Combinatorial optimization using quantum or classical driven systems

Yunpyo Hong, Jaejoon Gill, Daeyeun Kim +3 more

Junpeng Hou, Amin Barzegar, Helmut G. Katzgraber

Combinatorial optimization problems are ubiquitous in industrial applications. However, finding optimal or close-to-optimal solutions can often be extremely hard. Because some of these problems can be mapped to the ground-state search of the Ising model, tremendous effort has been devoted to developing solvers for Ising-type problems over the past decades. Recent advances in controlling and manipulating both quantum and classical systems have enabled novel computing paradigms such as quantum simulators and coherent Ising machines to tackle hard optimization problems. Here, we examine and benchmark several physics-inspired optimization algorithms, including coherent Ising machines, gain-dissipative algorithms, simulated bifurcation machines, and Hopfield neural networks, which we collectively refer to as stochastic driven nonlinear dynamical systems. Most importantly, we benchmark these algorithms against random Ising problems with planted solutions and compare them to simulated annealing as a baseline leveraging the same software stack for all solvers. We further study how different numerical integration techniques and graph connectivity affect performance. This work provides an overview of a diverse set of new optimization paradigms.

Alexandre Gondran, Michel Gondran

Nathan Kittichaikoonkij, Nutthapat Pongtanyavichai, Poopha Suwananek +2 more

Quantum computers can be much faster than any classical computers in solving a certain class of problems. One of the interesting problems is the combinatorial optimization problem, which is a challenge for classical computer systems. We encode these problems into the Quadratic Unconstrained Binary Optimization (QUBO) format and then solve them with quantum solvers. In this study, we compare the performance of D-Wave’s quantum annealing system with classical solvers, namely, Gurobi and Fixstars. We demonstrate the current capabilities of the D-Wave system.

Nicholas J. Guerrero

K Dhanalakshmi, Mohammad Kanan, P Hanumantha Rao +3 more

The need for efficient and scalable big data analytics frameworks is driven by the exponential growth of data in areas such as social media, the internet of things (IoT), finance, healthcare, and scientific simulations. While powerful, conventional HPC and distributed processing systems are illequipped to optimize and discover patterns in complicated, large datasets. The use of superposition and entanglement in quantum computing alters optimization and data processing. This research develops a hybrid quantum-classical paradigm for big data analytics by combining machine learning with quantum-assisted optimization. By integrating quantum algorithms such as variational optimization and quantum search with conventional distributed storage and preprocessing systems, we can enhance clustering, feature selection, and predictive modeling. Effective use of quantum-classical interfaces has been demonstrated for data encoding, parameter tuning, and iterative optimization. Processing is simplified, convergence is accelerated, and analytical accuracy for highdimensional datasets is enhanced by the suggested architecture. According to performance simulations, classical methods might not be as effective as combinatorial optimization and large-scale pattern recognition. According to this research, next-gen data-driven businesses can benefit greatly from quantum-enhanced analytics’ scalable, intelligent, and quick optimization mechanisms, which can completely transform big data processing.