Polynomial-time solution of prime factorization and NP-hard problems with digital memcomputing machines

TL;DR

This paper introduces Digital Memcomputing Machines (DMMs) and self-organizing logic circuits (SOLCs) that can solve NP-hard problems like prime factorization efficiently using polynomial resources, bridging physics, dynamical systems, and computation.

Contribution

The paper presents a novel class of digital machines, DMMs, with a practical implementation via SOLCs capable of solving NP-hard problems in polynomial time, supported by rigorous mathematical analysis.

Findings

DMMs can solve NP-hard problems with polynomial resources.

SOLCs possess a global attractor and exponential convergence to solutions.

The approach is robust, scalable, and applicable to problems like prime factorization.

Abstract

We introduce a class of digital machines we name Digital Memcomputing Machines (DMMs) able to solve a wide range of problems including Non-deterministic Polynomial (NP) ones with polynomial resources (in time, space and energy). An abstract DMM with this power must satisfy a set of compatible mathematical constraints underlying its practical realization. We initially prove this by introducing the complexity classes for these machines. We then make a connection with dynamical systems theory. This leads to the set of physical constraints for poly-resource resolvability. Once the mathematical requirements have been assessed, we propose a practical scheme to solve the above class of problems based on the novel concept of self-organizing logic gates and circuits (SOLCs). These are logic gates and circuits able to accept input signals from any terminal, without distinction between conventional input and output terminals. They can solve boolean problems by self-organizing into their solution. They can be fabricated either with circuit elements with memory (such as memristors) and/or standard MOS technology. Using tools of functional analysis, we prove mathematically the following constraints for the poly-resource resolvability: i) SOLCs possess a global attractor; ii) their only equilibrium points are the solutions of the problems to solve; iii) the system converges exponentially fast to the solutions; iv) the equilibrium convergence rate scales at most polynomially with input size. We finally provide arguments that periodic orbits and strange attractors cannot coexist with equilibria. As examples we show how to solve the prime factorization and the NP-hard version of the subset-sum problem. Since DMMs map integers into integers they are robust against noise, and hence scalable. We finally discuss the implications of the DMM realization through SOLCs to the NP=P question related to...