Solving the Shipment Rerouting Problem with Quantum Optimization Techniques

Yarkoni, Sheir; Huck, Andreas; Schülldorf, Hanno; Speitkamp, Benjamin; Tabrizi, Marc Shakory; Leib, Martin; Bäck, Thomas; Neukart, Florian

doi:10.1007/978-3-030-87672-2_33

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…Therefore, it is important to consider setting λ k such that the lowest possible value that maintains correctness of the constraints is used. While there is no universal way to set these parameters, simple rules-of-thumb may be found either algebraically or empirically [133,194]. In the case of purely constraint-based optimization problems such as constraint satisfaction problem (CSP) or Boolean satisfiability (SAT) problems, no λ is needed when summing independent constraints, since the minimum of each constraint, as well as the global optimum is at a cost function value of zero, C(x solution ) = 0.…”

Section: Constrained Optimization Problemsmentioning

confidence: 99%

“…Additionally, the number of qubits needed to encode such continuous variables needs to be rather large. For the very few practical applications that utilize such an encoding, the precision requirements are the most detrimental factor in QPU performance [145,194]. This means that to solve high-precision problems necessarily means that (in the worst case) the precision level is below the noise threshold for QPUs.…”

Section: Continuous Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Quantum annealing for industry applications: introduction and review

et al. 2022

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Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the quantum annealing algorithm for programmable use. Specifically, quantum annealing processors produced by D-Wave Systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for quantum annealing as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of quantum annealing technology. We identify the advantages, limitations, and potential of quantum annealing for both researchers and practitioners from various fields.

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Section: Constrained Optimization Problemsmentioning

confidence: 99%

Section: Continuous Variablesmentioning

confidence: 99%

Quantum annealing for industry applications: introduction and review

et al. 2022

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“…Quantum annealing is a commonly employed method of solving combinatorial optimization problems using quantum hardware and use cases for vehicle routing problems have been explored. [20,21,[105][106][107][108][109][110] Other commonly used methods of finding approximate solutions involve hybrid quantum-classical approaches such as the Quantum Approximate Optimization Algorithm (QAOA), [111][112][113][114] and Quantum Assisted Solvers. [115,116] For a more comprehensive review of near term quantum algorithms and their applications to the VRP, we refer our readers to ref.…”

Section: Quantum Algorithms For Qubomentioning

confidence: 99%

Qubit Efficient Quantum Algorithms for the Vehicle Routing Problem on Noisy Intermediate‐Scale Quantum Processors

Leonidas,

Dukakis,

Tan

et al. 2024

Adv Quantum Tech

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The vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry. In this work, the use of a previously‐introduced qubit encoding scheme is explored to reduce the number of qubits, to evaluate the effectiveness of Noisy Intermediate‐Scale Quantum (NISQ) devices when applied to industry relevant optimization problems. A quantum variational approach is applied to a testbed of multiple VRPTW instances ranging from 11 to 3964 routes. These intances are formulated as quadratic unconstrained binary optimization (QUBO) problems based on realistic shipping scenarios. The results are compared with standard binary‐to‐qubit mappings after executing on simulators as well as various quantum hardware platforms, including IBMQ, AWS (Rigetti), and IonQ. These results are benchmarked against the classical solver, Gurobi. The approach can find approximate solutions to the VRPTW comparable to those obtained from quantum algorithms using the full encoding, despite the reduction in qubits required. These results suggest that using the encoding scheme to fit larger problem sizes into fewer qubits is a promising step in using NISQ devices to find approximate solutions for industry‐based optimization problems, although additional resources are still required to eke out the performance from larger problem sizes.

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“…As for the railway industry, to the best of our knowledge, a preliminary version of the present work [15] was the first to apply a quantum computing approach to a problem in railway optimization. As the citations to our e-print illustrate [16,17], this research direction is attracting increasing interest.…”

Section: Introductionmentioning

confidence: 99%

Quantum Annealing in the NISQ Era: Railway Conflict Management

Domino

Koniorczyk

Krawiec

et al. 2023

Entropy

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We are in the noisy intermediate-scale quantum (NISQ) devices’ era, in which quantum hardware has become available for application in real-world problems. However, demonstrations of the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the train dispatching consequences of the arrival of an already delayed train to a given network segment. This problem is computationally hard and needs to be solved almost in real time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, which is compatible with the emerging quantum annealing technology. The model’s instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including the conventional solution of a linear integer version of the model as well as the solution of the QUBO model using a tensor network-based algorithm. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) does not perform well on those instances, either.

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Solving the Shipment Rerouting Problem with Quantum Optimization Techniques

Cited by 6 publications

References 27 publications

Quantum annealing for industry applications: introduction and review

Quantum annealing for industry applications: introduction and review

Qubit Efficient Quantum Algorithms for the Vehicle Routing Problem on Noisy Intermediate‐Scale Quantum Processors

Quantum Annealing in the NISQ Era: Railway Conflict Management

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