Leveraging Quantum Annealing for Election Forecasting

Henderson, Maxwell; Novak, John; Cook, Tristan

doi:10.7566/jpsj.88.061009

Cited by 30 publications

(14 citation statements)

References 6 publications

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“…The quantum-enabled forecasting can further provide the capability to take societal factors into account. As an example, the potential impact of adiabatic quantum computation is investigated in predicting the 2016 Presidential election in [56]. The forecast model outcomes illustrate a significant performance improvement compared to the best in class election modeling group, which could be a new technique to bring to the broader conversation of modeling in future election forecasts.…”

Section: B Forecastingmentioning

confidence: 99%

Quantum-Enhanced Grid of the Future: A Primer

et al. 2020

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Computing plays a significant role in power system analytics. As mathematical challenges increase and data become the epicenter of modern decision making, substantial progress needs to be made to draw on emerging analytics and computing technologies. Quantum computing is a groundbreaking technology in information processing that can support the global efforts in addressing power system challenges and in further envisioning the grid of the future. However, despite extensive research activities in quantum computing applications in various sectors, its application to power systems has remained mostly unexamined. It is necessary to have an across-the-board view of the quantum computing technology applications in power systems, and in particular, in building the grid of the future. This paper discusses the essential elements of quantum computing and presents a review of issues concerning this technology. The paper further provides an in-depth discussion of the potential of quantum computing in improving analytical and computing capabilities in solving multiple power system problems.

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Section: B Forecastingmentioning

confidence: 99%

Quantum-Enhanced Grid of the Future: A Primer

et al. 2020

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“…Другое направление -решение технических проблем. Здесь можно выделить встраивание математических задач в квантовые биты и их соединения [9, 10,15], подавление ошибок из-за специфики аппарата [20,22], масштабирование больших объемов данных для соответствия среде [20]. Cравнение устройств квантового отжига с традиционными термальными устройствами для отжига [17,19].…”

Section: сведение задачи к минимизации функции изингаunclassified

Adiabatic computing in the knowledge economy

Анатолий¹

2018

Herald of CEMI

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Центральный экономико-математический институт РАН Российская Федерация, Москва Аннотация Рассматривается возможность приведения характерных для экономики знаний оптимизационных задач с булевскими переменными к форме, удобной для решения методом квантового отжига, то есть к задаче минимизации целевой функции Изинга без ограничений. Приведение к требуемой форме означает замену всех ограничений штрафными функция-ми, содержащими только линейные и квадратичные члены. К такой форме приводится типичная для экономики знаний задача оптимизации, содержащая балансовые ограничения с операцией максимума вместо обычного сложения. Содержательно это означает возможность использовать однажды полученное знание сколько угодно раз, при этом повторно полученное знание ничего не добавляет к уже имеющемуся. Предлагаемый подход в принципе позволяет свести задачу к форме, удобной для компьютеров серии D-Wave-ре-ально работающих квантовых компьютеров, использующих метод квантового отжига.

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“…Discrete optimization problems have ubiquitous applications in various fields and, in particular, many NP-hard combinatorial optimization problems can be mapped to a quadratic Ising model [1] or, equivalently, to a quadratic unconstrained binary optimization (QUBO) problem. Such problems arise naturally in many fields of research, including finance [2], chemistry [3,4], biology [5,6], logistics and scheduling [7,8], and machine learning [9][10][11][12]. For this reason, there is much interest in solving these problems efficiently, both in academia and in industry.…”

Section: Introductionmentioning

confidence: 99%

Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer

Aramon¹,

Rosenberg²,

Valiante³

et al. 2019

Front. Phys.

352

277

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The Fujitsu Digital Annealer is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 1024 variables. The Digital Annealer's algorithm is currently based on simulated annealing; however, it differs from it in its utilization of an efficient parallel-trial scheme and a dynamic escape mechanism. In addition, the Digital Annealer exploits the massive parallelization that custom application-specific CMOS hardware allows. We compare the performance of the Digital Annealer to simulated annealing and parallel tempering with isoenergetic cluster moves on two-dimensional and fully connected spin-glass problems with bimodal and Gaussian couplings. These represent the respective limits of sparse versus dense problems, as well as high-degeneracy versus low-degeneracy problems. Our results show that the Digital Annealer currently exhibits a time-to-solution speedup of roughly two orders of magnitude for fully connected spin-glass problems with bimodal or Gaussian couplings, over the single-core implementations of simulated annealing and parallel tempering Monte Carlo used in this study. The Digital Annealer does not appear to exhibit a speedup for sparse two-dimensional spin-glass problems, which we explain on theoretical grounds. We also benchmarked an early implementation of the Parallel Tempering Digital Annealer. Our results suggest an improved scaling over the other algorithms for fully connected problems of average difficulty with bimodal disorder. The next generation of the Digital Annealer is expected to be able to solve fully connected problems up to 8192 variables in size. This would enable the study of fundamental physics problems and industrial applications that were previously inaccessible using standard computing hardware or special-purpose quantum annealing machines. arXiv:1806.08815v3 [physics.comp-ph]

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Leveraging Quantum Annealing for Election Forecasting

Cited by 30 publications

References 6 publications

Quantum-Enhanced Grid of the Future: A Primer

Quantum-Enhanced Grid of the Future: A Primer

Adiabatic computing in the knowledge economy

Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer

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