ILP-based Resource Optimization Realized by Quantum Annealing for Optical Wide-area Communication Networks -- A Framework for Solving Combinatorial Problems of a Real-world Application by Quantum Annealing

• URL: http://arxiv.org/abs/2401.00826v2
• Date: Mon, 20 May 2024 13:43:48 GMT
• Title: ILP-based Resource Optimization Realized by Quantum Annealing for Optical Wide-area Communication Networks -- A Framework for Solving Combinatorial Problems of a Real-world Application by Quantum Annealing
• Authors: Arthur Witt, Jangho Kim, Christopher Körber, Thomas Luu,
• Abstract summary: In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave AdvantageTM quantum annealer system. Here we report on our investigations for optimizing system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave.
• Score: 5.924780594614675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Resource allocation of wide-area internet networks is inherently a combinatorial optimization problem that if solved quickly, could provide near real-time adaptive control of internet-protocol traffic ensuring increased network efficacy and robustness, while minimizing energy requirements coming from power-hungry transceivers. In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave AdvantageTM quantum annealer system, demonstrating proof of principle. Our initial studies left open the possibility for improvement of D-Wave solutions via judicious choices of system run parameters. Here we report on our investigations for optimizing these system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. In particular, we use the Hamming distance to investigate correlations between various system-run parameters and solution vectors. We then apply a decision tree neural network (NN) to learn these correlations, with the goal of using the neural network to provide further guesses to solution vectors. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave. We find, however, for the 3-node network problem the NN is not able to enhance the quality of space of solutions.

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