Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions, making QML more viable for real-world applications. Our method significantly improves parameter optimization for VQC while delivering notable gains in representation and generalization capabilities, as evidenced by rigorous theoretical analysis and extensive empirical testing on quantum dot classification tasks. Moreover, our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach. By addressing the constraints of current quantum hardware, our work paves the way for a new era of advanced QML applications, unlocking the full potential of quantum computing in fields such as machine learning, materials science, medicine, mimetics, and various interdisciplinary areas.
Code (0)
등록된 구현이 없습니다.
Tasks
Quantum Machine LearningSimilar Papers 제목 키워드 기반
Machine Learning Phase Transitions with a Quantum Processor
Machine learning has emerged as a promising approach to study the properties of many-body systems. Recently proposed as a tool to classify phases of matter, the approach relies on classical simulation methods$-$such as M…
BIG-bench Machine LearningQuantum Machine LearningTensor NetworksDevanagari Digit Recognition using Quantum Machine Learning
Handwritten digit recognition in regional scripts, such as Devanagari, is crucial for multilingual document digitization, educational tools, and the preservation of cultural heritage. The script's complex structure and l…
Handwritten Digit RecognitionQuantum Machine LearningQuantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting
We present the Quantum Kernel-Based Long short-memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate ti…
Computational EfficiencyTime SeriesTime Series ForecastingQUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution
Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the …
Image EnhancementImage RestorationImage Super-ResolutionQuantum Machine Learning+2Variational Quantum Circuits Enhanced Generative Adversarial Network
Generative adversarial network (GAN) is one of the widely-adopted machine-learning frameworks for a wide range of applications such as generating high-quality images, video, and audio contents. However, training a GAN co…
BenchmarkingGenerative Adversarial NetworkImage Generation