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Found 10 papers, 0 papers with code
Projective Embedding of Dynamical Systems: uniform mean field equations
We study embeddings of continuous dynamical systems in larger dimensions via projector operators.
Global minimization via classical tunneling assisted by collective force field formation
Simple dynamical models can produce intricate behaviors in large networks.
Memcomputing: Leveraging memory and physics to compute efficiently
In this perspective we discuss how to employ one such property, memory (time non-locality), in a novel physics-based approach to computation: Memcomputing.
Accelerating Deep Learning with Memcomputing
In fact, the acceleration of pretraining achieved by simulating DMMs is comparable to, in number of iterations, the recently reported hardware application of the quantum annealing method on the same network and data set.
On the Universality of Memcomputing Machines
We show that UMMs can simulate both types of machines, hence they are both "liquid-" or "reservoir-complete" and "quantum-complete".
Evidence of an exponential speed-up in the solution of hard optimization problems
However, despite numerous research efforts, in many cases even approximations to the optimal solution are hard to find, as the computational time for further refining a candidate solution grows exponentially with input size.
Memcomputing Numerical Inversion with Self-Organizing Logic Gates
We propose to use Digital Memcomputing Machines (DMMs), implemented with self-organizing logic gates (SOLGs), to solve the problem of numerical inversion.
Memcomputing NP-complete problems in polynomial time using polynomial resources and collective states
Even though the particular machine presented here is eventually limited by noise--and will thus require error-correcting codes to scale to an arbitrary number of memprocessors--it represents the first proof-of-concept of a machine capable of working with the collective state of interacting memory cells, unlike the present-day single-state machines built using the von Neumann architecture.
Memcomputing with membrane memcapacitive systems
We show theoretically that networks of membrane memcapacitive systems -- capacitors with memory made out of membrane materials -- can be used to perform a complete set of logic gates in a massively parallel way by simply changing the external input amplitudes, but not the topology of the network.
Universal Memcomputing Machines
We introduce the notion of universal memcomputing machines (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the same physical location.
