20240020568. VARIATIONAL QUANTUM OPTIMIZATION simplified abstract (MULTIVERSE COMPUTING S.L.)

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VARIATIONAL QUANTUM OPTIMIZATION

Organization Name

MULTIVERSE COMPUTING S.L.

Inventor(s)

Roman Oscar Orus Lacort of Donostia (ES)

Pablo Bermejo Navas of Donostia (ES)

VARIATIONAL QUANTUM OPTIMIZATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240020568 titled 'VARIATIONAL QUANTUM OPTIMIZATION

Simplified Explanation

The abstract of the patent application describes a method for solving a variational quantum optimization problem using a hybrid quantum-classical computing system. The system consists of a classical digital computer and a quantum computer. The method involves minimizing a cost function that is composed of n variables, each of which can have up to p different values. The quantum computer is designed with as many qubits as there are variables in the cost function, with each qubit having p maximally orthogonal states. A quantum circuit is configured to perform various operations on the qubits, where each qubit represents a variable of the cost function and each maximally orthogonal state represents a possible value for that variable.

  • The patent application describes a method for solving variational quantum optimization problems.
  • The method involves using a hybrid quantum-classical computing system.
  • The system includes a classical digital computer and a quantum computer.
  • The cost function to be minimized consists of n variables, each with up to p different values.
  • The quantum computer has as many qubits as there are variables in the cost function.
  • Each qubit has p maximally orthogonal states.
  • A quantum circuit is configured to perform operations on the qubits.
  • Each qubit represents a variable of the cost function.
  • Each maximally orthogonal state represents a possible value for the corresponding variable.

Potential Applications:

  • Optimization problems in various fields such as finance, logistics, and chemistry.
  • Machine learning and artificial intelligence algorithms.
  • Cryptography and secure communication systems.
  • Simulation of complex physical systems.

Problems Solved:

  • Efficiently solving variational quantum optimization problems.
  • Handling large-scale optimization problems with a large number of variables.
  • Finding optimal solutions for complex problems that cannot be easily solved using classical computing methods.

Benefits:

  • Potential for faster and more efficient optimization compared to classical computing methods.
  • Ability to handle larger and more complex optimization problems.
  • Potential for breakthroughs in various fields such as finance, logistics, and chemistry.
  • Advancement in quantum computing technology and its applications.


Original Abstract Submitted

method for solving a variational quantum optimization problem in a hybrid quantum-classical computing system that includes a classical digital computer and a quantum computer by minimizing a cost function. the cost function comprises n variables, and each of the variables can take up to p different values. the quantum computer includes as many qubits as variables in the cost function, each qubit having p maximally orthogonal states, and a quantum circuit configured for performing a plurality of operations on said qubits. each qubit represents each of the variables of the cost function, and each maximally orthogonal state of a qubit represents each of the values that the corresponding variable can have.

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