JPMorgan Chase Bank, N.A. (20240330737). SYSTEMS AND METHODS FOR LOW-COST SIMULATION OF QUANTUM ALGORITHMS simplified abstract

SYSTEMS AND METHODS FOR LOW-COST SIMULATION OF QUANTUM ALGORITHMS

Organization Name

JPMorgan Chase Bank, N.A.

Inventor(s)

Ruslan Shaydulin of New York NY (US)

Yue Sun of Short Hills NJ (US)

Marco Pistoia of Amawalk NY (US)

SYSTEMS AND METHODS FOR LOW-COST SIMULATION OF QUANTUM ALGORITHMS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240330737 titled 'SYSTEMS AND METHODS FOR LOW-COST SIMULATION OF QUANTUM ALGORITHMS

Simplified Explanation: The patent application describes systems and methods for low-cost simulation of quantum algorithms using a quantum computer simulator program. The method involves receiving a problem description and objective, precomputing a diagonal vector, initializing a state vector, applying phase and mixing operators, computing the quality of the state vector, and updating circuit parameters based on the quality.

• Key Features and Innovation:

   - Utilizes a quantum computer simulator program for low-cost simulation of quantum algorithms.
   - Precomputes a diagonal vector comprising diagonal elements of a phase Hamiltonian.
   - Applies phase and mixing operators to a state vector based on circuit parameters.
   - Computes the quality of the state vector to evaluate the simulation.
   - Updates circuit parameters to improve the simulation quality.

• Potential Applications:

   - Quantum algorithm development and testing.
   - Quantum computing research and education.
   - Optimization problems in various industries.
   - Cryptography and secure communication protocols.
   - Quantum machine learning and artificial intelligence.

• Problems Solved:

   - Cost-effective simulation of quantum algorithms.
   - Efficient evaluation of quantum algorithm performance.
   - Facilitates research and development in quantum computing.
   - Provides a tool for testing and optimizing quantum algorithms.
   - Enhances understanding of quantum principles and applications.

• Benefits:

   - Reduces the cost of quantum algorithm simulation.
   - Improves the accuracy and efficiency of quantum algorithm evaluation.
   - Accelerates research and development in quantum computing.
   - Enables testing and optimization of quantum algorithms.
   - Enhances knowledge and skills in quantum computing.

• Commercial Applications:

   - Title: "Cost-Effective Quantum Algorithm Simulation Technology"
   - Potential commercial uses in quantum software development companies.
   - Market implications include increased demand for quantum simulation tools.
   - Offers competitive advantage to businesses in quantum computing industry.
   - Supports innovation and advancement in quantum technology market.

• Prior Art:

   - Prior research on quantum algorithm simulation methods.
   - Existing quantum computer simulators and software tools.
   - Academic studies on quantum algorithm evaluation techniques.
   - Patents related to quantum algorithm optimization and testing.
   - Industry publications on quantum computing applications and tools.

• Frequently Updated Research:

   - Latest advancements in quantum algorithm simulation techniques.
   - Research on improving the efficiency of quantum computer simulators.
   - Updates on quantum algorithm evaluation metrics and methodologies.
   - Studies on the impact of quantum simulation on various industries.
   - Emerging trends in quantum computing research and development.

Questions about Quantum Algorithm Simulation Technology: 1. How does the use of a quantum computer simulator program benefit the simulation of quantum algorithms? 2. What are the potential applications of low-cost quantum algorithm simulation technology in different industries?

Original Abstract Submitted

systems and methods for low-cost simulation of quantum algorithms are disclosed. a method may include a quantum computer simulator computer program: (1) receiving a compact description of a problem and an objective to evaluate, a first circuit parameter, and a second circuit parameter; (2) precomputing a diagonal vector comprising diagonal elements of a phase hamiltonian; (3) initializing a state vector; (4) applying a phase operator to the state vector with the first circuit parameter; (5) applying a mixing operator to the state vector with the second circuit parameter; (6) reading the state vector; (7) computing a quality of the state vector based on the objective; and (8) updating the first circuit parameter and the second circuit parameter based on the quality.