18087592. MEMORY-SAVING OPTIMIZATION OF QUADRATIC FORMS simplified abstract (Fujitsu Limited)
Contents
MEMORY-SAVING OPTIMIZATION OF QUADRATIC FORMS
Organization Name
Inventor(s)
Sarvagya Upadhyay of San Jose CA (US)
MEMORY-SAVING OPTIMIZATION OF QUADRATIC FORMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18087592 titled 'MEMORY-SAVING OPTIMIZATION OF QUADRATIC FORMS
Simplified Explanation: The patent application describes a method involving obtaining two copies of a quantum state representing a convex optimization problem, amplifying and measuring an amplitude associated with one copy, and determining a final quantum mixing state based on the measured amplitude to solve the optimization problem.
Key Features and Innovation:
- Obtaining two copies of a quantum state representing a convex optimization problem
- Amplifying and measuring an amplitude associated with one copy
- Determining a final quantum mixing state based on the measured amplitude
- Solving the convex optimization problem using the final quantum mixing state
Potential Applications: This technology could be applied in quantum computing, optimization algorithms, and mathematical problem-solving.
Problems Solved: This technology addresses the challenge of solving convex optimization problems efficiently using quantum states.
Benefits:
- Improved efficiency in solving convex optimization problems
- Potential for faster computation and problem-solving in quantum computing
- Enhanced accuracy in determining final quantum mixing states
Commercial Applications: Potential commercial applications include quantum computing software development, optimization software tools, and quantum algorithm research.
Prior Art: Readers can explore prior research in quantum optimization algorithms, quantum state manipulation, and convex optimization in quantum computing.
Frequently Updated Research: Stay updated on the latest advancements in quantum computing, optimization algorithms, and quantum state manipulation for potential improvements in this technology.
Questions about Quantum State Optimization: 1. How does this method improve upon traditional optimization algorithms? 2. What are the implications of using quantum states for solving convex optimization problems?
Original Abstract Submitted
A method may include obtaining a first and a second copy of a quantum state in which the first and second copies of the quantum state represent a convex optimization problem. The first and second copies of the quantum state may include respective index quantum registers that each hold indices and respective mixing state quantum registers that each hold quantum mixing states. The method may include amplifying and measuring an amplitude of the index quantum register associated with the first copy of the quantum state in which the measured amplified amplitude corresponds to a particular index. The method may include determining a final quantum mixing state corresponding to the mixing state quantum register of the second copy of the quantum state based on the measured amplified amplitude and the particular index. The method may include determining a solution to the convex optimization problem based on the final quantum mixing state.
