Quantum Computing in Protein Design: Foundations to Applications using Open Tools

To introduce participants to the interdisciplinary fusion of quantum computing and protein design, equipping them with the foundational concepts and hands-on skills using open-source quantum tools. The workshop enables learners to understand protein folding, quantum algorithms, and their real-world biological applications.

• Understand quantum computing principles and their application to biological systems

• Learn the fundamentals of protein structure and folding problems

• Apply quantum algorithms like VQE and QAOA to biomolecular challenges

• Gain hands-on experience with IBM Quantum and D-Wave platforms

• Build simple QML models to analyze protein-based datasets using open-source tools

• Introduction to Quantum Computing • Qubits, superposition, entanglement • Classical vs quantum computing
• Primer on Protein Structure and Design • Primary to quaternary structures • Protein folding problem
• Challenges in Protein Folding • Energy landscapes • NP-hard nature of folding
• Quantum Algorithms in Biology • VQE and QAOA explained with relevance to molecular systems
• Hands-on Demo with IBM Quantum Experience • Create account • Visualize gates and run basic circuits

Day 2: Quantum Applications for Protein Design and Learning

• Quantum Annealing & Protein Folding • Lattice models (e.g., HP) • QUBO formulation and quantum annealers
• Hands-on with D-Wave Leap • Environment setup • Run protein folding example via Ocean SDK / Leap IDE
•  Quantum Machine Learning (QML) • Frameworks: PennyLane, Qiskit ML • Applications in protein-ligand interactions
• Protein Feature Classification with QML • Preprocessing data • Building a QNN in PennyLane
• Future of Quantum Protein Design • Ethics, careers, and emerging research paths

• Clear understanding of protein design and quantum computation synergy

• Practical skills in using IBM and D-Wave platforms

• Ability to frame protein folding problems for quantum solvers

• Introduction to real-world use cases of quantum biology

• Hands-on experience with quantum neural networks

• Undergraduate or postgraduate degree in Biotechnology, Bioinformatics, Physics, Computer Science, or related fields.
• Professionals in quantum computing, life sciences, pharmaceutical R&D, or computational biology sectors.
• Individuals with a strong interest in cutting-edge applications of quantum technologies in biology.