Phase-Space Engineering and Collective Dynamics in Memcomputing
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arXiv
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| Format: | Preprint |
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2025
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| author | Sipling, Chesson Zhang, Yuan-Hang Di Ventra, Massimiliano |
| author_facet | Sipling, Chesson Zhang, Yuan-Hang Di Ventra, Massimiliano |
| contents | Digital Memcomputing machines (DMMs) are dynamical systems with memory (time non-locality) that have been designed to solve combinatorial optimization problems. Their corresponding ordinary differential equations depend on a few hyper-parameters that define both the system's relevant time scales and its phase-space geometry. Using numerical simulations on a prototypical DMM, we analyze the role of these physical parameters in engineering the phase space to either help or hinder the solution search by DMMs. We find that the DMM explores its phase space efficiently for a wide range of parameters, aided by the system-wide correlations in their fast degrees of freedom that emerge dynamically due to coupling with the (slow) memory degrees of freedom. In this regime, the time it takes for the system to find a solution scales well as the number of variables increases. When these hyper-parameters are chosen poorly, the system navigates its phase space far less efficiently. However, we find that, in many cases, collective behavior persists even when the phase-space exploration process is inefficient. This behavior only disappears if the memories are made to evolve as quickly as the fast degrees of freedom. This study points to the important role of memory and hyper-parameters in engineering the DMMs' phase space for optimal computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phase-Space Engineering and Collective Dynamics in Memcomputing Sipling, Chesson Zhang, Yuan-Hang Di Ventra, Massimiliano Computational Physics Adaptation and Self-Organizing Systems Digital Memcomputing machines (DMMs) are dynamical systems with memory (time non-locality) that have been designed to solve combinatorial optimization problems. Their corresponding ordinary differential equations depend on a few hyper-parameters that define both the system's relevant time scales and its phase-space geometry. Using numerical simulations on a prototypical DMM, we analyze the role of these physical parameters in engineering the phase space to either help or hinder the solution search by DMMs. We find that the DMM explores its phase space efficiently for a wide range of parameters, aided by the system-wide correlations in their fast degrees of freedom that emerge dynamically due to coupling with the (slow) memory degrees of freedom. In this regime, the time it takes for the system to find a solution scales well as the number of variables increases. When these hyper-parameters are chosen poorly, the system navigates its phase space far less efficiently. However, we find that, in many cases, collective behavior persists even when the phase-space exploration process is inefficient. This behavior only disappears if the memories are made to evolve as quickly as the fast degrees of freedom. This study points to the important role of memory and hyper-parameters in engineering the DMMs' phase space for optimal computational efficiency. |
| title | Phase-Space Engineering and Collective Dynamics in Memcomputing |
| topic | Computational Physics Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2506.10149 |