[2604.04414] Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks

Computer Science > Emerging Technologies arXiv:2604.04414 (cs) [Submitted on 6 Apr 2026] Title:Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks Authors:Poornima Kumaresan, Shwetha Singaravelu, Lakshmi Rajendran, Santhosh Sivasubramani View a PDF of the paper titled Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks, by Poornima Kumaresan and 2 other authors View PDF HTML (experimental) Abstract:Quantum machine learning (QML) stands at the intersection of quantum computing and artificial intelligence, offering the potential to solve problems that remain intractable for classical methods. However, the current landscape of QML software frameworks suffers from severe fragmentation: models developed in TensorFlow Quantum cannot execute on PennyLane backends, circuits authored in Qiskit Machine Learning cannot be deployed to Amazon Braket hardware, and researchers who invest in one ecosystem face prohibitive switching costs when migrating to another. This vendor lock-in impedes reproducibility, limits hardware access, and slows the pace of scientific discovery. In this paper, we present a framework-agnostic quantum neural network (QNN) architecture that abstracts away vendor-specific interfaces through a unified computational graph, a hardware abstraction layer (HAL), and a multi-framework export pipeline. The core architecture supports simultaneous integration with TensorFlow, PyTorch, and J...