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quantum optimization Methods for Satellite Mission Planning

IEEE ACCESS 2024年 12卷 71808-71820页

作者： Makarov, Anton Perez-Herradon, Carlos Franceschetto, Giacomo Taddei, Marcio M. Osaba, Eneko del Barrio Cabello, Paloma Villar-Rodriguez, Esther Oregi, Izaskun GMV Tres Cantos 28760 Madrid Spain IFF CSIC Inst Fundamental Phys Madrid 28006 Spain Barcelona Inst Sci & Technol Inst Ciencies Foton ICFO Castelldefels 08860 Barcelona Spain Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Spain European Univ Gasteiz EUNEIZ Dept New Interact Technol Vitoria 01013 Spain

Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to constraints, to be fulfilled during an orbit pass of a satellite. The ever-growing amount of satellites in orbit underscores the need to operate them efficiently, which requires solving many instances of the problem in short periods of time. However, current classical algorithms often fail to find the global optimum or take too long to execute. Here, we approach the problem from a quantum computing point of view, which offers a promising alternative that could lead to significant improvements in solution quality or execution speed in the future. To this end, we study a planning problem with a variety of intricate constraints and discuss methods to encode them for quantum computers. Additionally, we experimentally assess the performance of quantum annealing and the quantum approximate optimization algorithm on a realistic and diverse dataset. Our results identify key aspects like graph connectivity and constraint structure that influence the performance of the methods. We explore the limits of today's quantum algorithms and hardware, providing bounds on the problems that can be currently solved successfully and showing how the solution degrades as the complexity grows. This work aims to serve as a baseline for further research in the field and establish realistic expectations on current quantum optimization capabilities.

关键词： Satellites Planning optimization methods Cameras quantum annealing Approximation algorithms Annealing Combinatorial optimization earth observation quantum annealing quantum approximate optimization algorithm quantum computing satellite mission planning

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Novel Resolution of Unit Commitment Problems Through quantum Surrogate Lagrangian Relaxation

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IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第3期38卷 2460-2471页

作者： Feng, Fei Zhang, Peng Bragin, Mikhail A. Zhou, Yifan SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

Unit commitment (UC) problems faced by Independent System Operators on a daily basis are becoming increasingly complex due to the recent push for renewables and the consideration of sub-hourly UC to accommodate the increasing variability in the net load. A disruptive solution methodology to address the growing complexity is therefore required. quantum computing offers a promise to overcome the combinatorial complexity through the use of the so-called "qubits." To make the best use of near-term quantum computers to solve UC problems with a much larger number of binary variables than the number of qubits available, this paper devises a novel solution methodology based on a synergistic combination of quantum computing and Surrogate Lagrangian Relaxation (SLR) to solve UC problems. Our new contributions include: 1) A quantum-SLR (QSLR) algorithm incorporating quantum approximate optimization algorithm (QAOA) into the SLR method, which overcomes the fundamental difficulties of previous LR-based quantum methods such as zigzagging of multipliers and the need to know or estimate the optimal dual value for convergence;2) A Distributed QSLR framework (D-QSLR) capable of coordinating local quantum/classical computing resources with those within neighborhoods and, in the meantime, protecting data privacy;3) A Quantized UC model to obtain accurate commitment unit subproblems decision by using a quantum machine;and 4) A time-unit-decomposed quantum UC approach to overcoming the quantum resources' limitations. Promising quantum test results validate the effectiveness of QSLR and the scalability of the UC-oriented D-QSLR algorithm, which demonstrate QSLR's enormous potential in UC optimization.

关键词： quantum computing optimization Qubit Complexity theory quantum entanglement Computers Approximation algorithms Distributed quantum optimization quantum approximate optimization algorithm quantum computing surrogate lagrangian relaxation unit commitment

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The quantum approximate algorithm for Solving Traveling Salesman Problem

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Computers, Materials & Continua 2020年第6期63卷 1237-1247页

作者： Yue Ruan Samuel Marsh Xilin Xue Zhihao Liu Jingbo Wang School of Computer Science and Technology Anhui University of TechnologyMaanshanChina School of Physics University of Western AustraliaPerthAustralia School of Computer Science and Engineering Southeast UniversityNanjingChina

The quantum approximate optimization algorithm(QAOA)is an algorithmic framework for finding approximate solutions to combinatorial optimization *** consists of interleaved unitary transformations induced by two operators labelled the mixing and problem *** fit this framework,one needs to transform the original problem into a suitable form and embed it into these two *** this paper,for the well-known NP-hard Traveling Salesman Problem(TSP),we encode its constraints into the mixing Hamiltonian rather than the conventional approach of adding penalty terms to the problem ***,we map edges(routes)connecting each pair of cities to qubits,which decreases the search space significantly in comparison to other *** a result,our method can achieve a higher probability for the shortest round-trip route with only half the number of qubits consumed compared to IBM Q’s *** argue the formalization approach presented in this paper would lead to a generalized framework for finding,in the context of QAOA,high-quality approximate solutions to NP optimization problems.

