International Business Machines Corporation (20240265289). GLOBAL QUANTUM OPTIMIZATION ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS IN NISQ DEVICES simplified abstract

GLOBAL QUANTUM OPTIMIZATION ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS IN NISQ DEVICES

Organization Name

International Business Machines Corporation

Inventor(s)

Mitsuharu Takeori of Kashiwa (JP)

GLOBAL QUANTUM OPTIMIZATION ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS IN NISQ DEVICES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240265289 titled 'GLOBAL QUANTUM OPTIMIZATION ALGORITHM FOR COMBINATORIAL OPTIMIZATION PROBLEMS IN NISQ DEVICES

Simplified Explanation: The patent application describes a method, system, and computer program product that utilize quantum optimization algorithms for solving combinatorial optimization problems on noisy intermediate-scale quantum devices. The algorithms aim to minimize an objective function representing the problem, with circuit parameters adjusted using the Gauss-Newton based quantum algorithm (GNQA) to optimize the solution. Bayesian optimization is employed for global optimization if the initial solution is incorrect, updating the circuit parameters until a correct solution is reached.

• Objective function defined for combinatorial optimization problems
• Circuit parameters adjusted using Gauss-Newton based quantum algorithm (GNQA)
• Bayesian optimization used for global optimization if initial solution is incorrect
• Quantum algorithms employed on noisy intermediate-scale quantum devices
• Output indicates error-robust indicator value for solution legitimacy

Key Features and Innovation:

• Utilization of quantum optimization algorithms for combinatorial optimization problems
• Integration of Gauss-Newton based quantum algorithm (GNQA) for local optimization
• Implementation of Bayesian optimization for global optimization
• Error-robust indicator value for solution validation
• Application on noisy intermediate-scale quantum devices

Potential Applications: The technology can be applied in various industries such as logistics, finance, telecommunications, and healthcare for solving complex optimization problems efficiently.

Problems Solved: The technology addresses the challenges of solving combinatorial optimization problems on noisy intermediate-scale quantum devices by employing quantum optimization algorithms.

Benefits:

• Efficient solution to combinatorial optimization problems
• Improved accuracy in optimization results
• Utilization of quantum algorithms for complex problem-solving
• Potential for advancements in quantum computing technology

Commercial Applications: The technology has commercial potential in industries requiring optimization solutions, such as supply chain management, financial portfolio optimization, network routing, and drug discovery.

Prior Art: Readers can explore prior research on quantum optimization algorithms, combinatorial optimization problems, and quantum computing technologies to understand the background of this innovation.

Frequently Updated Research: Stay updated on the latest advancements in quantum optimization algorithms, combinatorial optimization techniques, and quantum computing developments to enhance understanding and application of this technology.

Questions about Quantum Optimization Algorithms: 1. How do quantum optimization algorithms differ from classical optimization algorithms? 2. What are the key challenges in implementing quantum optimization algorithms on noisy intermediate-scale quantum devices?

Original Abstract Submitted

a method, system and computer program product for employing quantum optimization algorithms for combinatorial optimization problems in noisy intermediate-scale quantum (nisq) devices. an objective function of a combinatorial optimization problem to be minimized is defined. the input to the objective function corresponds to the circuit parameters for the ansatz of the gauss-newton based quantum algorithm (gnqa). the output of the objective function corresponds to the error-robust indicator value indicating whether the result of gnqa (solution of the combinatorial optimization problem) is a legitimate or illegitimate return value. after initializing the circuit parameters (�) of the objective function, gnqa is employed for local optimization. furthermore, a bayesian optimization is employed for global optimization in response to the solution of the combinatorial optimization problem not reaching a correct solution, where the bayesian optimization updates the circuit parameters to minimize the indicator value. once a correct solution is reached, it is outputted.