Quantum-KIP: Kernel Inducing Points for Quantum Privacy

Summary

Quantum-KIP compresses a training set into a small set of kernel inducing points with soft labels, using a quantum feature map and forward evaluations only, avoiding backpropagation through quantum circuits. The authors show that replacing one training example changes the learned set and predictions by O(m/n), and they bound privacy-relevant distinguishability under finite-shot and depolarizing noise. On MNIST and CIFAR-10 with six-qubit feature maps, it reaches accuracy close to full-data training with fewer quantum runs and reduced privacy leakage.