Hybrid Quantum-Classical Image Classification via Segmented Variational Circuits
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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 tasks like image classification.
Innovation Solution
A hybrid quantum-classical computation system that employs a convolutional block to process image features, followed by a flattening layer and multiple independent variational quantum circuits to generate classifications, where each circuit processes subsets of features and combines outputs for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum computers use more qubits and deeper circuits to improve classification accuracy, then classification performance improves, but device complexity and hardware requirements worsen
Solution Approach 1:
The patent divides the feature processing into multiple independent variational quantum circuits, each handling a subset of features from the flattened feature vector. This segmentation allows the system to achieve high classification accuracy through parallel processing of feature subsets rather than requiring a single large complex circuit processing all features simultaneously, thus improving accuracy while controlling device complexity
Solution Approach 2:
The patent transforms the classification problem from a single high-dimensional quantum circuit into multiple lower-dimensional parallel circuits. By processing feature subsets in parallel and combining results classically, the system achieves comparable or superior accuracy to classical classifiers while using fewer qubits and shallower circuits than a monolithic quantum approach would require
2Adaptability or versatility
If NISQ devices are used for variational quantum circuits, then quantum advantage can be achieved with current technology, but the number of qubits and circuit depth are limited
Solution Approach 1:
The patent applies partial action by having multiple independent variational quantum circuits each process only a subset of the complete feature vector. This allows the system to work within the limited qubit capacity of NISQ devices while still achieving comprehensive classification by combining results from multiple partial processing operations, thus adapting current quantum device limitations into a functional architecture
Data Source
AI summary
A hybrid quantum-classical computation system for classifying a grid of features provided as an input, comprising a convolutional block comprising a filter configured to receive the grid of features and to output a plurality of output features for the grid of features based on a trainable configuration of the convolutional filter; a flattening layer for transforming the filtered grid of output features received from the convolutional block into a flattened feature vector; a classifying block configured to receive the flattened feature vector and generate an output classification, wherein the classifying block comprises a plurality of independent variational quantum circuits; wherein the variational quantum circuits of the plurality of independent variational quantum circuits receive different subsets of features from the flattened feature vector as an input feature vector; and wherein measured outputs of the plurality of independent variational quantum circuits are combined to determine a label as the output classification.