| name | qnn-option-pricing-nisq |
| description | Quantum Neural Network (QNN) approach for option pricing on NISQ hardware - methodology for implementing quantum derivative pricing across multiple quantum processors, benchmarking cross-platform performance, and approximating Black-Scholes-Merton pricing functions using QNNs. arXiv: 2604.19832 |
QNN Option Pricing on NISQ Hardware
Quantum Neural Network methodology for option pricing on Noisy Intermediate-Scale Quantum (NISQ) computers. Cross-platform evaluation of QNN-based derivative pricing on real quantum hardware.
Activation
Keywords: quantum option pricing, QNN derivative pricing, NISQ finance, quantum Black-Scholes, quantum neural network pricing, cross-platform quantum benchmark, quantum hardware finance
Core Concepts
Problem Setting
- Global derivatives market with notional values in hundreds of trillions of dollars
- Accuracy and efficiency of pricing models critical for risk management, capital allocation, regulatory compliance
- Black-Scholes-Merton (BSM) framework used as controlled benchmark environment
QNN Architecture
- Compact 2-qubit QNN for option pricing function approximation
- Exploits geometric structure of Hilbert space to approximate pricing functions
- Parameters optimized via classical-quantum hybrid training loop
Hardware Evaluation
Cross-platform study across 4 state-of-the-art quantum processors:
- IBM Fez (superconducting)
- IQM Garnet (superconducting)
- IonQ Forte (trapped-ion)
- Rigetti Ankaa-3 (superconducting)
Key Findings
- Distinct hardware-dependent performance characteristics revealed
- Accurate pricing approximations achievable consistently across different devices
- Demonstrates viability of QNN approaches for derivative pricing despite NISQ constraints
- Results extendable to more complex models: local volatility, stochastic volatility, interest rate frameworks
Workflow for Agents
Step 1: Define Pricing Problem
S = spot_price
K = strike_price
T = time_to_maturity
r = risk_free_rate
sigma = volatility
Step 2: Encode into QNN
- Map BSM input space to quantum state preparation
- Use parameterized quantum circuits as the QNN
- Encode asset price, strike, time into qubit rotations
Step 3: Train on Quantum Hardware
- Hybrid classical-quantum optimization loop
- Classical optimizer updates circuit parameters
- Quantum hardware evaluates circuit (expectation values)
- Loss function: MSE between QNN output and true BSM price
Step 4: Cross-Platform Benchmarking
- Run identical QNN on multiple quantum processors
- Compare pricing accuracy, circuit fidelity, noise resilience
- Identify hardware-specific performance characteristics
Step 5: Extension to Complex Models
- Local volatility models
- Stochastic volatility (Heston, etc.)
- Interest rate frameworks
Pitfalls
NISQ Hardware Constraints
- Coherence times: Limit circuit depth
- Gate errors: Accumulate noise in deeper circuits
- Qubit count: 2-qubit architecture is minimal; scaling requires error correction
- Calibration drift: Hardware performance varies day-to-day
Encoding Challenges
- State preparation: Mapping financial parameters to quantum states requires careful encoding
- Range normalization: Financial parameters span wide ranges; quantum states require normalized inputs
- Measurement precision: Limited shots affect pricing accuracy
Cross-Platform Comparison
- Different noise models: Each hardware platform has unique error characteristics
- Calibration schedules: Hardware recalibrated regularly, affecting reproducibility
- Gate set differences: Different platforms support different native gate sets
Future Directions
- Extension to path-dependent options (Asian, barrier, lookback)
- Multi-asset option pricing
- Real-time pricing with streaming data
- Integration with quantum Monte Carlo methods
- Hybrid classical-quantum pricing pipelines for production systems
Related Papers
- arXiv:2604.19832 - This paper (QNN option pricing on NISQ)
- Existing skills:
quantum-portfolio-optimizer, quantum-finance-portfolio
- Related:
quantum-ml-patterns, quantum-ml-healthcare
