Ising Machine–QPU QAOA Tuning for Combinatorial Optimization
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Solution Overview
Problem
Existing techniques for solving combinatorial optimization problems, such as quantum approximate optimization algorithms (QAOA), face inefficiencies due to non-convex relationships between energy and parameters, leading to increased time to locate optimal solutions.
Innovation Solution
An information processing method utilizing an Ising machine and quantum processing unit to determine parameter values that minimize energy and maximize the probability of matching a calculated solution, facilitating efficient calculation of combinatorial optimization problems through iterative parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum approximate optimization algorithm is used to solve combinatorial optimization problems, then solution accuracy is improved, but calculation time increases due to non-convex relationships between energy and parameters
Solution Approach 1:
The invention applies preliminary action by using an Ising machine to calculate a preliminary solution before the quantum approximate optimization algorithm. This preliminary solution serves as an initial reference point that guides the subsequent quantum optimization process, reducing the search space and accelerating convergence to the optimal solution.
Solution Approach 2:
The invention implements feedback by iteratively adjusting quantum circuit parameters based on the energy values obtained from quantum state measurements. The calculated energy feedback into the optimization loop enables continuous refinement of parameters, allowing the system to converge to optimal solutions more efficiently despite non-convex energy landscapes.
2Measurement precision
If parameter values are adjusted to minimize energy in quantum circuit, then solution quality is improved, but number of iterations increases
Solution Approach 1:
The Ising machine provides preliminary parameter suggestions based on classical optimization, which serve as starting points for the quantum optimization. This preliminary action reduces the number of iterations needed in the quantum approximate optimization algorithm to achieve high solution quality.
Solution Approach 2:
The invention employs parameter changes by dynamically adjusting quantum circuit parameters based on energy minimization feedback. The system transforms parameter values iteratively, changing them from initial random values to optimized values that minimize energy and maximize solution quality.
3Power
If quantum processing is used for combinatorial optimization, then computational power is enhanced, but system complexity increases
Solution Approach 1:
The invention applies segmentation by dividing the optimization system into distinct functional modules: an Ising machine for preliminary classical optimization, a quantum processing unit for quantum approximate optimization, and a control unit for coordinating between them. This modular segmentation manages system complexity while preserving computational power enhancements.
Solution Approach 2:
The control unit serves as an intermediary that coordinates between the Ising machine and quantum processing unit. It manages data flow, parameter transmission, and result integration, thereby simplifying the interaction complexity between the two powerful computational components.
Data Source
AI summary
A recording medium storing a program for causing a computer coupled to an Ising machine and a quantum processing unit (QPU), to execute: determining, for a quantum circuit of a quantum approximate optimization algorithm (QAOA) corresponding to the combinatorial optimization problem (COP), a first value of a parameter of the QAOA such that energy corresponding to a quantum state of the quantum circuit is locally minimized; calculating a first solution to the COP, by using the Ising machine having an Ising model corresponding to the COP; determining a second value of the parameter from the first value such that a probability that the quantum state of the quantum circuit matches the first solution is maximized; and calculating a second solution to the COP, by causing the QPU to calculate the second solution based on the quantum circuit in which the second value of the parameter is set.