Quantum computing frameworks like PennyLane, Qiskit, and others offer powerful tools for developing quantum algorithms. However, integrating their devices can be challenging for newcomers, especially given the frequent updates in frameworks like Qiskit, which can create significant barriers to learning and thus slow down the growth of quantum computing and quantum machine learning. The PennyLane–Qiskit plugin is a particularly interesting framework that allows researchers and practitioners interested in quantum computing and quantum machine learning to implement and simulate realistic models in a simple and efficient way. This work provides a practical method to use the PennyLane–Qiskit plugin, focusing on three main device types: the Aer simulator with noise models, IBM fake backends (with FakeManilaV2 as a representative example), and real IBM Quantum hardware accessed remotely (with ibm_torino used in this study). We demonstrate how to set up and use each of these devices within PennyLane using a simple example of a quantum machine learning regression task on simulated stock price data. Our goal is to simplify the process for all interested users, especially (but not only) beginners by clearly explaining and demonstrating how to use each of these device options while highlighting best practices for effectively leveraging PennyLane and Qiskit together via Pennylane–Qiskit plugin. This work aims to provide a turnkey solution enabling users with only minimal foundational knowledge to quickly start running meaningful simulations. The three devices implementation will be explained with the code here. The accompanying full code will be provided to ensure that all results can be easily reproduced, including with users’ own datasets.
Quantum computing frameworks like PennyLane, Qiskit, and others offer powerful tools for developing quantum algorithms. However, integrating their devices can be challenging for newcomers, especially given the frequent updates in frameworks like Qiskit, which can create significant barriers to learning and thus slow down the growth of quantum computing and quantum machine learning. The PennyLane–Qiskit plugin is a particularly interesting framework that allows researchers and practitioners interested in quantum computing and quantum machine learning to implement and simulate realistic models in a simple and efficient way. This work provides a practical method to use the PennyLane–Qiskit plugin, focusing on three main device types: the Aer simulator with noise models, IBM fake backends (with FakeManilaV2 as a representative example), and real IBM Quantum hardware accessed remotely (with ibm_torino used in this study). We demonstrate how to set up and use each of these devices within PennyLane using a simple example of a quantum machine learning regression task on simulated stock price data. Our goal is to simplify the process for all interested users, especially (but not only) beginners by clearly explaining and demonstrating how to use each of these device options while highlighting best practices for effectively leveraging PennyLane and Qiskit together via Pennylane–Qiskit plugin. This work aims to provide a turnkey solution enabling users with only minimal foundational knowledge to quickly start running meaningful simulations. The three devices implementation will be explained with the code here. The accompanying full code will be provided to ensure that all results can be easily reproduced, including with users’ own datasets.
Quantum machine learning (QML) has emerged as a promising field, combining the power of quantum computing with classical machine learning techniques to solve complex computational tasks. As the demand for efficient quantum simulations grows, multiple QML frameworks, including PennyLane, Qiskit, and TensorFlow Quantum (TFQ), have been developed to facilitate hybrid quantum-classical computations. This study aims to evaluate and compare the performance of three leading QML frameworks PennyLane, Qiskit, and TensorFlow Quantum in simulating quantum machine learning models, focusing on accuracy, execution time, and noise tolerance. The study examined three leading quantum machine learning (QML) frameworks PennyLane (v0.24), Qiskit (v0.43.2), and TensorFlow Quantum (TFQ, v0.7) each tested on a 64-bit Ubuntu Linux system with an Intel Core i7 processor, 32 GB RAM, and NVIDIA GeForce RTX 3080 GPU. Quantum algorithms like variational quantum circuits (VQCs) and quantum support vector machines (QSVMs) were simulated using these frameworks. Various quantum gates, including Pauli-X, Y, Z, Hadamard, CNOT, and Rotation gates (Rx, Ry, Rz), were used for parameter optimization and quantum superposition. The Iris and MNIST datasets were adapted for quantum encoding using Amplitude and Angle Encoding for binary classification tasks. Simulations were run on classical computers, while select circuits were executed on IBM Quantum’s Falcon r5.11 processor to compare hardware performance. PennyLane achieved the highest accuracy (92%) in quantum simulations, excelling in hybrid quantum-classical model integration. TensorFlow Quantum provided the fastest execution time, especially in classical simulations, making it suitable for rapid prototyping. Qiskit produced deeper quantum circuits but had higher error rates (6%) on real quantum hardware, highlighting noise and decoherence effects. The VQC models optimized by stochastic gradient descent (SGD) showed significant improvements in model performance, and QSVMs effectively classified both linearly separable and non-separable datasets, demonstrating the potential of quantum algorithms over classical approaches. Statistical analysis confirmed the significance of the results (p < 0.05). PennyLane is the most robust framework for hybrid quantum-classical models, while TensorFlow Quantum is suited for rapid prototyping. Qiskit, despite longer execution times, excels in hardware implementation. The choice of framework should align with the specific needs of quantum machine learning tasks.
