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Journal of Quantum Information Science

A New Path to Create Solutions for Quantum Annealing Problems

José Luis Hevia, Ezequiel Murina, Guido Peterssen, Mario G. Piattini

Published Jan 1, 2021• 3 citations
Publisher

Abstract

Quantum computing has already become a technology to be used by large companies in finance, distribution, health care, chemistry, etc. Among the different approaches, quantum annealing is one of the most promising in the short term. However, software development platforms do not offer user-friendly interfaces for the definition of annealing problems. In this paper we present a solution to this problem: QPath®’s Annealer Compositor that facilitates the definition and execution of annealing algorithms in either quantum annealing or digital annealing computers. An example based on a nurse work schedule is used for illustrating this special interface.

DOI
10.4236/jqis.2021.113009

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Quantum computing has already become a technology to be used by large companies in finance, distribution, health care, chemistry, etc. Among the different approaches, quantum annealing is one of the most promising in the short term. However, software development platforms do not offer user-friendly interfaces for the definition of annealing problems. In this paper we present a solution to this problem: QPath®’s Annealer Compositor that facilitates the definition and execution of annealing algorithms in either quantum annealing or digital annealing computers. An example based on a nurse work schedule is used for illustrating this special interface.
The area of quantum computing is a relatively new and promising branch of computation that hopes to provide help especially in the field of non-deterministic polynomial hard problems. Due to the fact that quantum computers are a heuristically non-binary approach to computation, based on the effects of quantum mechanics, a bunch of advantages and disadvantages come with it. The following work focuses especially on so-called quantum annealers, a special architecture of quantum computers that one can see as an application-specific unit that is using certain quantum mechanical effects to target binary quadratic optimization problems of the class of non-deterministic polynomial-time problems and tries to solve them faster than classical computers can do. Because this is a whole new area, it lacks extensive software support which is addressed in this work. Hence the majority of the thesis aims to develop and extend software that enables users to solve problem instances on a quantum annealer fast and reliable. Furthermore, the necessary steps to solve combinatorial optimization problems are explained and the necessary background of quantum physics theory is given. In conclusion, the main question of this work can be described as follows: 'Is it possible to create an automated pipeline to solve problems on a D-Wave Systems n.d. quantum annealer?'.
Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.
Quantum computing promises to improve the speed and scalability of computations over that of classical computing hardware. At this early stage of quantum computer hardware development, software frameworks which support rapid prototyping of quantum solutions on small-scale hardware or simulators are necessary to explore the application of quantum algorithms to hard computational problems. We present a software library, “QxLib,” which incorporates symbolic computation of optimization functions for quantum annealers as one means to enable rapid prototyping. We demonstrate its effectiveness on integer linear programming and integer factorization problems.
Quantum annealing is a promising heuristic method to solve combinatorial optimization problems, and efforts to quantify performance on real-world problems provide insights into how this approach may be best used in practice. We investigate the empirical performance of quantum annealing to solve the Nurse Scheduling Problem (NSP) with hard constraints using the D-Wave 2000Q quantum annealing device. NSP seeks the optimal assignment for a set of nurses to shifts under an accompanying set of constraints on schedule and personnel. After reducing NSP to a novel Ising-type Hamiltonian, we evaluate the solution quality obtained from the D-Wave 2000Q against the constraint requirements as well as the diversity of solutions. For the test problems explored here, our results indicate that quantum annealing recovers satisfying solutions for NSP and suggests the heuristic method is potentially achievable for practical use. Moreover, we observe that solution quality can be greatly improved through the use of reverse annealing, in which it is possible to refine returned results by using the annealing process a second time. We compare the performance of NSP using both forward and reverse annealing methods and describe how this approach might be used in practice.
Quantum annealing is a computing paradigm that has the ambitious goal of efficiently solving large-scale combinatorial optimization problems of practical importance. However, many challenges have yet to be overcome before this goal can be reached. This perspectives article first gives a brief introduction to the concept of quantum annealing, and then highlights new pathways that may clear the way towards feasible and large scale quantum annealing. Moreover, since this field of research is to a strong degree driven by a synergy between experiment and theory, we discuss both in this work. An important focus in this article is on future perspectives, which complements other review articles, and which we hope will motivate further research.
Task scheduling is one of the crucial agendas in cloud computing technology. The system consists of various tasks namely termed cloudlets which need to be fed among various processing units for its execution. However, the algorithm that has been used to schedule tasks among various processing units should provide the optimum solution to a given problem. This research realizes the power of quantum computation specifically quantum annealing to provide the optimum solution to schedule the tasks among various processing units under the given constraints. Quantum Annealing is used for combinatorial optimization problems and as task scheduling is also one of such problems, it can be done in the quantum environment for optimal results. The Hybrid Solver provided by the D-Wave Quantum Computer has been used to solve the given problem.Leveraging the D-Wave Quantum Annealer, we analyze various scheduling scenarios and evaluate the performance in terms of makespan and throughput. Our findings reveal substantial improvements in scheduling efficiency compared to Partical Swarm Optimization scheduling. Specifically, we observed a notable reduction in makespan by up to 70% across diverse workload scenarios. Moreover, there was not much difference in throughput provided by both algorithms. The task scheduling among various processing units using quantum annealing has provided significant differences in makespan. Thus, using an annealing technique to schedule the tasks in cloud computing would significantly enhance in load balancing and efficient use of resources.