关键词： quantum approximate optimization algorithm traveling salesman problem NP optimization problems

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Max-flow min-cut theorem in quantum computing

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2024年 649卷

作者： Singh, Nongmeikapam Brajabidhu Roy, Arnab Saha, Anish Kumar Natl Inst Technol Silchar Dept Comp Sci & Engn Silchar 788010 India

The max-flow min-cut theorem in graph theory states that the maximum flow of a network is equal to the minimum cut of edges of the flow network. Max-flow and min-cut are related as two primal-dual linear programs. The theorem is applicable in applications like network connectivity, graph matching problems, transportation and logistics, and scheduling problems. The aim of the paper is to model the classical max-flow and min-cut theorem in quantum computing. Two well-known methods for quantum optimization are quantum annealing and quantum approximate optimization algorithm. The max-flow min-cut is converted to its equivalent model of the above two methods for execution in polynomial time. This paper shows detailed classical to quantum conversion, analysis, implementation, and result of the theorem.

关键词： Quadratic unconstrained binary optimization Ising model quantum annealing quantum approximate optimization algorithm

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K-spin Hamiltonian for quantum-resolvable Markov decision processes

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quantum MACHINE INTELLIGENCE 2020年第2期2卷 1-11页

作者： Jones, Eric B. Graf, Peter Kapit, Eliot Jones, Wesley Natl Renewable Energy Lab Golden CO 80401 USA Colorado Sch Mines Dept Phys Golden CO 80401 USA

The Markov decision process is the mathematical formalization underlying the modern field of reinforcement learning when transition and reward functions are unknown. We derive a pseudo-Boolean cost function that is equivalent to a K-spin Hamiltonian representation of the discrete, finite, discounted Markov decision process with infinite horizon. This K-spin Hamiltonian furnishes a starting point from which to solve for an optimal policy using heuristic quantum algorithms such as adiabatic quantum annealing and the quantum approximate optimization algorithm on near-term quantum hardware. In arguing that the variational minimization of our Hamiltonian is approximately equivalent to the Bellman optimality condition for a prevalent class of environments we establish an interesting analogy with classical field theory. Along with proof-of-concept calculations to corroborate our formulation by simulated and quantum annealing against classical Q-Learning, we analyze the scaling of physical resources required to solve our Hamiltonian on quantum hardware.

关键词： K-spin hamiltonian Markov decision process quantum optimization quantum annealing Simulated annealing quantum approximate optimization algorithm

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optimization Applications as quantum Performance Benchmarks

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ACM TRANSACTIONS ON quantum COMPUTING 2024年第3期5卷 1-44页

作者： Lubinski, Thomas Coffrin, Carleton McGeoch, Catherine Sathe, Pratik Apanavici, Joshua Neira, David Bernal Quantum Circuits Inc New Haven CT 06520 USA Los Alamos Natl Lab Adv Network Sci Initiat Los Alamos CA USA D Wave Syst Inc Burnaby BC Canada Univ Calif Los Angeles Dept Phys & Astron Los Angeles CA USA Los Alamos Natl Lab Theoret Div T 4 Los Alamos CA USA Univ Space Res Assoc Res Inst Adv Comp Sci Mountain View CA USA Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA NASA Quantum Artificial Intelligence Lab Ames Res Ctr Mountain View CA USA Purdue Univ Syst W Lafayette IN USA

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The quantum approximate optimization algorithm and quantum Annealing can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-cut problems using gate-model quantum devices and a quantum annealing device. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through compelling visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. As an illustration, we explore the factors that influence quantum computing system throughput, using results obtained through execution on various quantum simulators and quantum hardware systems.

关键词： quantum Computing Benchmarks Benchmarking algorithms Applica- tion Benchmarks QAOA quantum approximate optimization algorithm Max-cut

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DC Optimal Power Flow in Unit Commitment using quantum Computing: An ADMM Approach 56

DC Optimal Power Flow in Unit Commitment using Quantum Compu...

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56th North American Power Symposium

作者： Magar, Madan Rana Pandit, Dilip Nguyen, Duong Nguyen, Nga Univ Wyoming Elect Engn & Comp Sci Laramie WY 82071 USA

ISBN: (纸本)9798331521042;9798331521035

This paper introduces an approach to solve unit commitment-based DC optimal power flow using the quantum-based alternating direction method of multipliers optimizer. The substantial interconnection of current generation and loads and the widespread incorporation of renewable energy have significantly altered and increased the complexity of the grid. As a result, efficient planning and operation of the power grid have become central focuses. This paper proposes an advanced method to solve DC-optimal power flow via quantum heuristic approaches leveraging the power of both quantum and classical computing. This is achieved by splitting the procedure to decompose a mixed binary quadratic problem into a quadratic unconstrained binary optimization sub-problem which is solved using a quantum approximate optimization algorithm. Following this, a continuous convex-constrained sub-problem is solved with a classical optimization solver. Within the DC-OPF, the startup cost and shutdown cost of each generator are incorporated in addition to the general cost function with the generator's cost coefficients. The DC-OPF is solved using the IBM Qiskit software development kit on an IEEE-14 bus system. The results obtained are then validated with the results obtained from classical methods using MATPOWER.

