A New QUBO Objective Function for Solving the Maximum Common Subgraph Isomorphism Problem Via Quantum Annealing

Huang, Nan; Roje, Dominik

doi:10.1007/s42979-020-00431-5

SN COMPUT. SCI.

2021

DOI: 10.1007/s42979-020-00431-5

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A New QUBO Objective Function for Solving the Maximum Common Subgraph Isomorphism Problem Via Quantum Annealing

Nan Huang

Dominik Roje

Abstract: We present QUBO objective functions for the maximum common subgraph isomorphism problem, proved their correctness and illustrate them with a few toy problems. Very small-scale problems can be solved by the D-Wave 2X machine with high enough success probabilities; however, for larger scale problems, the success probability drops significantly.

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QUBO Models for the FIFO Stack-Up Problem and Experimental Evaluation on a Quantum Annealer

Gebler,

Rethmann,

Ueberholz

2024

SN COMPUT. SCI.

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Quantum annealing has been applied to combinatorial optimization problems in recent years. In this paper we study the possibility to use quantum annealing for solving the combinatorial FIFO Stack-Up problem, where bins have to be stacked-up from a conveyor belt onto pallets. The problem is NP-hard and can be solved using linear programming approaches. We developed two QUBO (quadratic unconstrained binary optimization) objective functions based on a bin stack-up solution and a pallet stack-up solution for this problem suitable for a quantum annealer. The number of variables was minimized to increase the performance and their dependence on the number of bins and pallets was discussed. The performances of both methods were studied for various small problem sizes on a D-Wave quantum annealer. We found that only tiny instances could be solved and looked at the terms of the QUBO-formulations, which cause the quantum annealer to fail for larger problem sizes. Furthermore we compare the results to the performance of a classic computer using the same QUBO-formulations.

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QUBO Models for the FIFO Stack-Up Problem and Experimental Evaluation on a Quantum Annealer

Gebler,

Rethmann,

Ueberholz

2024

SN COMPUT. SCI.

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Implementation of Quantum Annealing: A Systematic Review

Yulianti

Surendro

2022

IEEE Access

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Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.

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A New QUBO Objective Function for Solving the Maximum Common Subgraph Isomorphism Problem Via Quantum Annealing

Cited by 2 publications

References 23 publications

QUBO Models for the FIFO Stack-Up Problem and Experimental Evaluation on a Quantum Annealer

QUBO Models for the FIFO Stack-Up Problem and Experimental Evaluation on a Quantum Annealer

Implementation of Quantum Annealing: A Systematic Review

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