Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems

Abstract

Quantum or quantum-inspired Ising machines have recently shown promise in solving combinatorial optimization problems in a short time. Real-world applications, such as time division multiple access (TDMA) scheduling for wireless multi-hop networks and financial trading, require solving those problems sequentially where the size and characteristics change dynamically. However, using Ising machines involves challenges to shorten system-wide latency due to the transfer of large Ising model or the cloud access and to determine the parameters for each problem. Here we show a combinatorial optimization method using embedded Ising machines, which enables solving diverse problems at high speed without runtime parameter tuning. We customize the algorithm and circuit architecture of the simulated bifurcation-based Ising machine to compress the Ising model and accelerate computation and then built a machine learning model to estimate appropriate parameters using extensive training data. In TDMA scheduling for wireless multi-hop networks, our demonstration has shown that the sophisticated system can adapt to changes in the problem and showed that it has a speed advantage over conventional methods.

References in corpus (14)

• XGBoost: A Scalable Tree Boosting System
• Emergence of scaling in random networks
• Ising formulations of many NP problems
• Quantum critical dynamics in a 5000-qubit programmable spin glass
• Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network: Toward quantum soft computing
• Massively Parallel Probabilistic Computing with Sparse Ising Machines
• Roadmap for Unconventional Computing with Nanotechnology
• Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity
• Simulated bifurcation for higher-order cost functions
• Real-time Trading System based on Selections of Potentially Profitable, Uncorrelated, and Balanced Stocks by NP-hard Combinatorial Optimization
• Correlation-diversified portfolio construction by finding maximum independent set in large-scale market graph
• Efficient and Scalable Architecture for Multiple-chip Implementation of Simulated Bifurcation Machines
• Pairs-trading System using Quantum-inspired Combinatorial Optimization Accelerator for Optimal Path Search in Market Graphs
• Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines