Semi-Stochastic Boolean-Neural Hybrids for Solving Hard Problems

Description:

Reference #: 01457

The University of South Carolina is offering licensing opportunities for Semi-Stochastic Boolean-Neural Hybrids for Solving Hard Problems

Background:

Currently, there exist several emerging approaches for solving hard computational problems. Quantum computation offers algorithms that in principle allow finding solution of some problems on quantum computers exponentially faster compared to the classical ones. A well-known example is the Shor’s factorization algorithm. Up to now, however, the Shor’s algorithm has been demonstrated experimentally only on a small number of qubits. To the best of our knowledge, the largest number factored by Shor’s algorithm to date is 21.

Invention Description:

This invention is related to a method of finding solutions to hard problems including factorization, subset sum, maximum satisfiability, and many other related and unrelated problems. For this purpose, we introduce a specialized electronic circuit that could solve the problems efficiently and fast. The method proposed here can be implemented in software or hardware.

Potential Applications:

This invention solves various difficult for traditional computers problems, including factorization, subset sum, maximum satisfiability, and many other related and unrelated problems.

Advantages and Benefits:

This invention provides a potentially more efficient way to solve various hard problems. It is expected that the solution presented in this disclose is more efficient compared to the existing approaches and easier to implement in hardware.

Patent Information:
For Information, Contact:
Technology Commercialization
University of South Carolina
technology@sc.edu
Inventors:
Yuriy Pershin
Keywords:
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