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.
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.
Abstract This practical guide presents methods for constructing noise models using the Qiskit Aer noise module within the IBM Q ecosystem. The objective is to simulate quantum circuits under realistic error conditions, enabling accurate emulation of noisy quantum processors. The guide demonstrates how to: Use the Qiskit Aer noise module to define custom noise models. Incorporate gate errors, readout errors, and decoherence effects into simulations. Apply noise channels (e.g., depolarizing, amplitude damping, phase damping). Attach noise models to quantum circuit simulations. Analyze how noise affects algorithm performance and output distributions. Building noise models is essential for studying the behavior of quantum algorithms in the NISQ (Noisy Intermediate-Scale Quantum) era. It allows researchers to evaluate algorithm robustness, optimize circuit depth, and test error mitigation strategies before execution on real IBM Quantum hardware. This approach provides a bridge between ideal theoretical circuits and physically realistic quantum devices, enhancing the reliability and applicability of quantum computing experiments.
Context: The main obstacle of quantum computing on its way to scalability is the noisy nature of current hardware. Various types of errors lead to incorrect computational results,and measures such as quantum error mitigation are needed to counteract these errors. Many error mitigation techniques require a thorough understanding of the noise and its impact on computations. Therefore, accurate noise models are indispensable to facilitate meaningful quantum computing in the near future. They also enable realistic simulations of quantum devices. Since these devices are currently limited and quantum computing is expensive, researchers typically rely on simulations for testing their algorithms.Objective: In this thesis, we aim to construct a realistic noise model for quantum computing and optimise its model parameters. The model construction should build on the underlying physical processes, and the number of parameters should scale well with the system size. We also address the evaluation and comparison of different noise models by benchmarks using quantum circuits specific to real-world applications. Finally, we aim to develop useful graphical representations of noise models based on the existing quantum circuit model.Method: We present a benchmarking framework for quantum computing noise models and evaluate it based on benchmarking quality attributes from the literature. Moreover, we construct a quantum computing noise model and develop a training procedure for its parameters. We analyse the parameters of the trained noise model and the impact ofdifferent types of errors on computations. We benchmark the noise model with the above approach and compare it to a noise model provided by IBM’s software development kit Qiskit. Finally, we develop an extension of the quantum circuit model to represent noise channels.Result: The benchmarks performed in this thesis show that our noise model predicts noisy hardware behaviour of IBM’s ibmq_manila quantum device equally well as the Qiskit model, if not better, based on quantum circuits commonly used for variational quantum algorithms. Within the trained noise model, readout error has the most detrimental impact on computations. Our benchmarking approach satisfies relevant quality criteria by choosing suitable quantum circuits and objective functions to compare model predictionsto hardware data. Moreover, the quantum circuit model can be extended meaningfully by noise channels.Conclusion: Our benchmarking approach is suitable for evaluating and comparing quantum computing noise models. In future work, volumetric benchmarks should be performed for various noise models and application contexts. The parameter optimisation based on learning with training circuits yields accurate noise models and shows the importance of mitigating measurement error. Different optimisation algorithms and quantum circuits should be explored, and more types of noise should be incorporated into the noise model.
This paper explains the roadmap for running quantum circuit programs based on the Qiskit library in quantum simulators as well as real cloud-based IBM quantum computers. Qiskit is a free and open-source software development platform based on the Python programming language that is used in quantum programming. Qiskit acts as a link between quantum computing’s theoretical foundations and the practical aspects of programming and experimentation. It also allows users to experiment with and develop quantum algorithms, as well as simulate and execute them on simulators and real-world cloud-based quantum devices. It also simplifies the quantum programming process and allows a diverse range of people to participate in the exciting world of quantum computing. The paper, on the other hand, provides the mathematical foundation for analyzing quantum circuits and algorithms using linear algebra principles, as they provide the tools needed to describe and manipulate quantum states and operations. Furthermore, the paper shows quantum circuit design and implementation using real Qiskit codes.
Abstract This practical guide provides a detailed introduction to quantum logic gates and unitary operators as implemented on the IBM Q platform. It focuses on the fundamental gate structures used in quantum programming, particularly within the Composer interface, and provides screenshots and detailed descriptions of each operator’s functionality. The guide emphasizes the operational role of gates in controlling qubit states and constructing quantum circuits. To enhance understanding, the behavior of gates is visualized on the Bloch sphere, demonstrating how single-qubit operations rotate qubit states around the XXX, YYY, and ZZZ axes, as well as how phase shifts and amplitude adjustments modify the qubit. These operations are essential for measurement, state preparation, and precise manipulation of quantum systems. The relevance of this study is underscored by the rapid development of quantum technologies and the increasing accessibility of quantum computing platforms like IBM Q. A thorough understanding of quantum gates and their implementations is critical for advancing quantum algorithm design and for training specialists capable of deploying quantum computational solutions in fields ranging from cryptography to complex physical system simulations. This guide provides both theoretical insights and practical exercises, bridging the gap between abstract quantum mechanics and applied quantum programming, thereby equipping readers with foundational skills for effective circuit construction and quantum computation.
Abstract This practical guide presents hands-on laboratory exercises on variational quantum algorithms (VQAs) and hybrid quantum-classical workflows, based on the IBM Q learning platform. The work was performed by the author on April 28, 2024, using IBM Q cloud resources. Variational algorithms leverage the variational principle of quantum mechanics to optimize quantum circuits for specific tasks, making them particularly suitable for current noisy intermediate-scale quantum (NISQ) devices and offering a practical route toward achieving quantum advantage. The guide explores the full workflow for designing and implementing variational algorithms, highlighting the trade-offs inherent at each stage, including circuit design, parameter initialization, optimization strategies, and measurement post-processing. It also demonstrates the use of Qiskit Runtime primitives to enhance both the speed and accuracy of computations. Through these exercises, participants gain experience in constructing hybrid quantum-classical algorithms, performing parameter optimization, and interpreting results within the context of near-term quantum devices. While this guide focuses on practical implementation, it also encourages exploration of the underlying theoretical foundations of quantum information and computation, connecting applied algorithmic practice with fundamental principles. The course and exercises provide a structured starting point for researchers, developers, and students aiming to explore the utility of quantum computers for real-world problem solving, particularly in optimization, quantum chemistry, and machine learning applications.
Abstract This practical guide introduces hands-on quantum computing, based on IBM Q learning resources. The exercises were completed by the author on April 28, 2024, using IBM Q cloud resources. The course provides a practical framework for understanding and experimenting with modern quantum computers, covering realistic use cases, circuit execution, and best practices for operating quantum processors with 100 or more qubits. Participants explore the concept of quantum usefulness, which refers to the practical utility of quantum computers in solving real-world problems, distinguishing it from quantum advantage, which is the point where quantum systems outperform classical computers on meaningful tasks. The course emphasizes that while classical computers remain highly powerful and adaptive, the evolving capabilities of quantum processors offer unique opportunities in computation that can complement classical methods. Hands-on exercises guide participants through practical aspects of designing and executing quantum circuits, running experiments on simulators and cloud-accessible quantum hardware, and interpreting results. This approach bridges theoretical knowledge with applied skills, enabling students and researchers to explore the current potential and limitations of quantum computing. The guide also contextualizes quantum computing within decades of classical computational development, highlighting how quantum devices may transform computational paradigms in fields such as optimization, chemistry, cryptography, and machine learning.