Sequential Quantum Enhanced Training

Sequential Quantum Enhanced Training (SQUENT) explores hybrid classical-quantum machine learning approaches where classical and quantum components are trained sequentially, rather than concurrently, to improve traceability and efficiency. Research focuses on developing efficient quantum circuits and algorithms, including quantum generative models for time series data and quantum recurrent neural networks, often leveraging techniques like variance regularization to mitigate noise and improve training speed. This approach aims to harness the potential of quantum computing for enhanced machine learning performance while addressing limitations of current quantum hardware by strategically integrating classical components, ultimately leading to more efficient and interpretable models for various applications.