20240046138. CONSTRUCTING AND PROGRAMMING DRIVER GRAPHS IN QUANTUM HARDWARE FOR NON-STOQUASTIC QUANTUM OPTIMIZATION ANNEALING PROCESSES simplified abstract (GLADIOLUS VERITATIS CONSULTING COMPANY)

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CONSTRUCTING AND PROGRAMMING DRIVER GRAPHS IN QUANTUM HARDWARE FOR NON-STOQUASTIC QUANTUM OPTIMIZATION ANNEALING PROCESSES

Organization Name

GLADIOLUS VERITATIS CONSULTING COMPANY

Inventor(s)

Vicky Choi of Mission (CA)

CONSTRUCTING AND PROGRAMMING DRIVER GRAPHS IN QUANTUM HARDWARE FOR NON-STOQUASTIC QUANTUM OPTIMIZATION ANNEALING PROCESSES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240046138 titled 'CONSTRUCTING AND PROGRAMMING DRIVER GRAPHS IN QUANTUM HARDWARE FOR NON-STOQUASTIC QUANTUM OPTIMIZATION ANNEALING PROCESSES

Simplified Explanation

The abstract of this patent application describes a computer-implemented method for solving optimization problems using quantum annealing. The method involves converting the optimization problem into a maximum weight independent set (MWIS) problem on a problem graph. The MWIS problem is then encoded into an energy spectrum of a problem Hamiltonian. An XX-driver graph is determined based on identified independent cliques, and XX-coupler strengths associated with the XX-driver graph are determined. A non-stoquastic driver Hamiltonian is specified based on the XX-driver graph and the associated XX-coupler strengths. The quantum annealing hardware is initialized and programmed with a time-dependent non-stoquastic system Hamiltonian using the determined values of parameters from the problem Hamiltonian and the non-stoquastic driver Hamiltonian. The hardware implements a quantum annealing schedule and provides an output representing eigen-states of the system Hamiltonian and corresponding energy values.

  • The patent application describes a computer-implemented method for solving optimization problems using quantum annealing.
  • The method involves converting the optimization problem into a maximum weight independent set (MWIS) problem on a problem graph.
  • The MWIS problem is encoded into an energy spectrum of a problem Hamiltonian.
  • An XX-driver graph is determined based on identified independent cliques.
  • XX-coupler strengths associated with the XX-driver graph are determined.
  • A non-stoquastic driver Hamiltonian is specified based on the XX-driver graph and the associated XX-coupler strengths.
  • The quantum annealing hardware is initialized and programmed with a time-dependent non-stoquastic system Hamiltonian using the determined values of parameters.
  • The hardware implements a quantum annealing schedule.
  • The hardware provides an output representing eigen-states of the system Hamiltonian and corresponding energy values.

Potential Applications:

  • Optimization problems in various fields such as logistics, finance, and manufacturing can be solved using this method.
  • This technology can be applied to machine learning and data analysis tasks that involve optimization.

Problems Solved:

  • This technology provides a method for efficiently solving complex optimization problems.
  • Quantum annealing allows for exploring a large solution space and finding optimal solutions.

Benefits:

  • Quantum annealing can potentially provide faster and more efficient solutions compared to classical optimization algorithms.
  • The method described in the patent application allows for encoding optimization problems into quantum systems, taking advantage of their inherent parallelism and quantum effects.


Original Abstract Submitted

a computer implemented method includes receiving, by a control system, an optimization problem expressed as a mwis on a problem graph. then encoding the mwis problem into an energy spectrum of a problem hamiltonian. also, determining an xx-driver graph based on an identified independent-cliques (ic), where the edges in the xx-driver graph are the edges between partites in each clique of the ic. also, determining xx-coupler strengths associated with the xx-driver graph, and specifying a non-stoquastic driver hamiltonian based on the xx-driver graph and the associated xx-coupler strengths. also, initializing and programming a time-dependent non-stoquastic system hamiltonian of the quantum annealing hardware using determined values of parameters included in the problem hamiltonian and determined values of parameters included in the non-stoquastic driver hamiltonian. furthermore, implementing, by the hardware, a quantum annealing schedule, and receiving an output representing eigen-states of the system hamiltonian and corresponding energy values.

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