Quantum Annealing in the NISQ Era: Railway Conflict Management

Domino, Krzysztof; Koniorczyk, Mátyás; Krawiec, Krzysztof; Jałowiecki, Konrad; Deffner, Sebastian; Gardas, Bartłomiej

doi:10.3390/e25020191

Cited by 9 publications

(3 citation statements)

References 89 publications

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“…For many problems the QUBO or Ising formulation is known [38,39]. Outside financial applications, QUBO-based quantum annealing use cases can be found in logistics [40][41][42], smart city planning [43], telecommunications [44], healthcare [45,46], and many more.…”

Section: Quantum Annealing and Qubo Formulationmentioning

confidence: 99%

Multi-Objective Portfolio Optimization Using a Quantum Annealer

Aguilera,

de Jong,

Phillipson

et al. 2024

Mathematics

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In this study, the portfolio optimization problem is explored, using a combination of classical and quantum computing techniques. The portfolio optimization problem with specific objectives or constraints is often a quadratic optimization problem, due to the quadratic nature of, for example, risk measures. Quantum computing is a promising solution for quadratic optimization problems, as it can leverage quantum annealing and quantum approximate optimization algorithms, which are expected to tackle these problems more efficiently. Quantum computing takes advantage of quantum phenomena like superposition and entanglement. In this paper, a specific problem is introduced, where a portfolio of loans need to be optimized for 2030, considering `Return on Capital’ and `Concentration Risk’ objectives, as well as a carbon footprint constraint. This paper introduces the formulation of the problem and how it can be optimized using quantum computing, using a reformulation of the problem as a quadratic unconstrained binary optimization (QUBO) problem. Two QUBO formulations are presented, each addressing different aspects of the problem. The QUBO formulation succeeded in finding solutions that met the emission constraint, although classical simulated annealing still outperformed quantum annealing in solving this QUBO, in terms of solutions close to the Pareto frontier. Overall, this paper provides insights into how quantum computing can address complex optimization problems in the financial sector. It also highlights the potential of quantum computing for providing more efficient and robust solutions for portfolio management.

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Section: Quantum Annealing and Qubo Formulationmentioning

confidence: 99%

Multi-Objective Portfolio Optimization Using a Quantum Annealer

Aguilera,

de Jong,

Phillipson

et al. 2024

Mathematics

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“…The QUBO model can be applied to a wide range of combinatorial optimization problems that are known to be NPhard, such as the maximum cut, minimum vertex cover, multiple knapsack, and graph coloring problems. Its applications span a diverse set of domains, including the automotive industry [74], portfolio optimization [75]- [77], traffic flow optimization [77], job scheduling [78]- [80], railway conflict management [81], bioinformatics [82], and others [83]. An extensive list of QUBO formulations for interesting problems can be found in [84].…”

Section: A Quadratic Unconstrained Binary Optimizationmentioning

confidence: 99%

Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing (May 2023)

Giron,

Korpas,

Parvaiz

et al. 2023

IEEE Trans. Quantum Eng.

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Collateral optimization refers to the systematic allocation of financial assets to satisfy obligations or secure transactions, while simultaneously minimizing costs and optimizing the usage of available resources. This involves assessing number of characteristics, such as cost of funding and quality of the underlying assets to ascertain the optimal collateral quantity to be posted to cover exposure arising from a given transaction or a set of transactions. One of the common objectives is to minimise the cost of collateral required to mitigate the risk associated with a particular transaction or a portfolio of transactions while ensuring sufficient protection for the involved parties. Often, this results in a large-scale combinatorial optimization problem. In this study, we initially present a Mixed Integer Linear Programming (MILP) formulation for the collateral optimization problem, followed by a Quadratic Unconstrained Binary optimization (QUBO) formulation in order to pave the way towards approaching the problem in a hybridquantum and NISQ-ready way. We conduct local computational small-scale tests using various Software Development Kits (SDKs) and discuss the behavior of our formulations as well as the potential for performance enhancements. We find that while the QUBO based approaches fail to find the global optima in the small scale experiments, they are reasonably close suggesting their potential for large instances. We further survey the recent literature that proposes alternative ways to attack combinatorial optimization problems suitable for collateral optimization.

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“…For certain tasks, quantum computers exploit the fundamental principles of quantum mechanics to perform complex calculations exponentially faster than classical computers 2 – 4 . The tremendous computational power offered by quantum systems has fueled excitement and exploration in various scientific, industrial, and financial sectors 1 , 5 – 12 . Consequently, there have been significant developments in the pursuit of quantum advantage that have propelled quantum computing from theoretical speculation to practical implementation 13 – 23 .…”

Section: Introductionmentioning

confidence: 99%

Efficiency optimization in quantum computing: balancing thermodynamics and computational performance

Śmierzchalski,

Mzaouali,

Deffner

et al. 2024

Sci Rep

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We investigate the computational efficiency and thermodynamic cost of the D-Wave quantum annealer under reverse-annealing with and without pausing. Our demonstration on the D-Wave 2000Q annealer shows that the combination of reverse-annealing and pausing leads to improved computational efficiency while minimizing the thermodynamic cost compared to reverse-annealing alone. Moreover, we find that the magnetic field has a positive impact on the performance of the quantum annealer during reverse-annealing but becomes detrimental when pausing is involved. Our results, which are reproducible, provide strategies for optimizing the performance and energy consumption of quantum annealing systems employing reverse-annealing protocols.

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Quantum Annealing in the NISQ Era: Railway Conflict Management

Cited by 9 publications

References 89 publications

Multi-Objective Portfolio Optimization Using a Quantum Annealer

Multi-Objective Portfolio Optimization Using a Quantum Annealer

Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing (May 2023)

Efficiency optimization in quantum computing: balancing thermodynamics and computational performance

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