HN-PQE: Hardware-Native Parameterized Quantum Embedding for Noise-Resilient Classifications of Medical Signals and Images

Quantum neural networks are a popular realization of quantum machine learning. Typically, classical data is first embedded into the state vector of a quantum system. A parameterized quantum circuit (PQC) then acts on the quantum state, with its trainable parameters continuously adjusted during the optimization process. However, current quantum hardware faces challenges, such as accumulating noise with increasing circuit complexity and a limited set of native gate operations. As a result, transpiling quantum circuits to real hardware often increases the number of the required gate instructions. These factors set considerable constraints on the accuracy of quantum machine learning models. To address these limitations, we propose a novel quantum neural network architecture, termed hardware-native parameterized quantum embedding (HN-PQE). Our approach offers two key advantages: First, HN-PQE exclusively employs the native gates of IBM's current Eagle quantum processor family, ensuring that the gate count remains stable during transpilation. Second, by combining data embedding with the PQC, HN-PQE achieves a highly compact circuit design. We empirically validate our approach on two classification tasks, simulating both idealized (no hardware noise) and noisy conditions (mimicking IBM's Brisbane device). When compared to two popular quantum neural networks, HN-PQE showed similar performance in idealized scenarios but demonstrated significant noise resilience under hardware-noise conditions. This resilience makes HN-PQE a promising candidate for future quantum machine learning experiments on real hardware.