Privacy Aware Quantum–Classical In-Context Learning for Tabular Data

Remote quantum serving for tabular in-context learning must simultaneously address privacy-sensitive context data, variable-sized task inputs, communication overhead, and tight quantum-resource constraints. We propose QICL, a privacy-aware split quantum-classical framework built around a compact structured interface between the client and a remote quantum server. On the client side, QICL maps row-level contextual representations into bounded class-wise summaries and combines them with class-prior information to form a compact interface with explicitly controlled record-level sensitivity. A block-wise Gaussian mechanism privatizes this interface before transmission, while the interface changes the dominant context communication from record-wise to class-wise scaling. On the server side, a candidate-aware relational predictor constructs query-candidate-rival interactions before compressing them into quantum-compatible coordinates for few-qubit prediction. We establish record-level differential privacy for the uploaded privatized interface and derive a condition linking privacy perturbation to prediction stability. Experiments on TALENT classification datasets evaluate QICL against multiple tabular ICL models, strong conventional tabular predictors, and classical prediction heads under the same structured interface, demonstrating a favorable privacy-utility-communication trade-off under constrained quantum resources.