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37th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Qian, Wenyang Basili, Robert Eshaghian-Wilner, Mary Khokhar, Ashfaq Luecke, Glenn Vary, James P. Univ Santiago de Compostela Inst Galego Fis Altas Enerxias IGFAE E-15782 Galicia Spain Iowa State Univ Dept Math Dept Phys & Astron Ames IA USA Iowa State Univ Dept Math Ames IA USA Iowa State Univ Dept Elect & Comp Engn Ames IA USA Iowa State Univ Dept Math Ames IA USA Iowa State Univ Dept Phys & Astron Ames IA USA

ISBN: (纸本)9798350311990

The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also being used heavily as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this work, we formulate the symmetric TSP as an optimization problem, which we solve using the quantum approximate optimization algorithm (QAOA) approach. By adopting an improved qubit encoding strategy and a layerwise learning optimization protocol, we obtain numerical results on the gate-based digital quantum simulator for the 3-, 4-, and 5city TSPs. Specifically, we focus on three QAOA mixer designs to evaluate their performances in terms of numerical accuracy and optimization cost. Based on our results, we propose that a well-balanced QAOA mixer design is more prominent on gatebased simulators or realistic quantum devices in the near future. In addition, we study the sensitivity of TSP graph properties such as graph skewness and penalty weight in the TSP-QAOA simulation. Overall, our results prove digital quantum simulation is a powerful candidate for obtaining the optimal solution to the TSP.

关键词： quantum Computing quantum Simulation quantum approximate optimization algorithm Traveling Salesman Proble

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Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational quantum algorithms

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INFORMS JOURNAL ON COMPUTING 2025年第1期37卷 1-196, C2页

作者： Ha, Yunsoo Shashaani, Sara Menickelly, Matt Natl Renewable Energy Lab Computat Sci Ctr Golden CO 80401 USA North Carolina State Univ Edward P Fitts Dept Ind & Syst Engn Raleigh NC 27695 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA

The quantum approximate optimization algorithm (QAOA) has enjoyed increasing attention in noisy, intermediate-scale quantum computing with its application to combinatorial optimization problems. QAOA has the potential to demonstrate a quantum advantage for NP-hard combinatorial optimization problems. As a hybrid quantumclassical algorithm, the classical component of QAOA resembles a simulation optimization problem in which the simulation outcomes are attainable only through a quantum computer. The simulation that derives from QAOA exhibits two unique features that can have a substantial impact on the optimization process: (i) the variance of the stochastic objective values typically decreases in proportion to the optimality gap, and (ii) querying samples from a quantum computer introduces an additional latency overhead. In this paper, we introduce a novel stochastic trust-region method derived from a derivative-free, adaptive sampling trust-region optimization method intended to efficiently solve the classical optimization problem in QAOA by explicitly taking into account the two mentioned characteristics. The key idea behind the proposed algorithm involves constructing two separate local models in each iteration: a model of the objective function and a model of the variance of the objective function. Exploiting the variance model allows us to restrict the number of communications with the quantum computer and also helps navigate the nonconvex objective landscapes typical in QAOA optimization problems. We numerically demonstrate the superiority of our proposed algorithm using the SimOpt library and Qiskit when we consider a metric of computational burden that explicitly accounts for communication costs.

关键词： quantum approximate optimization algorithm simulation optimization derivative-free optimization two-stage estimation state-dependent noise trust-region optimization adaptive sampling

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Implementing a Hybrid quantum Neural Network for Wind Speed Forecasting: Insights from quantum Simulator Experiences

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ENERGIES 2025年第7期18卷 1771-1771页

作者： Hong, Ying-Yi Santos, Jay Bhie D. Chung Yuan Christian Univ Dept Elect Engn Taoyuan City 320314 Taiwan

The intermittent nature of wind speed poses challenges for its widespread utilization as an electrical power generation source. As the integration of wind energy into the power system increases, accurate wind speed forecasting becomes crucial. The reliable scheduling of wind power generation heavily relies on precise wind speed forecasts. This paper presents an extended work that focuses on a hybrid model for 24 h ahead wind speed forecasting. The proposed model combines residual Long Short-Term Memory (LSTM) and a quantum neural network that is studied by a quantum simulator, leveraging the support of NVIDIA Compute Unified Device Architecture (CUDA). To ensure the desired accuracy, a comparative analysis is conducted, examining the qubit count and quantum circuit depth of the proposed model. The execution time required for the model is significantly reduced when the GPU incorporates CUDA, accounting for only 8.29% of the time required by a classical CPU. In addition, different quantum embedding layers with various entangler layers in the quantum neural network are explored. The simulation results utilizing an offshore wind farm dataset demonstrate that the proper number of qubits and embedding layer can achieve favorable 24 h ahead wind speed forecasts.

关键词： deep learning graphical processing unit quantum approximate optimization algorithm residual long short-term memory wind speed forecasting

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Robust Control optimization for quantum approximate optimization algorithms

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IFAC-PapersOnLine 2020年第2期53卷 242-249页

作者： Yulong Dong Xiang Meng Lin Lin Robert Kosut K. Birgitta Whaley Berkeley Center for Quantum Information and Computation Berkeley California 94720 USA Department of Chemistry University of California Berkeley California 94720 USA Department of Mathematics University of California Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA SC Solutions Sunnyvale California 94085 USA

quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation.

