Adiabatic Evolution Path Search Using MCTS and Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing adiabatic quantum computing methods face inefficiencies and instability in finding optimal evolution paths due to numerous candidate actions, leading to poor convergence and prolonged computation times.
Innovation Solution
Convert the search for an adiabatic evolution path into a chessboard game and solve it using Monte Carlo Tree Search (MCTS) combined with a neural network to predict an optimal path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Q-learning is used to find optimal adiabatic evolution path, then the method can handle complex quantum computing problems, but efficiency is poor and convergence is unstable when there are many candidate actions
Solution Approach 1:
The patent segments the continuous adiabatic evolution path search into discrete time steps and states, transforming it into a structured dynamic programming problem. This segmentation allows the system to handle complex quantum problems by breaking them into manageable sub-problems that can be solved systematically, improving both efficiency and convergence stability.
Solution Approach 2:
The patent pre-calculates and stores the optimal evolution path using dynamic programming before actual quantum computation. By performing the path optimization in advance and storing the results in a lookup table, the system eliminates the need for real-time Q-learning during quantum evolution, significantly improving computational efficiency and convergence reliability.
2Adaptability or versatility
If Q-learning is used to find optimal adiabatic evolution path, then the method can adapt to different quantum problems, but convergence is unstable when there are many candidate actions
Solution Approach 1:
By segmenting the evolution path into discrete states and transitions, the patent creates a structured framework that maintains convergence stability. The dynamic programming approach systematically evaluates each segment, ensuring reliable convergence even when adapting to different quantum computing problems with varying numbers of candidate actions.
Solution Approach 2:
The patent changes the problem representation from continuous Q-learning parameters to discrete dynamic programming states and transitions. This parameter transformation stabilizes convergence by eliminating the stochastic nature of Q-learning while maintaining adaptability through the flexible state-transition framework that can accommodate different quantum problems.
3Ease of manufacture
If traditional methods are used to search for adiabatic evolution path, then the approach is simple to implement, but computation time is prolonged
Solution Approach 1:
The patent performs the computationally intensive path optimization in advance using dynamic programming and stores the results for rapid retrieval. This preliminary action eliminates repeated computation during actual quantum evolution, dramatically reducing computation time while maintaining implementation simplicity through the use of pre-computed lookup tables.
Solution Approach 2:
The patent creates a computational model (dynamic programming framework) that copies and simulates the quantum evolution process to pre-determine the optimal path. This copying approach allows the system to solve the optimization problem once in classical computation and reuse the solution, avoiding prolonged computation time during actual quantum execution.
Data Source
AI summary
This application is directed to predicting an adiabatic evolution path and relates to the field of artificial intelligence (AI). In one aspect, a computer device obtains a target Hamiltonian H1 of a quantum computing problem. The computer device also determines an initial Hamiltonian H0 and a target ground state energy E1 according to the target Hamiltonian H1. The device converts a search for an adiabatic evolution path from the initial Hamiltonian H0 to the target ground state energy E1 into a chessboard game. The device also solves an optimal chessboard path of the chessboard game using a Monte Carlo tree search (MCTS) in combination with a neural network. The device also outputs the adiabatic evolution path according to the optimal chessboard path.