Day 1: Initial Research Presentation

Progress Review:

• Topics: Students present their initial research on quantum algorithms suitable for drug discovery, focusing on:
  • Variational Quantum Eigensolver (VQE) for molecular energy calculations.
  • Quantum Approximate Optimization Algorithm (QAOA) for optimization problems in drug design.
  • Quantum Machine Learning for predicting drug properties.
• Feedback:
  • Discussion on the choice of algorithms, their suitability for drug discovery, and potential quantum hardware limitations.
  • Strategies for integrating quantum results with classical systems for a comprehensive drug discovery pipeline.

Technical Support:

• Introduction to Qiskit:
  • Overview of Qiskit's capabilities for quantum circuit design and simulation.
  • Hands-on session to set up Qiskit environment and run basic quantum circuits.

Peer Review:

• Group Discussions:
  • Feasibility of different quantum algorithms for drug discovery.
  • Sharing insights on noise mitigation techniques to ensure accurate results.

Resources:

• Qiskit for quantum circuit design and simulation.
• IBM Quantum Experience for access to real quantum hardware.

Mentor Guidance:

• Industry Insights: Regular meetings with mentors from the pharmaceutical industry to discuss practical applications, industry needs, and potential challenges in implementing quantum computing in drug discovery.

Day 2: Quantum Circuit Design and Simulation

Progress Review:

• Topics:
  • Students present their initial quantum circuit designs for VQE and other algorithms.
  • Discussion on circuit optimization for noise reduction and efficiency.

Technical Support:

• Debugging Sessions:
  • Help students run their quantum circuits on simulators to identify and fix errors.
  • Introduction to error mitigation techniques in Qiskit.

Peer Review: