Technology | Quantum Links AI - How Intelligent Routing Works
The Intelligent Routing Engine
At the heart of Quantum Links AI is a sophisticated, data-driven routing engine. It leverages a vast library of pre-built benchmarks to create a performance map that predicts the time, cost, and energy consumption of a given task on different hardware. This allows the system to make a sub-second decision on whether a quantum advantage is achievable and routes the workload accordingly.
Our platform moves beyond simplistic, rule-based routing to a dynamic, predictive model that learns and improves over time. It is an autonomous, self-learning engine that intelligently routes computational problems to the optimal resource—classical or quantum—in real time.
A Robust, Production-Ready Foundation
Current Status: Phase 3 Complete – Live Quantum Hardware Execution Confirmed
Codebase: 33,000+ lines of production-quality Python code
Quantum Frameworks: Cirq, PennyLane, Qiskit
Hardware Integrations: AWS Braket (Rigetti – live ), IBM Quantum (live). Azure Quantum and Google Quantum AI: planned.
Simulated QPU Profiles: High-fidelity digital twins of IonQ, IBM, and Rigetti hardware using realistic noise parameters
Classical Frameworks: PyTorch, TensorFlow
Architecture: A modular, API-driven architecture designed for scalability and the seamless integration of new hardware and frameworks.
From Validation to Customer Value
Phase 1: Foundation (Complete)
Built and validated the core orchestration engine, demonstrating 72%+ routing accuracy across 20+ diverse computational benchmarks in a simulated environment.
Phase 2: Hardware Integration (Complete)
Hardware Integration (Complete) Developed the adaptive routing engine and successfully integrated with the AWS Braket SDK and IBM Quantum Platform. The platform can now communicate directly with physical quantum computers, query their status, and submit jobs.
Phase 3: Real Hardware Execution (Complete)
Real Hardware Execution (Complete) Successfully executed QAOA Max-Cut and Quantum Logistics Optimisation on a real QPU (Rigetti Cepheus-1-108Q via AWS Braket), confirming end-to-end platform operation on live quantum hardware. VQE (Variational Quantum Eigensolver) is supported within the platform architecture as a validated workflow capability.
Phase 4: Quantum Multi-Agents & Self-Service Framework (Upcoming)
Quantum Multi-Agents & QSVM Module (Upcoming) Shifting from single-problem optimisation to collaborative quantum decision-making via Quantum Multi-Agents. Initial modules include EV Fleet Charging Optimisation and a Quantum-Enhanced Portfolio Risk Assessor. This phase also includes development of the QSVM (Quantum Support Vector Machine) module for quantum machine learning workflows.
Phase 5: Quantum Co-pilot & Enterprise Deployment (Upcoming)
Self-Service Framework & Enterprise Deployment (Upcoming) Introducing a Self-Service Quantum Optimisation Framework, allowing business users to define and run optimisation problems within pre-built templates — no quantum expertise required. Following this, the platform will be deployed on Microsoft Azure with enterprise-grade security and communication layers. A Quantum Co-pilot (NLP / Explainable AI) feature is also planned, subject to requirements.