Physics-Informed Neural Networks (PINNs) aim to solve ordinary differential equations (ODEs) and partial differential equations (PDEs). So far, classical digital NNs have been successfully applied to many PDEs, such as the heat equation, Poisson equation, Navier-Stokes equation, to name a few. Recently, Continuous Variable Quantum Neural Networks (CVQNNs) have also been used to solve PDEs in...
With the emergence of quantum programming languages and compilers, the necessity for optimization naturally arises. In 2022, we introduced a new quantum language called Qubla, designed to facilitate the generation of quantum counterparts to classical (binary) algorithms. The Qubla compiler constructs a sequence of quantum operators based on a description of mixed classical/quantum computation,...
Variational quantum algorithms are widely regarded as promising approaches for achieving quantum advantage on near-term hardware. These algorithms typically rely on training parameterized quantum circuits through a classical optimization loop, most often using gradient-based methods with gradients computed via parameter-shift rules. However, in the continuous-variable (CV) model of photonic...
