scholarly journals Exploring Embeddings for MIMO Channel Decoding on Quantum Annealers

2021 ◽  
Vol 13 (1) ◽  
pp. 11-17
Author(s):  
Ádám Marosits ◽  
Zsolt Tabi ◽  
Zsófia Kallus ◽  
Péter Vaderna ◽  
István Gódor ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Heuristic Method ◽  
Solution Space ◽  
Mimo Channel ◽  
Quantum Annealing ◽  
Novel Device ◽  
Main Challenge ◽  
Wide Range ◽  
Shift Keying ◽  
D Wave

Quantum Annealing provides a heuristic method leveraging quantum mechanics for solving Quadratic Unconstrained Binary Optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for a wide range of use cases. In particular, a novel device, the D-Wave Advantage has been recently released. In this paper, we study the applicability of Maximum Likelihood (ML) Channel Decoder problems for MIMO scenarios in centralized RAN. The main challenge for exact optimization of ML decoders with ever-increasing demand for higher data rates is the exponential increase of the solution space with problem sizes. Since current 5G solutions can only use approximate methodologies, Kim et al. [1] leveraged Quantum Annealing for large MIMO problems with phase shift keying and quadrature amplitude modulation scenarios. Here, we extend upon their work and present embedding limits for both more complex modulation and higher receiver / transmitter numbers using the Pegasus P16 topology of the D-Wave Advantage system.

scholarly journals A QUBO Formulation of Minimum Multicut Problem Instances in Trees for D-Wave Quantum Annealers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
William Cruz-Santos ◽  
Salvador E. Venegas-Andraca ◽  
Marco Lanzagorta
Keyword(s):  
Optimization Problems ◽  
Theoretical Computer Science ◽  
Scaling Analysis ◽  
Quantum Annealing ◽  
Theoretical Computer ◽  
Combinatorial Optimization Problems ◽  
Hard Problems ◽  
Problem Instances ◽  
Annealing Algorithms ◽  
D Wave

AbstractQuantum annealing algorithms were introduced to solve combinatorial optimization problems by taking advantage of quantum fluctuations to escape local minima in complex energy landscapes typical of NP − hard problems. In this work, we propose using quantum annealing for the theory of cuts, a field of paramount importance in theoretical computer science. We have proposed a method to formulate the Minimum Multicut Problem into the QUBO representation, and the technical difficulties faced when embedding and submitting a problem to the quantum annealer processor. We show two constructions of the quadratic unconstrained binary optimization functions for the Minimum Multicut Problem and we review several tradeoffs between the two mappings and provide numerical scaling analysis results from several classical approaches. Furthermore, we discuss some of the expected challenges and tradeoffs in the implementation of our mapping in the current generation of D-Wave machines.

scholarly journals Analog errors in quantum annealing: doom and hope

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Adam Pearson ◽  
Anurag Mishra ◽  
Itay Hen ◽  
Daniel A. Lidar
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Quantum Annealing ◽  
Ising Spin ◽  
Analog Control ◽  
Quantum Device ◽  
Correction Scheme ◽  
Simple Error ◽  
Error Suppression ◽  
D Wave

AbstractQuantum annealing has the potential to provide a speedup over classical algorithms in solving optimization problems. Just as for any other quantum device, suppressing Hamiltonian control errors will be necessary before quantum annealers can achieve speedups. Such analog control errors are known to lead to $$J$$J-chaos, wherein the probability of obtaining the optimal solution, encoded as the ground state of the intended Hamiltonian, varies widely depending on the control error. Here, we show that $$J$$J-chaos causes a catastrophic failure of quantum annealing, in that the scaling of the time-to-solution metric becomes worse than that of a deterministic (exhaustive) classical solver. We demonstrate this empirically using random Ising spin glass problems run on the two latest generations of the D-Wave quantum annealers. We then proceed to show that this doomsday scenario can be mitigated using a simple error suppression and correction scheme known as quantum annealing correction (QAC). By using QAC, the time-to-solution scaling of the same D-Wave devices is improved to below that of the classical upper bound, thus restoring hope in the speedup prospects of quantum annealing.

