Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order
This work proposes a physics-informed quantum machine learning (PIQML) framework with hard constraint embedding, specifically designed for NISQ era, and demonstrates that the quantum model successfully learns the solution, showing close agreement with a high-precision classical numerical benchmark.