关键词： quantum approximate optimization algorithm robust control sequential convex programming error mitigation

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quantum approximate Bayesian optimization algorithms With Two Mixers and Uncertainty Quantification

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON quantum ENGINEERING 2023年第1期4卷 1页

作者： Kim, Jungin E. Wang, Yan Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA

The searching efficiency of the quantum approximate optimization algorithm is dependent on both the classical and quantum sides of the algorithm. Recently, a quantum approximate Bayesian optimization algorithm (QABOA) that includes two mixers was developed, where surrogate-based Bayesian optimization is applied to improve the sampling efficiency of the classical optimizer. A continuous-time quantum walk mixer is used to enhance exploration, and the generalized Grover mixer is also applied to improve exploitation. In this article, an extension of the QABOA is proposed to further improve its searching efficiency. The searching efficiency is enhanced through two aspects. First, two mixers, including one for exploration and the other for exploitation, are applied in an alternating fashion. Second, uncertainty of the quantum circuit is quantified with a new quantum Mat & eacute;rn kernel based on the kurtosis of the basis state distribution, which increases the chance of obtaining the optimum. The proposed new two-mixer QABOA's with and without uncertainty quantification are compared with three single-mixer QABOA's on five discrete and four mixed-integer problems. The results show that the proposed two-mixer QABOA with uncertainty quantification has the best performance in efficiency and consistency for five out of the nine tested problems. The results also show that QABOA with the generalized Grover mixer performs the best among the single-mixer algorithms, thereby demonstrating the benefit of exploitation and the importance of dynamic exploration-exploitation balance in improving searching efficiency.

关键词： Mixers optimization Uncertainty quantum circuit quantum computing Approximation algorithms Qubit Exploration-exploitation mixed-integer optimization noisy intermediate-scale quantum algorithms and devices quantum approximate optimization algorithm uncertainty quantification

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quantum Testing of Recommender algorithms on GPU-Based quantum Simulators

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COMPUTERS 2025年第4期14卷 137-137页

作者： Liu, Chenxi Lee, W. Bernard Constantinides, Anthony G. Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China HedgeSPA Pvt Ltd 12 Woodlands Sq05-70 Woods Sq Tower 1 Singapore 737715 Singapore Imperial Coll London AI & Data Analyt Lab London SW7 2AZ England

This study explores the application of quantum computing in asset management, focusing on the use of the quantum approximate optimization algorithm (QAOA) to solve specific classes of financial asset recommendation problems. While quantum computing holds promise for combinatorial optimization tasks, its application to portfolio management faces significant challenges in scalability for practical implementations. In this work, we model the problem using a graph representation where nodes represent investors, and edges reflect significant similarities in asset choices. We test the proposed method using quantum simulators, including cuquantum, Cirq-GPU, and Cirq with IonQ, and compare the performance of quantum optimization against classical brute-force methods. Our results suggest that quantum algorithms may offer computational advantages for certain use cases, though classical heuristics also provide competitive performance for smaller datasets. This study contributes to the ongoing investigation into the potential of quantum computing for real-time financial decision-making, providing insights into both its applicability and limitations in asset management for larger and more complex investor datasets.

关键词： quantum computing quantum approximate optimization algorithm max-cut problem financial asset recommendation IonQ graph neural networks

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Hybrid quantum approximate optimization Using Enhanced Ant Colony optimization to Solve Large-Scale Combinatorial optimization Problems 8

Hybrid Quantum Approximate Optimization Using Enhanced Ant C...

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8th International Conference on Soft Computing & Machine Intelligence (ISCMI)

作者： Ghimire, Bishad Mahmood, Ausif Elleithy, Khaled Univ Bridgeport Comp Sci & Engn Bridgeport CT 06604 USA

ISBN: (纸本)9781728186832

quantum computing is a promising technology that may provide breakthrough solutions to today's difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. A class of algorithms referred to as quantum approximate optimization algorithm (QAOA) have been recently proposed. QAOA attempts to approximately solve hard problems using a protocol know as bang-bang. The technique is based on the unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. QAOA has been explored in the context of several optimization problems such as Max-Cut problem, variational Eigenvalue problem etc. Recently, attempts have been made to create QAOA for the Traveling Salesman Problem (TSP). Due to small node size and limited solution capability of the currently available quantum computers and/or simulators, we develop a hybrid approach where subgraphs of a TSP tour are executed on a quantum computer and the results from the quantum optimization are combined in further optimization of the whole tour. Since the local optimization of a subgraph is prone to getting stuck in a local minima, we overcome this problem by using a parallel Ant Colony optimization algorithm with periodic pheromone exchange between colonies. Our results are encouraging and yield optimum results for benchmarks with less than 50 nodes, and usually within 1% of the optimal solution for benchmarks with around 200 nodes.

