Secure & confidential Quantum-AI execution for cross-institution collaboration, built for regulated environments.
QAILinks provides a secure execution platform that enables organizations to collaboratively develop, deploy, and optimize AI models across institutional boundaries, without moving sensitive data.
The platform integrates:
- federated learning
- privacy-preserving computation
- distributed orchestration
- quantum computation
to support high-impact use cases in healthcare, pharmaceuticals, and regulated industries.
Core capabilities
Enable model training and analytics across organizations without centralizing data.
- Distributed model training
- Secure aggregation
- Cross-site coordination
- Data locality preservation
Ensure sensitive data remains protected across all workflows.
- Secure enclaves / trusted execution environments (TEE)
- Differential privacy (where applicable)
- Encryption-in-use and in-transit
- Policy-based data access control
Coordinate complex workflows across institutions, models, and datasets.
- Workflow orchestration
- Policy enforcement and governance
- Auditability and traceability
- Multi-party coordination
Enable distributed decision-making across systems.
- Cohort discovery and analytics
- Scheduling and resource optimization
- Demand forecasting and planning
- Multi-site model evaluation
Prepare organizations for emerging quantum and hybrid computing capabilities.
- Integration with hybrid quantum-classical workflows
- Support for optimization problem mapping
- Experimentation with quantum algorithms (where applicable)
- Integration with quantum hardware ecosystems
Key differentiators
Built for regulated environments
Designed for industries where data cannot be centralized: healthcare, pharmaceuticals, and government.
Cross-institution by design
No data pooling required; native support for multi-party collaboration.
Secure by architecture
Security is built into the execution layer and enforced through policy and infrastructure—not bolted on.
Bridges AI and quantum
Enables secure, distributed AI today while preparing for quantum and hybrid workflows as ecosystems mature.
Supported use cases
- Clinical AI collaboration
- Drug discovery partnerships
- Clinical trial optimization
- Hospital operations optimization
- Supply chain coordination
Deployment model
Flexible deployment depending on customer needs: on-premise (regulated environments), hybrid cloud, multi-institution deployments, and partner ecosystems.