embedded-quantum-machine-learning

name

embedded-quantum-machine-learning

description

Feasibility analysis and hybrid architecture design for embedding quantum machine learning workloads in resource-constrained embedded systems. Explores the intersection of quantum computing and edge/embedded deployment. Use for: embedded quantum ML feasibility, edge quantum computing, hybrid quantum-classical embedded architectures, quantum workload optimization for constrained systems. Triggered by: embedded quantum ML, edge quantum computing, quantum embedded systems, hybrid quantum embedded architecture.

Embedded Quantum Machine Learning

Overview

Analysis of feasibility and design patterns for embedding quantum ML workloads in resource-constrained embedded systems. Explores hybrid classical-quantum architectures that bridge NISQ quantum processors with edge computing constraints.

Key Considerations

Resource Constraints

Embedded systems face:

• Limited memory for quantum state simulation
• Power budget constraints for cryogenic interfaces
• Real-time latency requirements for quantum-classical feedback
• Communication bandwidth between classical controller and quantum processor

Hybrid Architecture Patterns

1. Classical Preprocessing + Quantum Inference

   • Classical edge device processes input data
   • Lightweight quantum circuit performs feature mapping
   • Classical readout layer on edge device

2. Quantum-Assisted Feature Extraction

   • Quantum circuit extracts high-dimensional features
   • Classical model consumes quantum features
   • Suitable for near-term quantum processors

3. Distributed Quantum-Classical Pipeline

   • Quantum workload runs on cloud quantum processor
   • Edge device handles data I/O and post-processing
   • Latency-tolerant workloads only

Feasibility Criteria

• Quantum circuit depth must fit within coherence time
• Classical-quantum communication latency acceptable
• Power consumption of quantum interface within budget
• Memory requirements for quantum state manageable

Activation Keywords

• embedded quantum ML
• edge quantum computing
• quantum embedded systems
• hybrid quantum embedded architecture
• quantum ML feasibility

Tools Used

• exec: Run Qiskit simulations
• python: Analyze resource constraints

References

• Semantic Scholar / arXiv: 2603.12540 — "Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures"