QAOA-Based Quantum Machine Learning for Underwater Sonar Target Classification

We present the application of KeplerQ-Max, a hybrid quantum–classical machine learning platform built on the Quantum Approximate Optimization Algorithm (QAOA), to the problem of underwater acoustic target recognition using passive sonar data. The system employs a 25-qubit QAOA ansatz executed on IBM Heron R2 real quantum hardware (ibm_fez) to learn discriminative feature representations across three threat classes: Hostile, Unknown, and Friendly. Applied to a dataset of 5,000 sonar records, KeplerQ-Max completed full inference in 64 seconds (12.8 ms per record), classifying 73.58% of targets as Friendly, 6.14% as Hostile, and 20.28% as Unknown, with the Unknown class achieving a top posterior confidence of 98.9%. A total of 1,321 records (26.42%) were flagged as requiring operational action. The Hostile class achieved a mean quantum circuit score of 0.684 and a peak score of 0.806, while 101 high-confidence Unknown records (confidence ≥ 0.90) were identified as priority candidates for secondary sensor fusion. The model (kepler-qmax/qaoa-pytorch-25q9l) integrates a quantum-parameterized variational circuit with a PyTorch post-processing layer, enabling noise-resilient inference on near-term quantum devices at approximately 78 records per second. These results demonstrate that QAOA-based quantum machine learning achieves practical classification throughput on real sonar data without classical simulation, opening pathways for quantum-enhanced naval threat assessment and maritime security applications. Email: research@sonicium.ltd