A naukri.com initiative
Arxiv
4d
302
Image Credit: Arxiv
VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning
- VQC-MLPNet is a new hybrid quantum-classical architecture designed to address limitations of Variational Quantum Circuits (VQCs) for quantum machine learning.
- It aims to overcome challenges like linear expressivity constraints, optimization difficulties, and sensitivity to quantum hardware noise.
- VQC-MLPNet utilizes quantum circuits to generate parameters for classical Multi-Layer Perceptrons (MLPs) dynamically, expanding representation capabilities and enhancing training stability.
- The architecture combines amplitude encoding and parameterized quantum operations to improve performance.
- Theoretical guarantees are provided through statistical learning techniques and Neural Tangent Kernel analysis, giving upper bounds on approximation, deviation, and optimization errors.
- The research shows exponential improvements in representation capacity compared to quantum circuit depth and qubit numbers, offering computational advantages over standalone quantum circuits and existing hybrids.
- Extensive experiments include tasks like classifying semiconductor quantum-dot charge states and predicting genomic transcription factor binding sites, demonstrating robust performance even under realistic IBM quantum noise simulations.
- The study establishes a theoretically sound and practically robust framework, advancing quantum-enhanced learning for unconventional computing paradigms in the Noisy Intermediate-Scale Quantum era and beyond.
Read Full Article
18 Likes
For uninterrupted reading, download the app