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Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
Multistart Methods for Quantum Approximate Optimization
Hybrid quantum-classical algorithms such as the quantum approximate
optimization algorithm (QAOA) are considered one of the most promising
approaches for leveraging near-term quantum computers for practical
applications. Such algorithms are often implemented in a variational form,
combining classical optimization methods with a quantum machine to find
parameters to maximize performance. The quality of the QAOA solution depends
heavily on quality of the parameters produced by the classical optimizer.
Moreover, the presence of multiple local optima in the space of parameters
makes it harder for the classical optimizer. In this paper we study the use of
a multistart optimization approach within a QAOA framework to improve the
performance of quantum machines on important graph clustering problems. We also
demonstrate that reusing the optimal parameters from similar problems can
improve the performance of classical optimization methods, expanding on similar
results for MAXCUT
Advantages of Unfair Quantum Ground-State Sampling
The debate around the potential superiority of quantum annealers over their
classical counterparts has been ongoing since the inception of the field by
Kadowaki and Nishimori close to two decades ago. Recent technological
breakthroughs in the field, which have led to the manufacture of experimental
prototypes of quantum annealing optimizers with sizes approaching the practical
regime, have reignited this discussion. However, the demonstration of quantum
annealing speedups remains to this day an elusive albeit coveted goal. Here, we
examine the power of quantum annealers to provide a different type of quantum
enhancement of practical relevance, namely, their ability to serve as useful
samplers from the ground-state manifolds of combinatorial optimization
problems. We study, both numerically by simulating ideal stoquastic and
non-stoquastic quantum annealing processes, and experimentally, using a
commercially available quantum annealing processor, the ability of quantum
annealers to sample the ground-states of spin glasses differently than
classical thermal samplers. We demonstrate that i) quantum annealers in general
sample the ground-state manifolds of spin glasses very differently than thermal
optimizers, ii) the nature of the quantum fluctuations driving the annealing
process has a decisive effect on the final distribution over ground-states, and
iii) the experimental quantum annealer samples ground-state manifolds
significantly differently than thermal and ideal quantum annealers. We
illustrate how quantum annealers may serve as powerful tools when complementing
standard sampling algorithms.Comment: 13 pages, 11 figure
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