Goldman Sachs Quantum Portfolio Pricing
Derivative pricing and risk modeling
Overview
Goldman Sachs has invested in quantum computing research for derivative pricing and risk modeling. In collaboration with IBM Quantum, the firm has developed quantum algorithms for pricing financial derivatives using quantum amplitude estimation.
The research demonstrates that quantum algorithms can achieve quadratic speedups for Monte Carlo-based pricing, potentially transforming how financial institutions assess risk and value complex instruments.
Vendor
IBM Quantum
Quantum Modality
Superconducting
Benefits
Quadratic speedup for derivative pricing enables real-time risk assessment. Larger and more complex instruments can be priced accurately.
ROI Metrics
Monte Carlo speedup from hours to seconds once fault-tolerant quantum computers are available. Near-term gains on bounded problems.
Architecture
Hybrid quantum-classical pipeline with Qiskit Runtime for quantum amplitude estimation and classical post-processing for financial analytics.
Migration Notes
Identify Monte Carlo-intensive workloads. Use Qiskit Finance for algorithm development. Deploy via IBM Cloud Quantum.
Security Considerations
Financial data security is paramount. Transition to post-quantum cryptography for all client communications.
Implementation Roadmap
Phase 1: Algorithm development (complete). Phase 2: Bounded problem testing (ongoing). Phase 3: Fault-tolerant deployment (2027+).