Speaker
Zoltán Kolarovszki
Description
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 quantum computing, gradient evaluation poses a significant challenge due to the lack of exact parameter-shift rules. In this work, we introduce an alternative optimization strategy for CV variational quantum circuits based on interpolation techniques.
