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

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 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. An export module leveraging Open Neural Network Exchange (ONNX) metadata enables lossless circuit translation across Qiskit, Cirq, PennyLane, and Braket representations. We benchmark our framework on the Iris, Wine, and MNIST-4 classification tasks, demonstrating training time parity (within 8\% overhead) compared to native framework implementations, while achieving identical classification accuracy.

• The paper presents a new QNN architecture that abstracts software frameworks and quantum hardware to eliminate vendor lock-in.
• It integrates multi-framework support, a hardware abstraction layer, and universal encoding strategies to ensure high-fidelity model translation.
• Empirical benchmarks show minimal overhead and machine-precision fidelity across diverse quantum platforms, supporting reproducible research.

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

Introduction and Motivation

The current quantum machine learning (QML) ecosystem is severely fragmented, with researchers facing significant barriers when attempting to migrate quantum models across different frameworks or quantum hardware backends. In practice, models developed with TensorFlow Quantum (TFQ) are incompatible with PennyLane or Qiskit Machine Learning, and circuits authored in one vendor's format cannot be readily executed on other providers’ quantum hardware. This vendor lock-in impedes reproducibility, constrains hardware accessibility, and skews scientific benchmarking due to non-algorithmic performance differences. The paper proposes a comprehensive solution: a framework-agnostic quantum neural network (QNN) architecture that abstracts vendor-specific interfaces at both the software framework and quantum hardware layers, thus enabling seamless model interoperability and cross-hardware benchmarking.

Architectural Contributions

Multi-Framework Integration

The core abstraction is a vendor-independent QuantumLayer, defined as a directed acyclic graph (DAG) of quantum gates, parameterizations, and measurement operators. Rather than depending on any framework-specific quantum circuit representation, this architectural choice decouples the QNN definition from underlying libraries. Framework adapters are developed for TensorFlow, PyTorch, and JAX, each translating the QuantumLayer’s parameter tensors and gradient signals into the host framework’s automatic differentiation pipeline. Parameter-shift, finite-difference, and adjoint differentiation strategies are supported natively, and seamless batched quantum circuit evaluation is provided, aligning with classical ML workflow expectations.

Hardware Abstraction Layer (HAL)

A hardware abstraction layer (HAL) is designed to unify access to major quantum hardware platforms including IBM Quantum, Amazon Braket, Azure Quantum, IonQ, and Rigetti. The HAL performs circuit transpilation that guarantees unitary equivalence (up to a global phase) between the internal vendor-independent representation and each target’s native gate set, handling differences in topology, allowed gates, and qubit connectivity. Backend discovery, credential management, and automatic resource routing based on scores optimized for fidelity, connectivity, and queue time are all managed by the HAL, making quantum resource selection both optimal and transparent.

Universal Data Encoding Strategies

Three pluggable classical-to-quantum data encoding routines are offered: amplitude encoding (logarithmic qubit use yet high gate count), angle encoding (linear qubit scaling, shallow depth), and IQP encoding (feature-dependent entanglement suitable for kernel-based QML). Each encoding is verified through statevector simulation to be consistent across all transpiled backend targets, with round-trip Frobenius-norm fidelity bounded by a tight threshold.

Model Export and Interoperability via ONNX

Leveraging and extending the ONNX (Open Neural Network Exchange) format, the framework supports lossless serialization and translation of QNNs—including parameters, circuit topologies, encoding configurations, and measurement specifications—into the native models for Qiskit, Cirq, PennyLane, and Braket. Custom ONNX operators for quantum circuits ensure the hybrid quantum-classical graph is portable without fidelity loss across frameworks.

Experimental Results

Benchmark Performance

The system is empirically benchmarked on the canonical QML datasets: Iris, Wine (PCA-reduced), and a reduced-dimensionality MNIST-4. Accuracy parity—with differences well below the inter-run stochastic variation—is demonstrated relative to native TFQ, PennyLane, and Qiskit implementations. Training time overhead is tightly bounded (1–8%), with most overhead arising from parameter and gradient conversions, not quantum evaluation, and the JAX adapter achieving the least overhead due to robust functional transformation alignment.

Hardware Validation and Cross-Vendor Consistency

On real hardware (IBM Eagle r3/Brisbane, IBM Heron r2/ibm_fez, Rigetti Ankaa-3, IonQ Forte-1), parameter-shift gradients calculated via the HAL agree with simulation to within hardware noise margins. Rigorous error decomposition confirms the observed gradient discrepancies are within predicted shot, gate, and measurement noise bounds; the rare outlier is shown to result from a transient, device-specific calibration error, not a framework or HAL error. Cross-backend simulator results demonstrate machine-precision agreement, and on hardware, mean absolute gradient error (MAE) is $< 0.006$ for all superconducting and ion-trap devices evaluated.

Equivalence and Fidelity

The round-trip model translation, from one framework to another via ONNX, achieves output probability fidelity $\mathcal{F}_{\text{RT}} > 0.9999$; the residual is attributable only to floating-point disparities between underlying linear algebra engines. Batch circuit execution and all encoding strategies are confirmed to yield identical quantum states after transpilation.

Implications for Reproducibility and Benchmarking

This architecture directly addresses the predominant non-technical barrier to QML progress: the obligation to deeply invest in one vendor’s stack, with high switching costs and non-reproducibility risk. By decoupling model definition from both classical and quantum runtime specifics, the work establishes a reference architecture for reproducible cross-platform quantum machine learning. Benchmark comparisons across frameworks and hardware are now valid—differences can be confidently attributed to algorithmic or physical effects, not framework artefacts. Furthermore, institutions can hedge procurement decisions and training investments against hardware and framework churn, future-proofing their QML infrastructure.

Limitations and Future Directions

While the abstraction overhead is minimal in the NISQ regime (where QPU evaluation dominates end-to-end runtime), real-time applications or edge quantum workloads may find even the slight penalty non-negligible. The value-add of a custom HAL may decrease as the community converges on standard quantum APIs, such as those being advanced by the QIR Alliance. Support for dynamic quantum circuits, richer quantum operations, error correction, and automated encoding selection algorithms are identified as avenues worthy of further work.

Conclusion

The framework achieves complete decoupling of QML models from both classical ML and quantum execution environments, eliminating vendor lock-in and enabling rigorous, reproducible machine learning research in the quantum domain. With open cross-platform export and hardware-agnostic model deployment, the methodology paves the way for scalable, credible empirical quantum machine learning research. The work also demonstrates that careful architectural abstraction, when engineered with transparent circuit transpilation and round-trip fidelity guarantees, can resolve the most pressing interoperability constraints facing the QML field.