This paper represents an accessible overview of quantum program development with IBM's Qiskit. Current quantum computers are still making a lot of mistakes since we are in the NISQ era, where noise is the main problem. Qiskit developers are assisted by means of which they can readily plan their circuits and then the versions which are to be run on the actual hardware are automatically created by the Transpiler. Simulation of the circuit along with the check of how the noise can change the result is done by Qiskit Aer before the program is sent to the real quantum computer. As noise is the cause of the major problem, there is also the provision of error-reduction methods like Readout Error Mitigation and Zero Noise Extrapolation (ZNE) in Qiskit to eliminate the noise. Hybrid algorithms such as VQE and QAOA are increasingly becoming instrumental in solving real problems in chemistry, finance, and machine learning, the paper says, too. The study sums up that Qiskit has become the most popular open-source platform that is leading the way to the next quantum computing era and making the quantum advantage feasible
This paper represents an accessible overview of quantum program development with IBM's Qiskit. Current quantum computers are still making a lot of mistakes since we are in the NISQ era, where noise is the main problem. Qiskit developers are assisted by means of which they can readily plan their circuits and then the versions which are to be run on the actual hardware are automatically created by the Transpiler. Simulation of the circuit along with the check of how the noise can change the result is done by Qiskit Aer before the program is sent to the real quantum computer. As noise is the cause of the major problem, there is also the provision of error-reduction methods like Readout Error Mitigation and Zero Noise Extrapolation (ZNE) in Qiskit to eliminate the noise. Hybrid algorithms such as VQE and QAOA are increasingly becoming instrumental in solving real problems in chemistry, finance, and machine learning, the paper says, too. The study sums up that Qiskit has become the most popular open-source platform that is leading the way to the next quantum computing era and making the quantum advantage feasible
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.
Abstract Quantum computing has recently emerged as a novel paradigm for solving problems otherwise intractable on classical computers, yet the practical progress of quantum computers is severely impacted by noise, decoherence, and imperfect gate operations in Noisy Intermediate-Scale Quantum (NISQ) devices. Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results.This study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time.To achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices.Initial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading.To sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important.
Quantum noise poses a significant challenge for current near-term quantum computing. Quantum error mitigation (QEM) has therefore emerged as a key strategy, offering a practical and effective solution to reduce error impacts and enhance the performance of near-term variational quantum circuits (VQC). Given the lack of a comprehensive survey on this important topic in the literature, this article provides a dedicated overview of QEM, including both during and after the training of VQC. Specifically, during VQC training, we explore and discuss key QEM techniques such as optimal control and dynamical decoupling. For the post-processing stage of VQC, we will examine and discuss mitigation techniques such as zero-noise extrapolation (ZNE) and probabilistic error cancellation (PEC). For each of these QEM techniques, we will investigate the fundamentals, mitigation concepts, and recent advances. Subsequently, we also explore research toolboxes, including Mitiq and Qiskit Aer, as well as provide a case study that demonstrates contextual multi-armed bandit-guided ZNE. Finally, we discuss ongoing problems and future research initiatives, including ML-assisted mitigation and integration with error correction. This survey article aims to synthesize the state-of-the-art in QEM, offer organized insights across approaches, and propose potential paths towards error-resilient quantum computing.
The maturation of quantum computing from theoretical physics to engineering reality presents a complex landscape for stakeholders navigating the transition from Noisy Intermediate-Scale Quantum (NISQ) devices toward Fault-Tolerant Quantum Computation (FTQC). This report provides a comprehensive, technically rigorous analysis of the current quantum ecosystem, examining the hardware mosaic of leading qubit modalities—superconducting circuits, trapped ions, neutral atoms, and silicon spin qubits—and evaluating them against operational metrics, including coherence times, gate fidelities, and cryogenic infrastructure requirements. We dissect the full software stack, differentiating the near-term utility of error mitigation techniques such as Zero-Noise Extrapolation (ZNE) from the long-term necessity of Quantum Error Correction (QEC) and the milestone of logical qubit break-even. The analysis benchmarks hybrid algorithms (VQE, QAOA) and circuit knitting strategies against current cloud-accessible hardware performance, while also addressing the imminent non-computational impact of PostQuantum Cryptography (PQC) migration. Experimental data synthesised from leading vendor roadmaps and pre-print literature indicate that superconducting platforms currently lead in gate speed (nanosecond regime) but face crosstalk scaling challenges beyond 1,000 qubits. In contrast, trapped-ion and neutral-atom architectures demonstrate superior connectivity and coherence. The report concludes with a grounded, evidence-based five-year projection, identifying high-value vertical use cases in pharmaceuticals and finance, and providing a strategic framework for enterprises to separate quantum hype from tangible commercial opportunity. Future research directions include the realisation of low-overhead Quantum LDPC codes and the development of modular photonic interconnects to scale beyond single-chip architectures.
Quantum computing is being increasingly adopted for solving classically intractable problems across various domains. However, the availability of accessible and scalable software frameworks remains essential for practical experimentation and adoption. IBM's open-source quantum computing toolkit 'Qiskit' has become a key player in this space by offering tools for circuit design, simulation, hardware execution, and domain-specific applications. This survey provides a systematic review of how Qiskit has evolved and what it has contributed to several critical fields including cryptography and cybersecurity, image and signal processing, climate science and energy applications, and finance. We show how Qiskit facilitates hybrid classical-quantum workflows and enables the deployment of algorithms on physical quantum hardware through error mitigation and modular integration approaches. Our exploration covers several key applications, including quantum key distribution, climate simulation, and quantum-enhanced portfolio optimization, while providing practical insights into their implementation. This work also covers the framework's technical structure and current limitations associated with scalability and reproducibility. By bringing together developments that have been scattered across different areas, this work serves as a reference point for researchers and practitioners who want to understand or contribute to Qiskit-enabled quantum computing.
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.