Quantum computing has the potential to transform computational science by introducing new paradigms for solving complex problems in drug discovery, materials science, optimization, and beyond. This course provides an in-depth exploration of quantum machine learning (QML), covering both quantum-native and hybrid quantum-classical algorithms designed to enhance real-world applications. Mohammad Ghazi Vakili is currently a Postdoctoral Researcher at the University of Toronto working with Professor Alan Aspuru-Guzik. He has developed quantum-assisted generative models for ligand design, secured substantial external funding, and published in top venues such as Nature Biotechnology and Nature Communication

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Guest Lectures: Mohammad Ghazi Vakili is currently a Postdoctoral Researcher at the University of Toronto working with Professor Alan Aspuru-Guzik. He has developed quantum-assisted generative models for ligand design, secured substantial external funding, and published in top venues such as Nature Biotechnology and Nature Communication The course will introduce fundamental concepts of quantum computing and machine learning, followed by detailed discussions on: Quantum generative models Quantum-assisted optimization techniques Hybrid learning frameworks We will analyze the latest advancements in QML, including real-world applications: Quantum-Assisted Drug Discovery (Vakili et al., Nature Biotechnology, 2024) Quantum generative models for molecular design Hybrid quantum-classical techniques accelerating drug discovery Quantum circuit-based models for novel drug candidate synthesis Quantum Generative Models for Optimization (Vakili et al., Nature Communications, 2024) Quantum generative algorithms for combinatorial optimization Quantum Circuit Born Machines (QCBMs) for solving constrained search problems Hybrid Quantum-Classical Algorithms for Learning Representations (Vakili et al., arXiv, 2024) Quantum circuits as feature extractors in classical deep learning pipelines Scalability and generalization improvements in real-world applications Topics Covered Fundamentals of Quantum Computing Qubits, superposition, entanglement, quantum gates Noisy Intermediate-Scale Quantum (NISQ) devices and their limitations Quantum Generative Models Quantum Circuit Born Machines (QCBMs) Quantum Variational Autoencoders (QVAE) Hybrid quantum-classical generative adversarial networks (QGANs) Quantum-Assisted Optimization Variational Quantum Eigensolver (VQE) Quantum Approximate Optimization Algorithm (QAOA) Applications in drug discovery, finance, and combinatorial problems Hybrid Quantum-Classical Machine Learning Quantum-enhanced kernel methods Quantum feature maps for classical ML models Transformer-inspired QML architectures Course Format Lectures on theoretical foundations and cutting-edge research Hands-on coding sessions using Qiskit, Cirq, and PennyLane Case studies based on recent high-impact research papers Final project, where students design and implement a QML model for a real-world problem

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P.D.2-2 - Giugno