arXiv:2606.03917v1 Announce Type: cross Abstract: As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems. However, many analog, time-continuous Ising machines rely on gradient-descent-like dynamics to find solutions, which can limit speed and robustness. We investigate whether momentum and Adam optimization can improve these systems. Since these optimizers are traditionally formulated in discrete time, we derive continuous-time versions suitable for analog, time-continuous Ising-machine dynamics. On Max-Cut benchma
Moore's Law is reaching its physical limits, driving research into alternative computing paradigms like Ising machines to maintain computational progress.
Improving the performance of Ising machines could unlock new capabilities for solving difficult optimization problems, impacting fields from drug discovery to logistics.
The ability to integrate advanced optimization techniques like Adam into analog Ising machines could significantly enhance their speed and robustness compared to traditional gradient descent methods.
- · High-performance computing (HPC) research institutions
- · Analog chip manufacturers
- · Materials science
- · Logistics and supply chain optimization
- · Traditional digital computing architectures (long-term)
- · Companies reliant solely on classical optimization methods
Analog Ising machines become more practical and efficient for specific optimization tasks.
Increased investment and development in diverse non-von Neumann computing architectures accelerates.
New classes of AI and scientific algorithms emerge that are uniquely suited to these specialized computing paradigms.
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Read at arXiv cs.LG