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optimization with photonic wave-based annealers

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 2023年第2241期381卷 20210409-20210409页

作者： Prabhakar, A. Shah, P. Gautham, U. Natarajan, V. Ramesh, V. Chandrachoodan, N. Tayur, S. Indian Inst Technol Madras Chennai 600036 India Carnegie Mellon Univ Pittsburgh PA 15213 USA

Many NP-hard combinatorial optimization (CO) problems can be cast as a quadratic unconstrained binary optimization model, which maps naturally to an Ising model. The final spin configuration in the Ising model can adiabatically arrive at a solution to a Hamiltonian, given a known set of interactions between spins. We enhance two photonic Ising machines (PIMs) and compare their performance against classical (Gurobi) and quantum (D-Wave) solvers. The temporal multiplexed coherent Ising machine (TMCIM) uses the bistable response of an electro-optic modulator to mimic the spin up and down states. We compare TMCIM performance on Max-cut problems. A spatial photonic Ising machine (SPIM) convolves the wavefront of a coherent laser beam with the pixel distribution of a spatial light modulator to adiabatically achieve a minimum energy configuration, and solve a number partitioning problem (NPP). Our computational results on Max-cut indicate that classical solvers are still a better choice, while our NPP results show that SPIM is better as the problem size increases. In both cases, connectivity in Ising hardware is crucial for performance. Our results also highlight the importance of better understanding which CO problems are most likely to benefit from which type of *** article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.

关键词： photonics Ising quadratic unconstrained binary optimization Max-cut number partitioning

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Quantum-compliant users scheduling optimization in joint transmission mobile access networks

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QUANTUM INFORMATION PROCESSING 2024年第7期23卷 262-262页

作者： Volpe, Deborah Cirillo, Giovanni Amedeo Fantini, Roberto Boella, Andrea Mondo, Giovanni Graziano, Mariagrazia Turvani, Giovanna Politecn Torino Dept Elect & Telecommun Corso Duca Abruzzi 24 Turin Italy TIM SpA Res & Dev Via Reiss Romoli Guglielmo I-10148 Turin Italy Politecn Torino Dept Appl Sci & Technol Corso Duca Abruzzi 24 I-10129 Turin Italy

Joint Transmission (JT) is the dynamic coordination of transmission and/or reception at multiple geographically separated sites to improve end-user service quality. When user equipment receives signals from multiple sites, downstream performance improves. An optimization problem arises in selecting the best user subset for JT within a multiple-input-multiple-output (MIMO) system. Unfortunately, a pure brute-force approach is not feasible due to exponential time growth with user combinations, unsuitable for real-time selection in mobile networks with users continuously changing in time. This article proposes quantum-compliant heuristics using quadratic unconstrained binary optimization (QUBO) for JT user scheduling. QUBO handles initial user selection, followed by brute-force exploration for the solution. Numerical results indicate that quantum-compliant methods decrease solution time without substantial accuracy loss compared to brute-force methods.

关键词： Joint transmission Multiuser multiple-input-multiple-output Multi-users scheduling optimization quadratic unconstrained binary optimization Quantum computing Hybrid quantum-classical algorithm

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A large population island framework for the unconstrained binary quadratic problem

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COMPUTERS & OPERATIONS RESEARCH 2024年 168卷

作者： Goudet, Olivier Goeffon, Adrien Hao, Jin-Kao Univ Angers LERIA 2 Blvd Lavoisier F-49045 Angers France

The unconstrained binary quadratic problem is an NP-hard problem and has applications in many fields. Recently, the problem has attracted much interest in the field of quantum optimization, as it is directly related to the Ising problem in physics and the development of quantum computers. However, effectively solving large instances of this problem remains a major challenge for existing solution methods. To advance the state of the art in solving the problem on a large scale, we propose an evolutionary algorithm with a very large population organized in different islands and integrating a new pairing and recombination method to produce promising offspring in each generation. Numerous experiments are conducted to evaluate the effects of different pairing strategies, crossovers, and migration topologies. This research has led to the discovery of new bounds for difficult instances of the maximum cut problem, which has been transformed using the binary quadratic formulation.

关键词： Combinatorial optimization Evolutionary search Island model Parallel search Heuristics unconstrained binary quadratic problem quadratic unconstrained binary optimization

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Qoolchain: A QUBO Preprocessing Toolchain for Enhancing Quantum optimization

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ADVANCED QUANTUM TECHNOLOGIES 2024年第0期

作者： Orlandi, Giacomo Volpe, Deborah Graziano, Mariagrazia Turvani, Giovanna Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy Dept Appl Sci & Technol I-10129 Turin Italy

