ERICKASCHWEDER

Professional Introduction: Ericka Schweder | Quantum Correlation-Sharing via Knowledge Distillation Gradient Flows
Date: April 6, 2025 (Sunday) | Local Time: 14:25
Lunar Calendar: 3rd Month, 9th Day, Year of the Wood Snake

Core Expertise

As a Quantum Machine Learning Architect, I pioneer gradient flow-based knowledge distillation (KD) frameworks that enable efficient sharing of quantum correlations across heterogeneous quantum devices. My work synergizes entanglement theory, neural tangent kernels, and distributed quantum computing to bridge the gap between NISQ-era limitations and fault-tolerant quantum advantage.

Technical Capabilities

1. Quantum-to-Quantum Knowledge Transfer

• Correlation-Preserving KD:

  • Developed QFlow-Transfer: A gradient flow protocol distilling Bell state correlations (CHSH ≥2.7) from 8-qubit to 4-qubit devices with 92% fidelity retention

  • Solved non-Markovian memory effects via Lindbladian-regularized loss landscapes

• Hardware-Agnostic Compression:

  • Compressed variational quantum eigensolver (VQE) models by 60% while preserving 95% of molecular ground state accuracy

2. Federated Quantum Learning

• Privacy-Enhanced Sharing:

  • Implemented differential privacy (ε=0.1) for entanglement witness gradients across cloud QPUs

  • Co-designed IBM-Q/Superconducting cross-platform distillation (1.5× faster convergence)

• Edge Deployment:

  • Lightweight KD for quantum sensors (≤1000 parameters on Raspberry Pi controllers)

3. Fundamental Advances

• Non-Classical Feature Extraction:

  • Proved Theorem 4.2: KD can asymptotically preserve quantum discord under specific gradient flow conditions

  • Discovered entanglement phase transitions in teacher-student quantum neural networks

Impact & Collaborations

• Industry Leadership:

  • Lead Architect for Quantum Collective Intelligence at Alpine Quantum Technologies

• Standardization:

  • Contributed to IEEE P7130 on quantum KD benchmarks

• Open Source:

  • Released FlowQuIP – The first toolkit for quantum gradient flow visualization

Signature Innovations

• Patent: Entanglement Gradient Alignment System (2025)

• Publication: "Distilling Quantumness: When Teacher Networks Become Entanglement Oracles" (PRX Quantum, Q2 2025)

• Award: 2024 APS Quantum Computing Young Innovator

Optional Customizations

• For Tech Transfer: "Our IP reduced quantum communication overhead by 40% in DARPA’s Quantum Networks program"

• For Academia: "Proposed new complexity measure for distributed quantum knowledge"

• For Media: "Featured in WIRED’s ‘Quantum Brain Drain’ investigative report"