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Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors

Somdip Dey, Syed Muhammad Raza

Abstract

Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQML is technically feasible only in limited and highly experimental forms: (i) hybrid workflows where an embedded device performs sensing and classical processing while offloading a narrowly scoped quantum subroutine to a remote quantum processing unit (QPU) or nearby quantum appliance, and (ii) early-stage "embedded QPU" concepts in which a compact quantum co-processor is integrated with classical control hardware. A practical bridge is quantum-inspired machine learning and optimisation on classical embedded processors and FPGAs. This paper analyses feasibility from a circuits-and-systems perspective aligned with the academic community, formalises two implementation pathways, identifies the dominant barriers (latency, data encoding overhead, NISQ noise, tooling mismatch, and energy), and maps them to concrete engineering directions in interface design, control electronics, power management, verification, and security. We also argue that responsible deployment requires adversarial evaluation and governance practices that are increasingly necessary for edge AI systems.

Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors

Abstract

Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQML is technically feasible only in limited and highly experimental forms: (i) hybrid workflows where an embedded device performs sensing and classical processing while offloading a narrowly scoped quantum subroutine to a remote quantum processing unit (QPU) or nearby quantum appliance, and (ii) early-stage "embedded QPU" concepts in which a compact quantum co-processor is integrated with classical control hardware. A practical bridge is quantum-inspired machine learning and optimisation on classical embedded processors and FPGAs. This paper analyses feasibility from a circuits-and-systems perspective aligned with the academic community, formalises two implementation pathways, identifies the dominant barriers (latency, data encoding overhead, NISQ noise, tooling mismatch, and energy), and maps them to concrete engineering directions in interface design, control electronics, power management, verification, and security. We also argue that responsible deployment requires adversarial evaluation and governance practices that are increasingly necessary for edge AI systems.
Paper Structure (33 sections, 1 figure, 1 table)

This paper contains 33 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. BACKGROUND: QML PRIMITIVES AND EMBEDDED CONSTRAINTS
  3. QML Building Blocks
  4. Embedded Constraints Relevant to QML
  5. IMPLEMENTATION PATHWAYS FOR EMBEDDED QML
  6. Pathway 1: Hybrid Embedded--Quantum Systems
  7. Partitioning and Dataflow
  8. Feasible Application Classes
  9. IoT anomaly detection and security
  10. Embedded robotics and autonomy
  11. Authentication and trust services
  12. Circuits-and-Systems Implications
  13. Limitations
  14. Quantum-Inspired Methods on Embedded Hardware
  15. Tensor-Network Learning and Compression
  16. ...and 18 more sections

Figures (1)

  • Figure 1: Two EQML implementation pathways. Pathway 1 (Hybrid offload): the embedded MCU/SoC performs sensing and classical preprocessing, then offloads a bounded quantum subroutine to a remote QPU or nearby quantum appliance (QPA), returning kernel values/logits/gradients for fusion with a classical fallback. Pathway 2 (On-device QSoC): a local QPU module is coupled to the MCU/SoC via a low-latency interconnect to enable tighter quantum--classical loops without network dependence.