An approach for making optimal decisions in building energy efficiency retrofit projects

2016 ◽  
Vol 27 (3) ◽  
pp. 348-368 ◽  
Author(s):  
Aslihan Senel Solmaz ◽  
Fahriye H Halicioglu ◽  
Suat Gunhan
Keyword(s):  
Energy Efficiency ◽  
Solution Space ◽  
Building Energy ◽  
Multiple Criteria ◽  
Optimization Approach ◽  
Building Energy Efficiency ◽  
Main Challenge ◽  
Study Results ◽  
Wide Range ◽  
Project Objectives

The study presents an optimization-based decision support approach to determine the optimal energy efficiency retrofit options in existing buildings. The main challenge encountered in the decision-making process of building retrofit projects is the selection of an optimal set of solutions within a wide range of solution space according to multiple criteria. In order to overcome this problem, this study uses an integrated optimization approach by combining both the variance-based sensitivity analysis and optimization methods to maximize energy savings and optimize financial returns in building energy efficiency retrofit projects. The proposed approach was applied to an existing public school building in Izmir, Turkey that represents hot-humid climate to test its validity and performance. The optimizations were performed and results were obtained for three different scenarios whose content was determined based on project objectives and constraints fulfilled by a survey conducted by building users. The case study results indicate that the proposed approach is effective in defining the application order of retrofit options by identifying the most influential parameters for building energy efficiency and finding out the optimal retrofit solutions per multiple criteria.

scholarly journals On Particle Swarm Optimization for MIMO Channel Estimation

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Christopher Knievel ◽  
Peter Adam Hoeher
Keyword(s):  
Particle Swarm Optimization ◽  
Channel Estimation ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mimo Channel ◽  
Swarm Optimization ◽  
Minimum Mean Squared Error ◽  
Wide Range ◽  
Mmse Estimator ◽  
Number Of Iterations

Evolutionary algorithms, in particular particle swarm optimization (PSO), have recently received much attention. PSO has successfully been applied to a wide range of technical optimization problems, including channel estimation. However, most publications in the area of digital communications ignore improvements developed by the PSO community. In this paper, an overview of the original PSO is given as well as improvements that are generally applicable. An extension of PSO termed cooperative PSO (CPSO) is applied for MIMO channel estimation, providing faster convergence and, thus, lower overall complexity. Instead of determining the average iterations needed empirically, a method to calculate the maximum number of iterations is developed, which enables the evaluation of the complexity for a wide range of parameters. Knowledge of the required number of iterations is essential for a practical receiver design. A detailed discussion about the complexity of the PSO algorithm and a comparison to a conventional minimum mean squared error (MMSE) estimator are given. Furthermore, Monte Carlo simulations are provided to illustrate the MSE performance compared to an MMSE estimator.

scholarly journals Analysis of the Results of Applying the Bee Colony Method in the Problem of Coloring General Graphs

2021 ◽  
Vol 24 (4) ◽  
pp. 126-145
Author(s):  
А. O. Pshenichnykh ◽  
E. I. Vatutin
Keyword(s):  
Convergence Rate ◽  
Chromatic Number ◽  
Optimization Problems ◽  
Random Search ◽  
Heuristic Method ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Wide Range

Purpose of research. We have discovered a wide range of problems that are important in practice and which can be reduced in polynomial time to discrete combinatorial optimization problems, many of which can be solved using graph theory. One of these tasks is finding the chromatic number of a graph and its corresponding coloring. Taking into account the fact that the combinatorial problem of finding the chromatic number of a graph belongs to the complexity class and does not allow obtaining an optimal solution in a rational time for problems of practically important dimension, the search for a suitable heuristic method that allows obtaining high-quality solutions with low costs required for computation is demanded and relevant. The aim of the study is to analyze the results of using the bee colony method in the task at hand. The tasks of this research are: description of algorithmic techniques in a formalized form, which make it possible to apply the bee colony method in the problem to be solved, making modifications to the bee colony method that increase the efficiency of the method, namely the quality of the resulting final colorings, as well as the determination of factors affecting the quality and the time spent in finding solutions. Methods. To conduct research in the selected area, computational experiments were organized based on the use of heuristic methods in the problem under consideration. Meta-optimization of the tuning parameters of the methods and determination of their convergence rate was carried out, as well as a comparison of the quality and time of obtaining solutions. Results. As a result of the study, the convergence rate of the method was found to be higher than that of the random walk method; the dependence of the quality of the resulting final colorings on the graph size N and density d was found. It was found that the chosen method is faster than the method of weighted random enumeration with the variation of vertices according to the minimum of admissible colors on »67% , which currently generates solutions with the lowest chromatic number, while losing quality to it on »7% . A higher rate of convergence was noticed when compared with the method of random walks, the principle of which is the same as that of foraging bees. Conclusion. It was found that the bee colony method finds colorings with the same average chromatic number in fewer iterations than the random walk method, i.e. it has a higher convergence rate, while remaining significantly fast relative to the method of random search with a variation of vertices to reduce the allowed colors.

scholarly journals Syllabification of the Divine Comedy

2021 ◽  
Vol 14 (3) ◽  
pp. 1-26
Author(s):  
Andrea Asperti ◽  
Stefano Dal Bianco
Keyword(s):  
Text Processing ◽  
Solution Space ◽  
Generative Models ◽  
Divine Comedy ◽  
Point Of View ◽  
Computer Assisted ◽  
Wide Range ◽  
The Divine Comedy ◽  
Left And Right ◽  
Assisted Analysis

We provide a syllabification algorithm for the Divine Comedy using techniques from probabilistic and constraint programming. We particularly focus on the synalephe , addressed in terms of the "propensity" of a word to take part in a synalephe with adjacent words. We jointly provide an online vocabulary containing, for each word, information about its syllabification, the location of the tonic accent, and the aforementioned synalephe propensity, on the left and right sides. The algorithm is intrinsically nondeterministic, producing different possible syllabifications for each verse, with different likelihoods; metric constraints relative to accents on the 10th, 4th, and 6th syllables are used to further reduce the solution space. The most likely syllabification is hence returned as output. We believe that this work could be a major milestone for a lot of different investigations. From the point of view of digital humanities it opens new perspectives on computer-assisted analysis of digital sources, comprising automated detection of anomalous and problematic cases, metric clustering of verses and their categorization, or more foundational investigations addressing, e.g., the phonetic roles of consonants and vowels. From the point of view of text processing and deep learning, information about syllabification and the location of accents opens a wide range of exciting perspectives, from the possibility of automatic learning syllabification of words and verses to the improvement of generative models, aware of metric issues, and more respectful of the expected musicality.

scholarly journals Benchmarking Quantum Annealing Against “Hard” Instances of the Bipartite Matching Problem

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Daniel Vert ◽  
Renaud Sirdey ◽  
Stéphane Louise
Keyword(s):  
Simulated Annealing ◽  
Polynomial Time ◽  
Optimal Solution ◽  
Quantum Computers ◽  
Quantum Annealing ◽  
Maximum Cardinality ◽  
Matching Problem ◽  
Bipartite Matching ◽  
Wave Device ◽  
D Wave

AbstractThis paper experimentally investigates the behavior of analog quantum computers as commercialized by D-Wave when confronted to instances of the maximum cardinality matching problem which is specifically designed to be hard to solve by means of simulated annealing. We benchmark a D-Wave “Washington” (2X) with 1098 operational qubits on various sizes of such instances and observe that for all but the most trivially small of these it fails to obtain an optimal solution. Thus, our results suggest that quantum annealing, at least as implemented in a D-Wave device, falls in the same pitfalls as simulated annealing and hence provides additional evidences suggesting that there exist polynomial-time problems that such a machine cannot solve efficiently to optimality. Additionally, we investigate the extent to which the qubits interconnection topologies explains these latter experimental results. In particular, we provide evidences that the sparsity of these topologies which, as such, lead to QUBO problems of artificially inflated sizes can partly explain the aforementioned disappointing observations. Therefore, this paper hints that denser interconnection topologies are necessary to unleash the potential of the quantum annealing approach.

Gearbox (China) Ltd.: Will the Company's ERP System Support its Ambitious Growth Strategy?

2004 ◽  
Vol 19 (2) ◽  
pp. 140-148 ◽  
Author(s):  
Kai Reimers
Keyword(s):  
Operations Management ◽  
Emerging Market ◽  
Growth Strategy ◽  
Medium Size ◽  
Cultural Issues ◽  
Erp System ◽  
Organizational Behaviour ◽  
Main Challenge ◽  
Wide Range ◽  
Mechanical Devices

This case describes the experience of a wholly foreign-owned manufacturing company in Tianjin/China regarding the use of its ERP system in its main functional departments, purchasing, production planning, sales/distribution, and finance. The company is part of a group which is a global leader in the manufacturing and distribution of mechanical devices, called gearboxes, that are needed to drive a wide range of facilities such as escalators and baggage conveyor belts in airports. It has entered China in 1995 and the Tianjin manufacturing facility has soon become a hub for the Asian market. The main challenge confronting the management team is to support the breakneck growth rate of this young company. The company's ERP system plays a crucial role in this task. However, it seems that middle managers are frequently hitting an invisible wall when trying to expand the use of the ERP system in order to cope with ever-increasing workloads and coordination tasks. This case serves to illustrate cultural issues implicated in the use of an enterprise wide information system in a medium size company operating in an emerging market economy. In addition, issues of operations management, global management, and organizational behaviour are addressed.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

Sign in / Sign up

Export Citation Format

Share Document