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.
Among notable applications, quantum machine learning (QML) is a promising task that combines quantum computing with machine learning, cited for various types of complex computational problems. This chapter briefly summarizes the frameworks, software platforms, and toolchains currently available for implementing, simulating, and executing hybrid quantum–classical learning algorithms. Major frameworks (e.g., Qiskit Machine Learning, PennyLane, TensorFlow Quantum, and the PyTorch-based toolsets) are analyzed in detail based on their architectural design, functionality, and integration capabilities. They have several key features, such as variational quantum circuits, parameterized quantum gates, automatic differentiation capabilities, and easy integration into a classical deep learning ecosystem. The chapter groups together ongoing research in different architectural paradigms, practical usability aspects, and empirical performance tests. Additionally, it explores aspects of scalability, adaptability to hardware, noise resistance, and reproducibility. The results bring to light elements of the design of trade-offs for creating robust and efficient toolchains for QML, a QML best practice, and guidance for future QML design.
Hybrid quantum-classical machine learning represents a frontier in computational research, combining the potential advantages of quantum computing with established classical optimization techniques. PennyLane provides a Python framework that seamlessly bridges quantum circuits and classical machine learning, enabling researchers to build, optimize, and deploy variational quantum algorithms. This paper introduces PennyLane as a versatile tool for quantum machine learning, optimization, and quantum chemistry applications. We demonstrate use cases including quantum kernel methods, variational quantum eigensolvers, portfolio optimization, and integration with classical ML frameworks such as PyTorch, TensorFlow, and JAX. Through concrete Python examples with widely used libraries such as scikit-learn, pandas, and matplotlib, we show how PennyLane facilitates efficient quantum circuit construction, automatic differentiation, and hybrid optimization workflows. By situating PennyLane within the broader context of quantum computing and machine learning, we highlight its role as a methodological building block for quantum-enhanced data science. Our goal is to provide researchers and practitioners with a concise reference that bridges foundational quantum computing concepts and applied machine learning practice, making PennyLane a default citation for hybrid quantum-classical workflows in Python-based research.
QML is a budding interdisciplinary arena that associates quantum computing and regular machine learning methods to progress computational efficacy and provide solutions for complex problems. With the fast development of quantum technologies, QML has received a lot of devotion for its high possible applications in AI, cybersecurity, healthcare, and financial modeling. This review covers all QML frameworks, with IBM Qiskit, Google Cirq, PennyLane, TensorFlow Quantum, and Microsoft Q\#. We thoroughly classify current QML algorithms, including QSVM, QNN, VQC, and Quantum Boltzmann Machines (QBM), highlighting their advantages and some restrictions. Additionally, we explore significant barriers to extensive QML acceptance, like hardware constraints, scalability matters, quantum noise, and the need for explainability. We also deliberate auspicious research directions, which contain hybrid quantum-classical learning models, quantum federated learning, and QML framework consistency. This article wants to serve as an introductory reference for researchers and experts interested in the improvement of QML by providing a properly planned review of existing work and recognizing future opportunities.
Abstract This practical guide explores the use of IBM Quantum cloud simulators for modeling and analyzing quantum circuits under realistic conditions. Using the IBM Q platform and Qiskit Runtime, the work demonstrates how to configure the ibmq_qasm_simulator and map a basic noise model derived from a physical IBM Quantum™ device. The guide focuses on: Setting up the ibmq_qasm_simulator in Qiskit. Extracting and applying a hardware-based noise model from a real IBM Quantum backend. Performing noisy simulations of QuantumCircuits. Using Sampler and Estimator primitives in Qiskit Runtime to analyze circuit outputs. Studying the impact of realistic quantum errors such as decoherence, gate infidelity, and readout noise. By incorporating noise models into simulations, researchers and developers can better understand how quantum circuits behave on real hardware, optimize algorithms before deployment, and design more robust quantum programs. This approach bridges the gap between idealized simulations and physical quantum devices, making it a critical step toward practical quantum application development.