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

Somdip Dey; Syed Muhammad Raza

cs.LGMar 13, 2026

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

Authors: Somdip Dey, Syed Muhammad Raza

Organizations: School of Engineering and Computing, Regent College London, London, United Kingdom

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.

Figures & tables

Architecture	Typical Latency	Determinism	Suitable Tasks
Classical MCU / SoC	$\mu$ s–ms	High	Control, safety-critical loops
Hybrid EQML (remote QPU)	ms–s	Low	Background inference, optimisation
QSoC-based EQML (on-device QPU)	ms (est.)	Medium	Advisory inference, tuning

TABLE I: Latency, Determinism, and Suitable Tasks for Classical and EQML Architectures

Fig. 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.

Quantum Machine Learning

Quantum machine learning uses quantum circuits and quantum data to build learning algorithms and study their computational and statistical limits.

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Machine learning is widely regarded as a promising application in which quantum computing could offer a computational advantage. However, general quantum machine learning models with provable guarantees on performance, trainability, and efficient inference remain limited. In this work, we introduce quantum Hamiltonian embeddings, a supervised learning framework in which a classical training dataset is encoded into a single effective, parameterized Hamiltonian. Training optimizes the parameters of this data-dependent Hamiltonian, whose approximate ground state defines the classification rule. Once the ground state has been approximated using a quantum algorithm, inference can be performed either classically using a classical representation of the learned state, or with a quantum representation of the state. We establish efficient methods for gradient estimation and prove favorable generalization guarantees. Furthermore, under standard cryptographic assumptions, we show that Hamiltonian embeddings can realize hypothesis functions beyond the reach of efficient classical algorithms. We then demonstrate the framework at scale on IBM Heron processors using a standard benchmark dataset. Together, these results establish Hamiltonian embeddings as a new framework for quantum machine learning, combining theoretical guarantees with a path for experimental investigations at scale on quantum hardware.

Abstract

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