关键词： quantum algorithm quantum approximate optimization algorithm combinatorial optimization problem ant colony optimization algorithm

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Improving the performance of quantum approximate optimization for preparing non-trivial quantum states without translational symmetry

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NEW JOURNAL OF PHYSICS 2023年第1期25卷 013015页

作者： Sun, Zheng-Hang Wang, Yong-Yi Cui, Jian Fan, Heng Chinese Acad Sci Inst Phys Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Phys Sci Beijing 100190 Peoples R China Beihang Univ Sch Phys Beijing 100191 Peoples R China Songshan Lake Mat Lab Dongguan 523808 Guangdong Peoples R China Univ Chinese Acad Sci CAS Ctr Excellence Topol Quantum Computat Beijing 100190 Peoples R China Beijing Acad Quantum Informat Sci Beijing 100193 Peoples R China

The variational preparation of complex quantum states using the quantum approximate optimization algorithm (QAOA) is of fundamental interest, and becomes a promising application of quantum computers. Here, we systematically study the performance of QAOA for preparing ground states of target Hamiltonians near the critical points of their quantum phase transitions, and generating Greenberger-Horne-Zeilinger (GHZ) states. We reveal that the performance of QAOA is related to the translational invariance of the target Hamiltonian: without the translational symmetry, for instance due to the open boundary condition (OBC) or randomness in the system, the QAOA becomes less efficient. We then propose a generalized QAOA assisted by the parameterized resource Hamiltonian (PRH-QAOA), to achieve a better performance. In addition, based on the PRH-QAOA, we design a low-depth quantum circuit beyond one-dimensional geometry, to generate GHZ states with perfect fidelity. The experimental realization of the proposed scheme for generating GHZ states on Rydberg-dressed atoms is discussed. Our work paves the way for performing QAOA on programmable quantum processors without translational symmetry, especially for recently developed two-dimensional quantum processors with OBC.

关键词： variational quantum simulation quantum computation quantum approximate optimization algorithm

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Dynamic resource allocation scheme for mobile edge computing

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JOURNAL OF SUPERCOMPUTING 2023年第15期79卷 17187-17207页

作者： Gong, Changqing He, Wanying Wang, Ting Gani, Abdullah Qi, Han Shenyang Aerosp Univ Sch Comp Sci Shenyang 110000 Peoples R China Univ Malaysia Sabah Fac Comp & Informat Labuan Int Campus Labuan 87000 Malaysia

Mobile edge computing is a promising paradigm that provides edge users with dependable computing services. However, due to the dynamic nature of mobile users and the limited resources of edge servers, it is essential to emphasize the load balancing of edge servers and the cooperation of heterogeneous computing resources. This paper proposes a Dynamic Resource Allocation (DRA) scheme based on a quantum approximate optimization algorithm (QAOA). The DRA is composed of the two components listed below. Firstly, we apply generative adversarial network to predict the future user density in various regions, which is an effective resource allocation aid. Secondly, QAOA is utilized to pre-allocate edge servers resources based on an advanced model of user density. The simulation results demonstrate that the efficient application of DRA ensures the load balancing of edge servers and simultaneously alleviates communication latency.

关键词： quantum approximate optimization algorithm Generative adversarial network Mobile edge computing Dynamic resource allocation

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Multiple Query optimization Using a Gate-Based quantum Computer

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IEEE ACCESS 2023年 11卷 114031-114043页

作者： Fankhauser, Tobias Soler, Marc E. Fuchslin, Rudolf Marcel Stockinger, Kurt Univ Zurich Dept Informat CH-8050 Zurich Switzerland Univ St Gallen Dept Informat CH-9000 St Gallen Switzerland ZHAW Zurich Univ Appl Sci Dept Informat CH-8050 Winterthur Switzerland European Ctr Living Technol I-30123 Venice Italy

quantum computing promises to solve difficult optimization problems in chemistry, physics and mathematics more efficiently than classical computers. However, it requires fault-tolerant quantum computers with millions of qubits;a technological challenge still not mastered by engineers. To lower the barrier, hybrid algorithms combining classical and quantum computers are used, where quantum computing is only used for those parts of computation that cannot be solved efficiently otherwise. In this paper, we tackle the multiple query optimization problem (MQO), an important NP-hard problem in database research. We present an implementation based on a scheme called quantum approximate optimization algorithm to solve the MQO on a gate-based quantum computer. We perform a detailed experimental evaluation of our implementation and compare its performance against a competing approach that employs a quantum annealer - another type of quantum computer. Our implementation shows a qubit efficiency of close to 99%, which is almost a factor of 2 higher than the state-of-the-art implementation. We emphasize that the problems we can solve with current gate-based quantum technology are fairly small and might not seem practical yet compared to state-of-the-art classical query optimizers. However, our experiments on using a hybrid approach of classical and quantum computing show that our implementation scales favourably with larger problem sizes. Hence, we conclude that our approach shows promising results for near-term quantum computers and thus sets the stage for a challenging avenue of novel database research.

关键词： optimization Databases databases multiple query optimization quantum approximate optimization algorithm experimental evaluation

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