Time series generation for option pricing on quantum computers using tensor network

This project is a technical implementation of a research paper (arXiv:2402.17148). It addresses a critical bottleneck in quantum finance: the 'data loading problem.' Specifically, it uses tensor networks to efficiently prepare quantum states that represent the joint distribution of asset prices over time, which is required for pricing path-dependent options (like Asian or Barrier options). From a competitive standpoint, the project currently has no organic traction (0 stars), which is expected for a niche academic repo only 8 days old. Its defensibility is low because it is a reference implementation rather than a platform or a tool with network effects. However, the underlying IP is highly specialized. Frontier labs (OpenAI, Anthropic) are unlikely to enter this space as it is hardware-dependent and deeply domain-specific. The primary risk comes from quantum hardware providers like IBM (via Qiskit Finance) or specialized quantum software firms like Multiverse Computing, who could integrate similar tensor-network-based loading techniques into their production libraries. The displacement horizon is long (3+ years) because quantum hardware capable of running these algorithms for real-world finance is still in the NISQ/Error-Correction transition phase.