Day 941: Probabilistic Error Cancellation - Quasi-Probability Decomposition

Learning Objectives

• •**Derive quasi-probability decompositions** - Express ideal operations as linear combinations of noisy operations
• •**Calculate sampling overhead** - Quantify the cost of PEC in terms of variance amplification
• •**Implement noise inversion** - Construct inverse channels for common noise models
• •**Apply Monte Carlo PEC** - Sample from quasi-probability distributions to cancel errors
• •**Optimize PEC protocols** - Balance tomography cost vs mitigation benefit
• •**Compare PEC to other methods** - Understand when PEC is preferred over ZNE

Today's Schedule (7 hours)