QAOA Parameter Setting Using Ising Solutions for Faster Optimization
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Solution Overview
Problem
Existing methods for solving combinatorial optimization problems, such as quantum approximation optimization algorithms (QAOA), face inefficiencies in finding optimal parameters due to non-convex relationships between energy and circuit parameters, leading to increased solution time, especially when initial values are far from the optimal solution.
Innovation Solution
An information processing method that utilizes an Ising model and quantum circuit to calculate a first solution, determines parameter values to maximize the probability of achieving this solution, and performs repeated sampling to refine the solution, using a quantum processing unit (QPU) to efficiently converge on a second solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum approximation optimization algorithm is used to solve combinatorial optimization problem, then solution can be obtained, but solution time increases when initial values are far from optimal solution due to non-convex relationship between energy and circuit parameters
Solution Approach 1:
The patent applies preliminary action by using simulated annealing to obtain an initial solution before executing the quantum approximation optimization algorithm. This preliminary solution serves as a starting point that is already close to the optimal solution, thereby reducing the solution time required by the QAOA when initial values are far from optimal. The process flow shows: obtaining combinatorial optimization problem → executing simulated annealing to obtain initial solution → executing QAOA with this initial solution → outputting final solution.
2Reliability
If parameter values of quantum circuit are not appropriately set, then quantum state may not converge to optimal solution, but adjusting parameters increases complexity of optimization process
Solution Approach 1:
The patent applies feedback by using the initial solution from simulated annealing as input to the quantum approximation optimization algorithm. The QAOA then adjusts its circuit parameters based on this initial solution, with the objective of maximizing the probability that the quantum state becomes this initial solution. This feedback mechanism allows the system to automatically adjust parameters without requiring manual intervention, reducing parameter adjustment complexity while improving the probability of achieving the optimal solution.
Data Source
AI summary
A computer-readable recording medium stores therein an information processing program for causing a computer to execute a process, the process including: calculating a first solution of a combinatorial optimization problem based on an Ising model corresponding to the combinatorial optimization problem; determining a value of a parameter of a quantum approximation optimization algorithm corresponding to the combinatorial optimization problem so as to maximize a probability that a quantum state of a quantum circuit of the quantum approximation optimization algorithm becomes the calculated first solution; and calculating a second solution of the combinatorial optimization problem based on the quantum circuit of the quantum approximation optimization algorithm in which the determined value of the parameter is set.