Volatility time series modeling by single-qubit quantum circuit learning

Battle Count: 73

The paper addresses volatility forecasting, which is fundamental to quantitative trading for risk management, options pricing, and position sizing. However, the work is at a very early exploratory stage using only synthetic data and a single-qubit circuit. The practical relevance to trading is limited by the lack of real-data validation, absence of comparison with established models, and the current impracticality of quantum computing for production trading systems. The theoretical contribution to understanding quantum approaches to financial modeling is notable.

The QCL implementation is relatively simple: a single-qubit circuit with angle encoding (4 rotation gates), one parameterized unitary U(θ), and Z-basis measurement. The optimization uses COBYLA (a classical derivative-free optimizer). The RGARCH data generation is straightforward. However, the multifractal analysis (MDFA), Hurst exponent computation, and cross-correlation analysis add moderate complexity. Overall, the quantum component is minimal (single qubit), making it accessible via Qiskit without requiring quantum hardware.

The paper provides detailed equations for the QCL circuit, angle encoding, RGARCH model parameters (α=0.11, β=0.85, ω=0.005, γ=0.1), and specifies the use of Qiskit and COBYLA optimizer. However, no code repository is provided, and the exact Qiskit version, circuit depth, and number of optimization iterations are not fully specified. The synthetic data generation process is described but no seed or exact generation code is shared.