Thesis title: From Computational Cost Reduction to Industrial Applications: A Study on Variational Quantum Circuits
Quantum computing has evolved into a promising computational paradigm capable of addressing problems beyond the reach of classical methods. Among current approaches, hybrid quantum–classical algorithms as Variational Quantum Algorithms (VQAs)—play a central role in the Noisy Intermediate-Scale Quantum (NISQ) era.
This thesis analyzes the research conducted during the PhD within the variational framework along two main directions.
First, it introduces the Quantum Non-Demolition Measurement (QNDM) method, a novel strategy for efficient gradient estimation in VQAs. By coupling an auxiliary detector qubit through non-demolition operators, QNDM enables derivative extraction from a single measurement, reducing quantum resource requirements linearly with the system dimension.
The second part explores the application of quantum machine learning to the solution of differential equations, extending the Differential Quantum Circuit (DQC) framework to stochastic systems. This work was carried out during a one-year research internship at the French scale-up Pasqal.