Quantum Finance in Action: Workshop Content Index

Session Theme

“From Classical Monte Carlo to Quantum-Enhanced Financial Analytics”

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Workshop Structure (2 Hours)

#	File	Topic	Duration
1	Part_1_Introduction_to_Quantum_Finance.md	Introduction: Why Quantum Finance? — Computational challenges, quantum promise, session roadmap	10 min
2	Part_2_Risk_Assessment_Basics.md	Risk Assessment Basics — Portfolio risk (variance, covariance, diversification), VaR, CVaR, Monte Carlo applications	15 min
3	Part_3_Classical_Monte_Carlo_Python.md	Classical Monte Carlo in Python — GBM simulation, VaR computation, multi-asset portfolio, convergence analysis	30 min
4	Part_4_Quantum_Concepts_for_Finance.md	Quantum Concepts for Finance — Qubits, superposition, entanglement, quantum-finance mapping, O(1/√N) vs O(1/N)	20 min
5	Part_5_Quantum_Monte_Carlo_Python.md	Quantum Monte Carlo (Qiskit) — Quantum RNG, amplitude estimation workflow, convergence comparison	20 min
6	Part_6_Quantum_Portfolio_Optimization.md	Quantum Portfolio Optimization — Efficient frontier, QUBO formulation, QAOA/VQE	15 min
7	Part_7_Future_Scope_and_QA.md	Realistic Discussion + Q&A — Current limitations, hybrid roadmap, resources, anticipated questions	10 min

Content Summary

What Each Part Contains

• Full speaker content with explanations, examples, and analogies
• Python code ready to run (Parts 3, 5, 6)
• Visual diagrams in ASCII art for circuit and workflow illustrations
• Comparison tables for at-a-glance understanding
• Speaker notes with delivery tips
• References with proper academic citations backed by scite literature search

Key Citations Used

• Brassard et al. (2002) — Quantum amplitude estimation foundations
• Montanaro (2015) — Quantum speedup of Monte Carlo methods
• Woerner & Egger (2019) — Quantum risk analysis (IBM)
• Brandhofer et al. (2022) — QAOA portfolio optimization benchmarks
• Wilkens & Moorhouse (2023) — Quantum computing for financial risk measurement
• Herman et al. (2022) — Comprehensive survey of quantum computing for finance
• Carrera Vazquez & Woerner (2021) — Efficient state preparation for QAE

Tech Stack

Purpose	Tool
Classical simulation	NumPy
Data analysis	Pandas
Visualization	Matplotlib
Quantum SDK	Qiskit
Finance module	qiskit-finance
Optimization	qiskit-optimization

Setup Command

pip install numpy pandas matplotlib qiskit qiskit-aer qiskit-finance qiskit-optimization qiskit-algorithms