A hard-constraint, parameter-shift quantum circuit model is fitted to first-order ODEs, but its input-derivative formula omits a chain-rule factor and the validation uses the same reference data it trains on.
Study of stability criteria of numerical solution of or- dinary and partial differential equations using eulers and finite difference scheme
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Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order
A hard-constraint, parameter-shift quantum circuit model is fitted to first-order ODEs, but its input-derivative formula omits a chain-rule factor and the validation uses the same reference data it trains on.