The paper introduces the Quantum-Train (QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address challenges in data encoding, model compression, and inference hardware requirements. QT employs a quantum neural network (QNN) alongside a classical mapping model to significantly reduce the parameter count from \(M\) to \(O(\text{polylog}(M))\) during training, achieving remarkable results with slightly reduced accuracy. The framework is effective in classification tasks, demonstrating its potential to revolutionize machine learning by leveraging quantum computational advantages. It improves model efficiency, reduces generalization errors, and shows promise across various machine learning applications. Key contributions of the QT framework include: 1. **Addressing Data Encoding Issues**: QT eliminates the challenges of data encoding faced by pure QML by using classical data inputs and outputs, avoiding the complexities and potential information loss associated with encoding larger datasets into quantum states. 2. **Model Compression During Training**: The method reduces the number of parameters needed to train classical neural networks from \(M\) to \(O(\text{polylog}(M))\), achieving efficiency with \(N = \lceil \log_2 M \rceil\) qubits and a polynomially scaled number of QNN layers. 3. **Hardware Requirements for Inference**: The trained model operates seamlessly on classical hardware, bypassing the need for quantum computing resources, enhancing its practicality. The paper also discusses the efficiency and generalization performance of QT, showing that it achieves competitive accuracy with fewer parameters while maintaining or slightly reducing predictive accuracy. The framework's versatility is highlighted, suggesting its potential applications in various machine learning scenarios, including quantum reinforcement learning and generative models.The paper introduces the Quantum-Train (QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address challenges in data encoding, model compression, and inference hardware requirements. QT employs a quantum neural network (QNN) alongside a classical mapping model to significantly reduce the parameter count from \(M\) to \(O(\text{polylog}(M))\) during training, achieving remarkable results with slightly reduced accuracy. The framework is effective in classification tasks, demonstrating its potential to revolutionize machine learning by leveraging quantum computational advantages. It improves model efficiency, reduces generalization errors, and shows promise across various machine learning applications. Key contributions of the QT framework include: 1. **Addressing Data Encoding Issues**: QT eliminates the challenges of data encoding faced by pure QML by using classical data inputs and outputs, avoiding the complexities and potential information loss associated with encoding larger datasets into quantum states. 2. **Model Compression During Training**: The method reduces the number of parameters needed to train classical neural networks from \(M\) to \(O(\text{polylog}(M))\), achieving efficiency with \(N = \lceil \log_2 M \rceil\) qubits and a polynomially scaled number of QNN layers. 3. **Hardware Requirements for Inference**: The trained model operates seamlessly on classical hardware, bypassing the need for quantum computing resources, enhancing its practicality. The paper also discusses the efficiency and generalization performance of QT, showing that it achieves competitive accuracy with fewer parameters while maintaining or slightly reducing predictive accuracy. The framework's versatility is highlighted, suggesting its potential applications in various machine learning scenarios, including quantum reinforcement learning and generative models.