luckysitsinback (@luckysitsinback.bsky.social)

Design of a hybrid quantum machine learning architecture and analysis of quantum noise effects QML encompasses a wide range of approaches, shaped primarily by two dimensions: the nature of the input data (classical or quantum) and the type of hardware used for processing (classical or quantum). A commonly accepted taxonomy organizes the field into four categories, as illustrated in Fig. 118,19. 1. Classical–Classical (CC): Both the data and the computation are fully classical, but the algorithms borrow inspiration from quantum mechanics (for example, using Hilbert-space embeddings or tensor-network ideas20). No quantum hardware is involved, and while these methods can offer novel mathematical tools, they do not exploit genuine quantum resources. 2. Quantum–Classical (QC): Classical machine learning algorithms are applied to data produced by a quantum process—say, measurements from a quantum experiment or outputs of a quantum simulator. The learning itself remains classical, treating quantum-generated data as a special input modality21. 3. Classical–Quantum (CQ): In this paradigm, classical (often tabular or image) data are encoded into a quantum circuit, processed by a parametrized quantum subroutine, and then decoded back into classical form for further analysis or classification. CQ models harness quantum superposition and entanglement to enrich feature representations, while relying on mature classical layers for final decision making22. 4. Quantum–Quantum (QQ): Both the data and... https://www.nature.com/articles/s41598-026-42216-5