Solving combinatorial optimization problems is crucial in research and industry but still challenging since these problems are usually NP-hard or NP-complete. Classical solvers struggle with their non-polynomial complexity. Although heuristic algorithms are widely used, they often fall short in execution time and accuracy, increasing the interest in quantum computing alternatives using quadratic unconstrained binary optimization (QUBO) formulations. However, current Noisy Intermediate-Scale Quantum (NISQ) computers and future early fault-tolerant quantum devices face limitations in qubit availability and circuit depth, necessitating preprocessing to reduce problem complexity. This study introduces Qoolchain, a QUBO preprocessing toolchain designed to reduce problem size and enhance solver performance. Developed in Cython, Qoolchain is compatible with major quantum frameworks and optimized for the Grover Adaptive Search (GAS) algorithm. It includes steps like persistency identification, decomposition, and probing to estimate function bounds, all with polynomial complexity. Qoolchain also proposes using the Grover Search algorithm for problem segments whose optimal value is known a priori from graph theory and Shannon decomposition to reduce QUBO problem complexity further. Evaluated against the D-Wave preprocessing toolchain on various problems, Qoolchain demonstrates higher efficiency and accuracy. It represents a significant advancement in enabling practical quantum solvers, addressing hardware limitations, and solving complex industry-relevant problems.

关键词： grover adaptive search grover search preprocessing quantum computing quadratic unconstrained binary optimization

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High-speed train timetable optimization based on space-time network model and quantum simulator

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QUANTUM INFORMATION PROCESSING 2023年第11期22卷 418-418页

作者： Xu, Hui-Zhang Chen, Jun-Hua Zhang, Xing-Chen Lu, Te-Er Gao, Tian-Ze Wen, Kai Ma, Yin Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing QBoson Quantum Technol Co Ltd Beijing 100015 Peoples R China

Timetable scheduling is a combinatorial optimization problem that presents formidable challenges for classical computers. This paper introduces a pioneering methodology for addressing the high-speed train timetabling problem through quantum computing. Initially, a comprehensive binary integer programming model, grounded in the space-time network, is proposed (M1). To manage the intricacy of model M1, a knapsack problem reformulation is employed to establish a simplified binary integer programming model (M2). Both M1 and M2 are subsequently converted into quadratic unconstrained binary optimization (QUBO) models to harness the potential of quantum computing. Several techniques, including the Gurobi solver, simulated annealing, and the coherent Ising machine (CIM) quantum simulator, are deployed to solve the model across four distinct scenarios of varying complexity. The findings indicate that CIM quantum simulator outperforms the simulated annealing method in terms of solution quality for medium-scale problems.

关键词： High-speed railway Timetable scheduling Quantum computing quadratic unconstrained binary optimization Space-time network

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QUBO Formulation Using Sequence Pair With Search Space Restriction for Rectangle Packing Problem

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IEEE ACCESS 2024年 12卷 156627-156638页

作者： Okada, Akihisa Otaki, Keisuke Matsumori, Tadayoshi Yoshida, Hiroaki Terada, Kotaro Togawa, Nozomu Toyota Cent Res & Dev Labs Inc Tokyo 1120004 Japan Waseda Univ Green Comp Syst Res Org Tokyo 1620042 Japan Waseda Univ Dept Comp Sci & Commun Engn Tokyo 1698555 Japan

The development of quantum annealing has stimulated interest in solving NP-hard problems, including various industrial problems, such as quadratic unconstrained binary optimization (QUBO), with specialized solvers. This study focuses on the rectangle packing problem (RPP), which is an NP-hard problem applicable to industrial applications. The objective of this problem is to place given items in a minimum area without overlap. Naive QUBO representations, such as a direct binary encoding of locations, could require a large number of bits, which limits the problem size, hindering the acquisition of the optimal solution. To circumvent this difficulty, we employ the concept of sequence pairs to efficiently represent the locations of items. We also propose a strategy that uses multiple sampling solutions obtained with a QUBO solver, where we employ new constraints on the number of the rectangles contributing to the placement area for restricting the search space to smoothly bridge the QUBO formulation of the sequence pair and the minimization of area of rectangle placement. We estimate an approximate curve for the optimum number of rectangles contributing to the placement area. Then, we add the penalty term concerning the deviation from the estimated optimal value on the objective function. In numerical experiments with instances with 4 to 10 rectangles, we demonstrate that the proposed sampling-based strategy can efficiently find feasible solutions. This strategy would be useful for dealing with more complex problems, such as rectangle orientation and three-dimensional problems, with a relatively small number of bits using sequence pairs.

关键词： Encoding Minimization optimization NP-hard problem Quantum annealing Annealing Linear programming Costs Strips Solids Rectangle packing problem sequence pair quadratic unconstrained binary optimization

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binary matrix factorization via collaborative neurodynamic optimization

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NEURAL NETWORKS 2024年 176卷 106348页

作者： Li, Hongzong Wang, Jun Zhang, Nian Zhang, Wei City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Univ Dist Columbia Dept Elect & Comp Engn Washington DC USA Chongqing Three Gorges Univ Chongqing Engn Res Ctr Internet Things & Intellige Chongqing Peoples R China

binary matrix factorization is an important tool for dimension reduction for high -dimensional datasets with binary attributes and has been successfully applied in numerous areas. This paper presents a collaborative neurodynamic optimization approach to binary matrix factorization based on the original combinatorial optimization problem formulation and quadratic unconstrained binary optimization problem reformulations. The proposed approach employs multiple discrete Hopfield networks operating concurrently in search of local optima. In addition, a particle swarm optimization rule is used to reinitialize neuronal states iteratively to escape from local minima toward better ones. Experimental results on eight benchmark datasets are elaborated to demonstrate the superior performance of the proposed approach against six baseline algorithms in terms of factorization error. Additionally, the viability of the proposed approach is demonstrated for pattern discovery on three datasets.

