SPATE: Spiking-Phase Adaptive Temporal Encoding for Quantum Machine Learning

Most quantum machine learning (QML) pipelines still rely on static encodings such as angle and amplitude maps, and this limits their ability to handle temporal information. To address this limitation, this paper uses spike-based data representation as an effective encoding mechanism that incorporates temporal structure into quantum feature preparation. Specifically, we propose Spiking-Phase Adaptive Temporal Encoding (SPATE), a novel spike-driven temporal encoding method that converts real-valued tabular features into leaky integrate-and-fire spike trains and maps spike statistics to quantum rotations, augmented with a small set of temporal qubits through controlled phase operations. An encoding-centric evaluation protocol is also introduced to assess representation quality independently of the classifier, covering centered kernel-target alignment (CKTA), Fisher-style separability, inter/intra-class distance ratios, silhouette score, normalized entropy, and pairwise total-variation (TVpair) collapse indicators. Under stratified cross-validation, SPATE yields stronger representations across multiple datasets. For example, SPATE reaches a CKTA of 0.966 and a Fisher score of 7.37 on Blobs, compared with a CKTA of 0.632 and a Fisher score of 0.70 using angle encoding, and achieves a CKTA of 0.506 on Moons, compared with 0.015 using angle or amplitude encoding. These gains translate into stronger hybrid quantum neural network performance within a fixed qubit budget across several tasks, including an accuracy of 0.826 and an AUC of 0.978 for Wine, as well as an accuracy of 0.840 and an AUC of 0.923 for Moons. These results demonstrate that SPATE provides a practical spike-to-phase interface for building more informative quantum feature representations under constrained resources.