Quantum-Infused Whisper: A Framework for Replacing Classical Components

AbstractWe propose a compact hybrid quantum–classical extension of OpenAI’s Whisper in which classical components are replaced by Quantum Convolutional Neural Networks (QCNN), Quantum LSTMs (QLSTM), and optional Quantum Adaptive Self-Attention (QASA). Log-mel spectrograms are angle encoded and processed by QCNN kernels, whose outputs feed a Transformer encoder, while QLSTM-based decoding introduces quantum-enhanced temporal modeling. The design incorporates pretrained acoustic embeddings and is constrained to NISQ-feasible circuit depths and qubit counts. Although this work is primarily architectural, we provide a fully specified, reproducible evaluation plan using Speech Commands, LibriSpeech, and Common Voice, along with strong classical baselines and measurable hypotheses for assessing noise robustness, efficiency, and parameter sparsity. To our knowledge, this is the first hardware-aware, module-wise quantum replacement framework for Whisper.