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

• URL: http://arxiv.org/abs/2603.12540v1
• Date: Fri, 13 Mar 2026 00:48:35 GMT
• Title: Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors
• Abstract summary: Embedded quantum machine learning (EQML) seeks to bring quantum machine learning capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers.<n>In 2026, EQML is technically feasible only in limited and highly experimental forms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Strategic Plan for Neutral Atom Quantum Computation [84.56908019536233]
  We present a strategic plan for neutral atom quantum computation, bringing together hardware development and theory advancements.<n>The concept of practical quantum advantage is defined, along with how to verify claims of advantage, and approaches to designing quantum algorithms that deliver practical advantage.
  arXiv  Detail & Related papers  (2026-07-23T17:36:19Z)
• Quantum Software Engineering in Practice: FPGA and AI Integration for Quantum Certification [36.94429692322632]
  Quantum Software Engineering (QSE) responds to the need for systematic, disciplined, and quantifiable approaches to the development, operation, and maintenance of quantum software.<n>This paper presents QAccCert, a hybrid certification framework developed following QSE principles.<n>The framework implements entanglement certification through CHSH inequality violation in ideal quantum simulations using Qiskit AerSimulator.
  arXiv  Detail & Related papers  (2026-07-08T16:14:07Z)
• Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms [50.573955644831386]
  Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, machine learning and quantum computing.<n>It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing.<n>This survey contributes to the existing literature on QeCV with a holistic review of this research field.
  arXiv  Detail & Related papers  (2025-10-08T17:59:51Z)
• Towards System-Level Quantum-Accelerator Integration [3.4486179803947254]
  We propose a vertically integrated quantum systems architecture that treats quantum accelerators and processing units as peripheral system components.<n>A central element is the Quantum Abstraction Layer (QAL) at operating system kernel level.<n>We present first results towards such an integrated architecture, including a virtual QPU model based on QEMU.
  arXiv  Detail & Related papers  (2025-07-25T12:30:42Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Hardware-level Interfaces for Hybrid Quantum-Classical Computing Systems [0.4697760524661718]
  Hybrid Quantum-Classical computing systems is neither straightforward nor standardized while crucial for unlocking the real potential of QCs.<n>This study focuses on hardware approaches that enable effective hybrid quantum-classical operation.
  arXiv  Detail & Related papers  (2025-03-24T16:43:42Z)
• Bridging Paradigms: Designing for HPC-Quantum Convergence [0.9360388224886863]
  We propose a hardware-agnostic software framework that supports both current intermediate-scale quantum devices and future fault-tolerant quantum computers.<n>The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms.<n>Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, and (4) a comprehensive tool chain for quantum circuit optimization and execution.
  arXiv  Detail & Related papers  (2025-03-03T18:18:45Z)
• Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
  Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
  arXiv  Detail & Related papers  (2024-11-14T12:27:50Z)
• Multi-GPU-Enabled Hybrid Quantum-Classical Workflow in Quantum-HPC Middleware: Applications in Quantum Simulations [1.9922905420195367]
  This study introduces an innovative distribution-aware Quantum-Classical-Quantum architecture. It integrates cutting-edge quantum software framework works with high-performance classical computing resources. It addresses challenges in quantum simulation for materials and condensed matter physics.
  arXiv  Detail & Related papers  (2024-03-09T07:38:45Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [48.491303218786044]
  TeD-Q is an open-source software framework for quantum machine learning.<n>It seamlessly integrates classical machine learning libraries with quantum simulators.<n>It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.