Parallel data processing in quantum machine learning

We propose a Quantum Machine Learning (QML) framework that applies the core design principle of quantum algorithms–superposition, oracle, and interference–to accelerate training. Building on the structural analogy between feature extraction in foundational quantum algorithms and parameter optimization in QML, we reformulate the training process to leverage quantum parallelism: all training samples are encoded into a quantum superposition, processed through a parameterized quantum circuit, and classified via an interferometer module that implements quantum interference across the dataset. This architectural reformulation reduces the theoretical complexity of loss function evaluation from \(O(N²)\) in conventional QML training to O ( N ), where N is the dataset size. Numerical simulations on multiple binary and multi-class classification datasets (with up to \(N=128\) samples) demonstrate that our method achieves classification accuracies comparable to conventional circuits while reducing the number of quantum circuit executions per cost function evaluation from N to 1. This represents a near N -fold reduction in quantum overhead per training iteration, reducing the required circuit executions without loss of accuracy. These results highlight the potential of quantum algorithmic design principles as a scalable pathway to efficient QML implementations.