关键词： Alternating direction method of multipliers mixed binary optimization quantum approximate optimization algorithm quantum computing

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Benchmarking Metaheuristic-Integrated QAOA Against quantum Annealing

Benchmarking Metaheuristic-Integrated QAOA Against Quantum A...

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Computing Conference

作者： Mazumder, Arul Rhik Sen, Anuvab Sen, Udayon Carnegie Mellon Univ Pittsburgh PA 15289 USA Indian Inst Engn Sci & Technol Sibpur 711103 India

ISBN: (纸本)9783031622717;9783031622694;9783031622687

The quantum approximate optimization algorithm (QAOA) is one of the most promising Noisy Intermediate quantum (NISQ) algorithms in solving combinatorial optimizations and displays potential over classical heuristic techniques. Unfortunately, QAOA's performance depends on the choice of parameters and standard optimizers often fail to identify key parameters due to the complexity and mystery of these optimization functions. In this paper, we benchmark QAOA circuits modified with metaheuristic optimizers against classical and quantum heuristics to identify QAOA parameters. The experimental results reveal insights into the strengths and limitations of both quantum Annealing and metaheuristic-integrated QAOA across different problem domains. The findings suggest that the hybrid approach can leverage classical optimization strategies to enhance the solution quality and convergence speed of QAOA, particularly for problems with rugged landscapes and limited quantum resources. Furthermore, the study provides guidelines for selecting the most appropriate approach based on the specific characteristics of the optimization problem at hand.

关键词： Quadratic unconstrained binary optimization quantum approximate optimization algorithm quantum annealing Metaheuristics

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Exploiting In-Constraint Energy in Constrained Variational quantum optimization 3

Exploiting In-Constraint Energy in Constrained Variational Q...

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IEEE/ACM 3rd International Workshop on quantum Computing Software (QCS)

作者： Hao, Tianyi Shaydulin, Ruslan Pistoia, Marco Larson, Jeffrey Argonne Natl Lab Lemont IL 60439 USA Univ Wisconsin Madison Madison WI 53706 USA JPMorgan Chase New York NY 10017 USA

ISBN: (纸本)9781665475365

A central challenge of applying near-term quantum optimization algorithms to industrially relevant problems is the need to incorporate complex constraints. In general, such constraints cannot be easily encoded in the circuit, and the quantum circuit measurement outcomes are not guaranteed to respect the constraints. Therefore, the optimization must trade off the in-constraint probability and the quality of the in-constraint solution by adding a penalty for constraint violation into the objective. We propose a new approach for solving constrained optimization problems with unconstrained, easy-to-implement quantum ansatze. Our method leverages the in-constraint energy as the objective and adds a lower-bound constraint on the in-constraint probability to the optimizer. We demonstrate significant gains in solution quality over directly optimizing the penalized energy. We implement our method in QVoice, a Python package that interfaces with Qiskit for quick prototyping in simulators and on quantum hardware.

关键词： quantum approximate optimization algorithm variational quantum eigensolver constrained combinatorial optimization

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Joint Transmission and Transcoding in Computing Power Networks for Livecast: A quantum-inspired optimization Approach 25

Joint Transmission and Transcoding in Computing Power Networ...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Zou, Xin Xie, Renchao Tang, Qinqin Huang, Tao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

ISBN: (纸本)9798350303582;9798350303599

With the continuous development of network technology and hardware devices, panoramic livecast is promising and widely used in various industries. To meet massive heterogeneous viewer demands, livecast video streams must be transcoded into multiple versions and then transmitted to viewers. By offloading transcoding workloads to network nodes closer to broadcasters and viewers, computing power networks (CPN) have been considered an effective means to provide viewers with a higher quality of experience (QoE). In this paper, we study the joint video transmission and transcoding resource allocation in the CPN-based panoramic livecast system. Considering the versatility and simplicity, we offer a multi-layer network model to capture the stochastic characteristics of transmission and transcoding processes and transform the joint resource allocation problem into a broader shortest path problem (SPP). As the scale of networks expands, the SPP may become intractable on classical computers. This paper explores the viability of solving the SPP on quantum computers by utilizing quantum resources such as superposition and entanglement, then proposes a shortest path algorithm based on the quantum approximate optimization algorithm (QAOA-SPP) for jointly optimizing the latency and overhead of transmission and transcoding. Simulation results indicate that our algorithm can achieve better performance than the benchmark schemes under reasonable parameter settings.

关键词： Computing power network joint video transmission and transcoding resource allocation quantum approximate optimization algorithm

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