Quantum Annealing Intensity Schedule Optimization
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Solution Overview
Problem
The efficiency of quantum annealing on the LHZ model is difficult to improve, as existing methods do not effectively apply classical quantum hybrid algorithms to this architecture, which is based on many-body interactions and local fields.
Innovation Solution
An arithmetic apparatus and method that adjust the intensity schedule function in quantum annealing using a hybrid algorithm, where the value of the intensity schedule function at one or multiple time points serves as a variational parameter to optimize the energy expectation value, thereby improving the efficiency of quantum annealing on the LHZ model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum annealing is performed for a long time to obtain optimal solutions, then solution accuracy improves, but noise and decoherence increase making ideal quantum annealing difficult to execute
Solution Approach 1:
The patent applies dynamics by making the intensity schedule function adjustable and adaptable during quantum annealing. The hybrid algorithm dynamically modifies the intensity schedule function based on feedback from quantum annealing results, allowing the system to optimize the balance between annealing time and solution quality rather than using a fixed schedule.
Solution Approach 2:
The patent implements feedback through the hybrid algorithm that uses classical computing to analyze quantum annealing results and adjust the intensity schedule function accordingly. The classical computer receives measurement results from the quantum computer and modifies the intensity schedule function to improve subsequent quantum annealing executions, creating a closed-loop optimization system.
2Productivity
If the intensity schedule function is adjusted to improve quantum annealing efficiency, then productivity improves, but the complexity of controlling the annealing process increases
Solution Approach 1:
The patent applies segmentation by dividing the quantum annealing control into two distinct components: the quantum computer that executes the annealing process and the classical computer that optimizes the intensity schedule function. This segmentation allows each component to focus on its strength while reducing the overall system complexity.
Solution Approach 2:
The patent uses the classical computer as an intermediary that mediates between the desired quantum annealing outcomes and the actual execution parameters. The classical computer translates optimization goals into adjusted intensity schedule functions that are then applied to the quantum computer, simplifying the control interface.
3Adaptability or versatility
If existing classical quantum hybrid algorithms are applied to LHZ model quantum annealing, then adaptability improves, but the methods are not specifically optimized for many-body interactions in LHZ model
Solution Approach 1:
The patent applies local quality by tailoring the hybrid algorithm specifically for the LHZ model's many-body interactions. Rather than using a generic hybrid algorithm, the invention customizes the optimization approach to account for the specific characteristics of LHZ model constraint terms and their many-body interaction structure.
Solution Approach 2:
The patent implements parameter changes by modifying the intensity schedule function parameters based on the specific requirements of LHZ model quantum annealing. The hybrid algorithm adjusts parameters such as the timing and magnitude of constraint term intensities to optimize performance for many-body interactions specific to the LHZ architecture.
Data Source
AI summary
The efficiency of quantum annealing on an LHZ model is improved. An arithmetic apparatus includes an arithmetic unit configured to adjust an intensity schedule function in quantum annealing of a constraint term expressed by many-body interactions in an LHZ model by a hybrid algorithm that uses a value of the intensity schedule function at one time point or each of a plurality of time points as a variational parameter.