Qiskit Quantum Seminar

Currently Playing: Conditional Generative Models for Learning Stochastic Processes | Qiskit Seminar Series

Qiskit Seminar Series episode 129 with Dr. Andrew Vlasic & Sal Certo Host: Maria Violaris Slides: https://drive.google.com/file/d/1UgaVLLi_oF6cXj0VTnFQjADHyWDMk1Xf/view?usp=share_link A framework to learn a multi-modal distribution is proposed, denoted as the Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network structure is strictly within a quantum circuit and, as a consequence, is shown to represent a more efficient state preparation procedure than current methods. This methodology has the potential to speed-up algorithms, such as Monte Carlo analysis. In particular, after demonstrating the effectiveness of the network in the learning task, the technique is applied to price Asian option derivatives, providing the foundation for further research on other path-dependent options. Dr. Andrew Vlasic has a PhD in mathematics with extensive experience in fundamental and applied research that spans academia, the DoD (Army Research Laboratory), and industry (Bank of America). Since joining Deloitte, Dr. Vlasic specializes in fundamental quantum algorithms, making advancements in quantum machine learning, and optimization problems. Sal Certo is an experienced data scientist helping companies solve their hardest problems utilizing the latest technology. With experience in deep learning, graph analytics, and operations research, he is leveraging the latest quantum algorithms to provide state-of-the-art solutions in optimization and machine learning.