Synergy T1
Quantum-Ready Cyber Lab Powered by HPC Simulation
A very practical flagship capability is a Quantum-ready Cyber Lab:
HPC
Use HPC GPUs to run quantum simulators (state-vector / tensor-network and noise simulations).
Quantum
Use that to stress-test and benchmark post-quantum crypto deployments, protocols, and performance tradeoffs.
Cyber
Use cyber controls to provide multi-tenant access safely (audit trails, isolated workspaces, controlled datasets).
This is compelling because it produces outputs that decision-makers understand:
- "How much slower is PQC TLS for our services?"
- "Which algorithms are feasible for constrained environments?"
- "What migration sequencing minimizes risk?"
Platform alignment: This leverages exactly what the stack is already building:
GPU-centric compute, Kubernetes-managed toolchains, and integrated security observability.
Synergy T2
Secure "Quantum-as-a-Service" (QaaS) via Cloud-Native Control Plane
A next-level synergy is to expose quantum capability as an internal/external service:
Kubernetes, HPC
- Runs toolchains and orchestrators (jobs, workflows, notebooks, CI/CD)
- Slurm-style batch and GPU scheduling handles heavy simulation workloads
Quantum Backends
- Simulators, external QPUs exposed through controlled interfaces
- Multiple QPU vendors through CUDA-Q integrations
Cyber Governance
- Enforces policy, auditing, segmentation
- Incident response and compliance
What this creates
A credible "one platform" story: classical HPC, cloud, quantum endpoints, security under one governance model.
Synergy T3
AI-Driven Cyber Defense, Quantum Experimentation on One GPU-First Platform
A differentiator is that the same GPU investment supports:
- Quantum simulation / experimentation
- AI/ML security analytics (e.g., phishing/fraud/detection models, traffic analytics)
- Accelerated inference services
This enables a single operational pattern:
Unified
GPU Scheduling
Shared
MLOps Stack
Shared
Observability
Shared
Data Layer (Ceph)
Shared
Identity & Policy
Where the ROI lives: This "shared substrate" reuses the same platform primitives
across multiple research and product tracks.