Hybrid Quantum Digital Processor Solving NP-Hard Problems
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Solution Overview
Problem
Current heuristic solvers for computationally complex NP-hard problems, such as those used in quantum computers, face challenges in finding optimal solutions efficiently due to dependence on parameter settings that vary greatly by problem type and require extensive user experimentation, and adiabatic quantum computation faces barriers in maintaining qubit coherence for practical implementation.
Innovation Solution
A method is developed to determine optimal parameters for solvers by comparing problem features with previously determined sets, using a parameter learning system to generate and refine parameters for solving NP-hard problems, and combining quantum computation with classical optimization algorithms to improve solution quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristic solvers are used to solve NP-hard problems, then solution quality can be improved, but parameter setting complexity increases and requires extensive user experimentation
Solution Approach 1:
The system automatically determines solver parameters by analyzing problem features and comparing them with previously solved problems, eliminating the need for manual parameter tuning by users. The parameter learning system self-adjusts based on problem characteristics and historical data.
Solution Approach 2:
The system uses feedback from previously solved problems to improve parameter selection for current problems. By comparing problem features with historical data and using machine learning models, the system continuously refines its parameter determination accuracy.
2Productivity
If adiabatic quantum computation is used, then computational efficiency for certain problems improves, but qubit coherence maintenance becomes difficult
Solution Approach 1:
The system segments the computational task by using quantum computers only for specific sub-problems where they provide advantage, while handling other computations classically. This reduces the coherence requirements for quantum parts while maintaining overall efficiency.
Solution Approach 2:
The system dynamically adjusts computational parameters based on problem characteristics, selecting quantum versus classical approaches based on problem size, structure, and available quantum resources, thereby optimizing the balance between efficiency and coherence requirements.
3Speed
If quantum computers are used to solve NP-hard problems, then solution speed improves, but adaptability to different problem types decreases due to fixed parameter settings
Solution Approach 1:
The system dynamically selects and adjusts solver parameters based on the specific characteristics of each problem input. By analyzing problem features in real-time and comparing them with historical data, the system adapts its approach to optimize performance for each unique problem type.
Solution Approach 2:
The system creates a universal parameter determination framework that can handle multiple problem types by learning from diverse historical problems. The machine learning model generalizes patterns across different NP-hard problems to provide adaptable parameter settings.
Data Source
AI summary
Quantum and digital processors are employed together to solve computational problems. The quantum processor may be configured with a problem via a problem Hamiltonian and operated to perform adiabatic quantum computation and/or quantum annealing on the problem Hamiltonian to return a first solution to the problem that is in the neighborhood of the global minimum of the problem Hamiltonian. The digital processor may then be used to refine the first solution to the problem by casting the first solution to the problem as a starting point for a classical optimization algorithm. The classical optimization algorithm may return a second solution to the problem that corresponds to a lower energy state in the neighborhood of the global minimum, such as a ground state of the problem Hamiltonian. The quantum processor may include a superconducting quantum processor implementing superconducting flux qubits.