Technology: From a defined problem to an evidence supported workflow
Quantum Links AI is developing an enterprise optimisation framework for supported operational and scientific problems. The system begins with the customer’s objectives, constraints and available data, then selects an appropriate available classical, machine learning or quantum workflow.
How the framework works
1. Define the problem
Establish the decision to be made, objectives, constraints, data requirements and relevant success measures.
2. Select a supported workflow
Use the relevant template and available module to select a classical, machine learning or quantum workflow.
3. Execute locally
Run the selected supported workflow and generate results.
4. Assess the real QPU option
For quantum candidates, assess technical feasibility, expected execution requirements and estimated cost.
5. Produce evidence
Present results and the recommended next step using measures appropriate to the workflow and, where suitable, an agreed classical baseline.
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.
Current technical foundation
Quantum Links AI has completed internal operational and freight optimisation demonstrators. This work combines problem structuring, classical comparison methods, local quantum simulation, controlled live quantum hardware execution and structured technical outputs. Live execution has been demonstrated on Rigetti hardware through AWS Braket.
What Phase 5 will add
Phase 5 will integrate existing modules into a guided customer framework for agreed supported workflows. Planned features include structured problem and data input, compatibility checks, reusable validated workflow configurations, repeat use checks and clearer assessment summaries. This work will make the existing technical foundation more repeatable for early customers.
Technology boundaries
Quantum Links AI does not claim that quantum will be the right route for every problem. It does not claim universal automated hardware selection or guaranteed quantum advantage. Current work is focused on supporting defined use cases and producing evidence for the most practical available route.