Eliminating vendor lock-in in quantum machine learning via framework-agnostic neural networks

Introduction Quantum machine learning (QML) sits at the intersection of quantum computing and artificial intelligence and offers the potential to solve problems that remain intractable for classical methods. However, the current landscape of QML software frameworks is severely fragmented: 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 scientific discovery. Methods 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 JAX as classical co‐processors, while the HAL provides transparent access to IBM Quantum, Amazon Braket, Azure Quantum, IonQ, and Rigetti backends through a single application programming interface (API). We introduce three pluggable data encoding strategies (amplitude, angle, and instantaneous quantum polynomial encoding) that are compatible with all supported backends, and an export module that leverages Open Neural Network Exchange (ONNX) metadata to enable lossless circuit translation across Qiskit, Cirq, PennyLane, and Braket representations. Results We benchmark the framework on the Iris, Wine, and MNIST‐4 classification tasks, demonstrating training‐time parity (1% to 7% overhead) relative to native framework implementations while achieving statistically indistinguishable classification accuracy. Hardware validation on IBM Heron r2 processors (up to 156 superconducting qubits) confirms that parameter‐shift gradients computed through the HAL agree with simulator predictions within noise margins. Discussion By removing the obligation to commit irrevocably to a single vendor ecosystem, the framework addresses the single largest non‐technical barrier to QML adoption and establishes a reference architecture for interoperable quantum software.