关键词： binary matrix factorization Collaborative neurodynamic optimization Discrete Hopfield network quadratic unconstrained binary optimization Pattern discovery

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Quantum Annealing with Inequality Constraints: The Set Cover Problem

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ADVANCED QUANTUM TECHNOLOGIES 2023年第11期6卷

作者： Djidjev, Hristo Los Alamos Natl Lab Los Alamos NM 87545 USA Bulgarian Acad Sci Inst Informat & Commun Technol Ul Acad G Bonchev Bl 25A Sofia 1113 Bulgaria

Quantum annealing is a promising method for solving hard optimization problems by transforming them into quadratic unconstrained binary optimization (QUBO) problems. However, when constraints are involved, particularly multiple inequality constraints, incorporating them into the objective function poses challenges. In this paper, the authors present two novel approaches for solving problems with multiple inequality constraints on a quantum annealer and apply them to the set cover problem (SCP). The first approach uses the augmented Lagrangian method to represent the constraints, while the second approach employs a higher-order binary optimization (HUBO) formulation. The experiments show that both approaches outperform the standard approach for solving the SCP on the D-Wave Advantage quantum annealer. The HUBO formulation performs slightly better than the augmented Lagrangian method in solving the SCP, but its scalability in terms of embeddability in the quantum chip is worse. The results demonstrate that the proposed augmented Lagrangian and HUBO methods can successfully implement a large number of inequality constraints, making them applicable to a broad range of constrained problems beyond the SCP. Quantum annealing is a promising method for solving hard optimization problems if they can be formulated as quadratic unconstrained binary optimization problems. Most problems of practical interest do include constraints, however, and this paper proposes two methods for dealing with multiple inequality constraints that are more accurate than the existing ones, and applies them to the set cover ***

关键词： augmented Lagrangian method constrained optimization D-Wave higher-order binary optimization Ising problem quadratic penalty method quantum annealing quadratic unconstrained binary optimization set cover problem

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Quantum computing for future real-time building HVAC controls

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APPLIED ENERGY 2023年第1期334卷

作者： Deng, Zhipeng Wang, Xuezheng Dong, Bing Syracuse Univ Dept Mech & Aerosp Engn Syracuse NY 13244 USA

Buildings contribute to more than 70% of overall U.S. electricity usage and greenhouse gas (GHG) emissions. HVAC systems in buildings often consume more than 40% of the total building energy usage. To reduce such high energy use, numerous control strategies including optimal and predictive controls have been developed and demonstrated. To achieve a near real-time solution, most previous research has simplified the non-linearity of building thermodynamics and provided an approximate optimal solution. The future HVAC control optimizes more connected devices in buildings, which requires a rapid and accurate response, not only to the building itself but also to the grid signals. It also poses the challenge of solving non-linear problems with discrete variables. With the recent development of quantum computers, this has become feasible. In this paper, we developed a new optimization solution based on quantum annealing for model predictive control (MPC) of a rooftop unit (RTU). Compared to traditional optimization methods, we obtained similar solutions with less than 2% differences and improved computational speed from hours to seconds. We also demonstrated an 80% reduction in total electricity consumption and a 21% reduction in electricity bills by considering day-ahead price time-of-use demand response signals. Quantum computing has proven capable of solving large-scale non-linear discrete optimization problems for building energy systems.

关键词： Discrete optimization Mixed-integer programming quadratic unconstrained binary optimization Quantum annealing Energy-efficient building

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Harnessing quantum power:Revolutionizing materials design through advanced quantum computation

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Materials Genome Engineering Advances 2024年第4期2卷 2-25页

作者： Zikang Guo Rui Li Xianfeng He Jiang Guo Shenghong Ju China-UK Low Carbon College Shanghai Jiao Tong UniversityShanghaiChina Graduate School of Frontier Science The University of TokyoKashiwaChibaJapan Materials Genome Initiative Center School of Material Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

The design of advanced materials for applications in areas of photovoltaics,energy storage,and structural engineering has made significant ***,the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing,fabrication,and *** review introduces a comprehensive methodology for materials design using cutting-edge quantum computing,with a particular focus on quadratic unconstrained binary optimization(QUBO)and quantum machine learning(QML).We introduce the loop framework for QUBO-empowered materials design,including constructing high-quality datasets that capture critical material properties,employing tailored computational methods for precise material modeling,developing advanced figures of merit to evaluate performance metrics,and utilizing quantum optimization algorithms to discover optimal *** addition,we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative *** review also highlights advanced active learning strategies that integrate quantum artificial intelligence,offering a more efficient pathway to explore the vast,complex material design ***,we discuss the key challenges and future opportunities for QML in material design,emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.

关键词： active learning framework materials design and optimization quadratic unconstrained binary optimization quantum machine learning

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