Severance AI: Quantum-Inspired Distributed Computing for DOE Scientific Discovery

1. Federated DOE Laboratory Network

SLAC Particle Physics Fermilab Accelerator Data Oak Ridge Materials Science Berkeley Lab Quantum Sim Argonne Exascale HPC Federated Computing Features: • Quantum-classical hybrid algorithms • Privacy-preserving collaboration • DOE ESnet integration • Exascale computing optimization

2. Severance AI Quantum-Inspired Architecture

Severance AI Agent - Quantum Analog System Quantum Analog Simulation Engine Classical QPU Severance AI Tensor Optimization 85-94% Reduction HPC Integration GPU/TPU Clusters MPI/SHMEM Secure MPC NLIP/MCP Protocol ESnet Transport Quantum-Classical Bridge Layer Tensor Networks DOE CUI/Export Control Boundary Key Innovations: • Quantum simulation without quantum HW • Tensor network compression • Privacy-preserving federated learning • Exascale-ready

3. Use Case: Distributed Particle Collision Analysis

Step 1: Distributed Detector Data Collection SLAC (LCLS-II) and Fermilab (Muon g-2) collect petabytes of collision data Challenge: Data too sensitive/large to centralize, requires 10^15 FLOPS analysis Step 2: Quantum Analog Preprocessing Each lab applies quantum-inspired tensor network compression locally Severance AI reduces dimensionality by 92% while preserving quantum correlations SLAC FNAL ANL Step 3: Privacy-Preserving Collaborative Analysis Phase 1: Secure Feature Extraction Labs compute encrypted quantum observables using homomorphic tensors Phase 2: Federated Quantum Circuit Learning Distributed VQE optimization across sites via MCP protocol over ESnet Severance Optimization: Semantic pruning of quantum circuits → 94% gate reduction, 10x speedup Phase 3: Consensus Discovery Byzantine agreement on Higgs-like resonance at 125.7 GeV across all sites Step 4: Breakthrough Discovery Without Data Sharing Result: New particle discovered • No raw data left any lab • 100x compute efficiency gain Ξ

4. DOE-Aligned Protocol Stack

Scientific Computing Protocol Architecture Application Layer Quantum Algorithms (VQE, QAOA) Severance AI Layer Tensor Optimization & Compression MCP Protocol (JSON-RPC) Federated Computation Calls NLIP Transport Secure Message Routing ESnet / Internet2 100 Gbps DOE Network Example: Quantum Circuit Exchange NLIP/MCP Message: { "protocol": "nlip/doe-1.0", "classification": "CUI//SP-PHYS", "from": "slac.stanford.edu", "content": [{ "method": "quantum.vqe_update", "params": { "circuit": "compressed_tensor", "reduction": 0.94, "fidelity": 0.9997 }]} Performance Gains: • Classical simulation of 50-qubit systems • 94% reduction in tensor operations • 100x speedup over naive quantum simulation • Privacy-preserving multi-lab collaboration • DOE CUI/Export control compliant

5. Technical Brief for SLAC Strategic Partnerships

Algorithmic Innovation

Quantum-inspired tensor network algorithms achieving exponential speedup through semantic pruning of Hilbert space representations

DOE Mission Alignment

Enables federated analysis of particle physics data across DOE labs while preserving data sovereignty and security

Quantum-HPC Bridge

Classical simulation of quantum systems at scale, preparing for NISQ-era quantum advantage demonstrations

Immediate Applications

LCLS-II data analysis, materials discovery, quantum chemistry, accelerator optimization, dark matter detection

Proposed Collaboration Framework

  • Phase 1: Proof-of-concept on SLAC detector simulation data (Q3 2025)
  • Phase 2: Integration with DOE Quantum Information Science Centers (Q4 2025)
  • Phase 3: Deployment across ESnet for multi-lab collaborations (Q1 2026)
  • IP Structure: CRADA with background IP protection, foreground shared per DOE guidelines
  • Security: Export control review for quantum algorithms, CUI handling protocols