy
algorithms that are applicable to a broad variety of high-dimensional
statistical models and can enjoy almost linear computational complexity. We
investigate the performance of DCDP in three commonly studied change point
settings in high dimensions: the mean model, the Gaussian graphical model, and
the linear regression model. In all three cases, we derive non-asymptotic
bounds for the accuracy of the DCDP change point estimators. We demonstrate
that the DCDP procedures consistently estimate the change points with sharp,
and in some cases, optimal rates while incurring significantly smaller
computational costs than the best available algorithms. Our findings are
supported by extensive numerical experiments on both synthetic and real data.Comment: 84 pages, 4 figures, 6 table
The Complexity of Some Geometric Proof Systems
In this Thesis we investigate proof systems based on Integer Linear Programming. These methods inspect the solution space of an unsatisfiable propositional formula and prove that this space contains no integral points.
We begin by proving some size and depth lower bounds for a recent proof system, Stabbing Planes, and along the way introduce some novel methods for doing so.
We then turn to the complexity of propositional contradictions generated uniformly from first order sentences, in Stabbing Planes and Sum-Of-Squares.
We finish by investigating the complexity-theoretic impact of the choice of method of generating these propositional contradictions in Sherali-Adams
Lower Bounds for (Batch) PIR with Private Preprocessing
In this paper, we study (batch) private information retrieval with private preprocessing. Private information retrieval (PIR) is the problem where one or more servers hold a database of bits and a client wishes to retrieve the -th bit in the database from the server(s). In PIR with private preprocessing (also known as offline-online PIR), the client is able to compute a private -bit hint in an offline stage that may be leveraged to perform retrievals accessing at most entries. For privacy, the client wishes to hide index from an adversary that has compromised some of the servers. In the batch PIR setting, the client performs queries to retrieve the contents of multiple entries simultaneously.
We present a tight characterization for the trade-offs between hint size and number of accessed entries during queries. For any PIR scheme that enables clients to perform batch retrievals of entries, we prove a lower bound of when . When , we prove that . Our lower bounds hold when the scheme errs with probability at most and against PPT adversaries that only compromise one out of servers for any . Our work also closes the multiplicative logarithmic gap for the single query setting as our lower bound matches known constructions. Our lower bounds hold in the model where each database entry is stored without modification but each entry may be replicated arbitrarily.
Finally, we show connections between PIR and the online matrix-vector (OMV) conjecture from fine-grained complexity. We present barriers for proving lower bounds for two-server PIR schemes in general computational models as they would immediately imply the OMV conjecture
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