Hybrid Quantum-Classical System for Labeling Function Approximation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current quantum computers, particularly noisy intermediate-scale quantum (NISQ) devices, are limited by the number of qubits and circuit depth, restricting their application in variational quantum circuits for practical implementations.
Innovation Solution
A hybrid quantum-classical computation system that combines a variational quantum circuit with a machine learning model, where the quantum circuit processes input feature vectors using parametrized quantum gates and an encoding gate, while the machine learning model processes the data using classical processing, with both systems jointly optimized to approximate labeling functions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If variational quantum circuits are used to process data, then quantum advantage can be achieved through entanglement and superposition, but the limited number of qubits and circuit depth in NISQ devices restricts their practical application
Solution Approach 1:
The patent divides the data processing task into two segments: a variational quantum circuit that processes the input feature vector and generates quantum outputs, and a classical machine learning model that processes the same input and generates classical outputs. This segmentation allows each component to operate within its capabilities, with the quantum circuit handling tasks benefiting from quantum effects and the classical model compensating for quantum device limitations.
Solution Approach 2:
The patent merges the variational quantum circuit and classical machine learning model into a hybrid architecture where both processes are trained jointly. The outputs of both the quantum circuit and classical model are combined through a labeling module to produce the final output, allowing the system to leverage both quantum and classical processing strengths while working around NISQ device constraints.
2Measurement precision
If the quantum circuit is trained independently, then quantum parameters can be optimized, but the system cannot effectively leverage classical processing strengths for complex pattern recognition
Solution Approach 1:
The patent combines the variational quantum circuit and classical machine learning model into a unified hybrid system where both are trained together using a common loss function. This merging allows the quantum and classical components to learn complementary patterns, with the quantum circuit capturing quantum-correlated features and the classical model handling complex non-linear relationships, thereby enhancing overall pattern recognition capability.
Solution Approach 2:
The labeling module serves as an intermediary that receives outputs from both the quantum circuit and classical model, combines them through trainable parameters, and produces the final prediction. This intermediary component enables the integration of quantum and classical processing results, allowing the system to leverage both computational paradigms effectively.
3Manufacturing precision
If more qubits and deeper circuits are used to improve approximation capability, then the system can handle more complex functions, but NISQ device limitations make such implementations infeasible
Solution Approach 1:
The patent segments the approximation task between quantum and classical processors, allowing the variational quantum circuit to provide quantum-enhanced feature extraction with limited qubits, while the classical machine learning model handles the heavy lifting of complex function approximation. This segmentation enables achieving high approximation capability without requiring large-scale quantum hardware.
Solution Approach 2:
The patent employs parameterized quantum gates with trainable parameters in the variational quantum circuit, allowing the system to adapt its behavior to match complex target functions. By optimizing these parameters during joint training with the classical model, the system achieves high approximation accuracy for complex functions while maintaining a shallow circuit depth suitable for NISQ devices.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for training a hybrid quantum-classical computation system for approximating a labeling function for an input feature vector, the system comprising a variational quantum circuit, a machine learning model, and a labeling module configured to receive a first output generated by the variational quantum circuit and a second output generated by the machine learning model and to generate an output label, wherein the method comprises an iterative process comprising the steps of providing an input feature vector of the sample dataset to the variational quantum circuit and to the machine learning model, providing the first output and the second output to the labeling module, and determining a parameter update of the variational parameters, the machine-learning parameters, and the trainable combination parameters based on a value of a cost function for the output label for the input feature vector.