David Strnadel

Background

About David Strnadel

I am a software engineer with 4 years of experience in the automotive industry and a deep passion for pushing the boundaries of artificial intelligence. I believe the greatest breakthroughs happen at the intersection of established disciplines and emerging technologies. Specialization: Hybrid Quantum-Classical AI Models My Bachelor's thesis,"Quantum Computational Power in AI: VQE as a Tool for Optimizing Generative Adversarial Networks"(graded A), introduces a novel approach to stabilizing and enhancing the training of GANs. I designed and successfully implemented a hybrid QACGAN model in a simulated environment (Qiskit), where the Variational Quantum Eigensolver (VQE) algorithm serves as a differentiable regularizer. In my experiments on the MNIST dataset, this approach led to demonstrably faster convergence and superior results (in terms of FID score) in fewer epochs compared to the classical model. I am seeking opportunities where I can apply my expertise in Quantum Machine Learning (QML), Generative AI, and advanced algorithm optimization to solve complex R&D challenges. Key Domains & Technologies: Quantum Machine Learning (QML) Generative Adversarial Networks (GANs, ACGAN) Variational Quantum Eigensolver (VQE) Quantum Simulation: IBM Qiskit Deep Learning Frameworks: PyTorch Software Engineering & Testing (ISTQB Certified)