Pioneering research at the intersection of quantum computing, tensor networks, and logistics optimization.
Peer-reviewed research from the QRADHA team.
Dr. A. Quantum, Dr. B. Neural, Dr. C. Tensor
We present a novel approach to logistics optimization using tensor network contraction methods, achieving polynomial speedup over classical algorithms.
Dr. D. Matrix, Dr. E. State, Dr. F. Product
A comprehensive study of MPS-based algorithms for solving large-scale intermodal logistics problems with exponential state spaces.
Dr. G. Port, Dr. H. Harbor, Dr. I. Dock
Application of quantum-inspired optimization techniques to real-world port operations, demonstrating 34% efficiency improvements.
Dr. J. Supply, Dr. K. Chain, Dr. L. Network
A scalable framework for simulating quantum systems applied to supply chain optimization problems.
Real-world implementations and measurable results.
Managing 45,000+ container movements daily with legacy systems causing significant delays.
Implemented QRADHA Resiliency Engine for real-time optimization of berth allocation and rail coordination.
Coordinating vessel schedules across 12 major ports with unpredictable weather patterns.
Deployed FX-700 Quantum Simulator for multi-port schedule optimization with weather integration.