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Qiskit
DevelopersIBM Research, Qiskit community
Initial releaseMarch 7, 2017; 8 years ago (2017-03-07).[1]
Stable release
2.0.2 / 27 May 2025; 5 months ago (2025-05-27)[2]
Repository
Written inPython
Operating systemCross-platform
TypeSDK for Quantum Computing
LicenseApache License 2.0[3]
Websiteqiskit.org

Qiskit (Quantum Information Software Kit) is an open-source, Python-based, high-performance software stack for quantum computing, originally developed by IBM Research and first released in 2017. It provides tools for creating quantum programs (by defining quantum circuits and operations) and executing them on quantum computers or classical simulators. The name "Qiskit" refers broadly to a collection of quantum software tools. It is centered around the core Qiskit SDK, and combined with a suite of tools and services for quantum computation, like the Qiskit Runtime service that enables optimized computations through the cloud. Qiskit allows users to write quantum circuits and execute them on real quantum processors (such as superconducting qubit systems) or on various other compatible quantum devices. Over time, Qiskit’s capabilities have expanded with new components and improvements, such as Qiskit Functions and Qiskit addons, while maintaining an open-source ecosystem for quantum computing research and application development.

Components

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Qiskit SDK

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The Qiskit SDK is the core software development kit for working with quantum computers at the level of extended (static, dynamic, and scheduled) quantum circuits, operators, and primitives. Distributed as the Python package qiskit, it is an open‑source framework that allows users to construct quantum circuits, manipulate quantum operators, and interface with quantum hardware or simulators. The Qiskit SDK is the foundational component of the Qiskit software stack – it provides the largest set of features and acts as the base upon which other Qiskit services and modules are built.

Key features of the Qiskit SDK include modules for building quantum circuits and registering quantum operations, a library of predefined quantum logic gates and parameterized circuits, a quantum information module for working with quantum states and operators, and a transpiler that optimizes and converts circuits to run on specific quantum hardware backends. For example, Qiskit allows users to create quantum circuits using an intuitive Python API, then employ the transpiler to adapt and optimize these circuits for a given device’s topology and constraints (reducing gate counts or circuit depth as needed). The SDK also provides tools for noise modeling and supports primitives – basic quantum program components (such as samplers and estimators for circuit outcomes) – which serve as building blocks for higher‑level quantum algorithms and can be executed using local simulators or cloud services. Together, these features make the Qiskit SDK a comprehensive platform for developing quantum algorithms and experiments in a hardware‑agnostic manner.

Qiskit Runtime

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Qiskit Runtime is a cloud‑based quantum execution service introduced by IBM to streamline running quantum computations on IBM Quantum systems. It allows users to run quantum programs with additional server‑side management, improving performance by minimizing the latency of classical‑quantum communication and leveraging advanced quantum error mitigation techniques. In essence, Qiskit Runtime provides an optimized environment on IBM’s cloud where quantum circuits (or higher‑level primitive programs) are executed closer to the quantum hardware, thereby reducing latency and enabling more complex workflows than the traditional request‑response method of job submission. This service is designed to make quantum program execution more efficient and scalable, especially for algorithms that involve repeated circuit evaluations or iterative processes.

Qiskit Runtime utilizes additional classical and quantum computing resources to incorporate error‑mitigation strategies that increase the quality and accuracy of the results obtained from quantum processors. For example, Qiskit Runtime can automatically apply techniques like dynamical decoupling (to suppress noise during circuit execution) and zero-noise extrapolation or other forms of readout error mitigation (to reduce the effect of measurement errors), aiming to return higher‑quality outcomes from noisy quantum hardware. The runtime service also supports multiple execution modes for running quantum jobs, each suited to different use cases. Users can execute a single job (a one‑off circuit or primitive call with a specified number of shots), create a session to run a series of jobs interactively with lower overhead between them, or submit jobs in batch mode for parallel execution in the queue. These modes allow flexibility in managing experiments – for instance, session mode enables iterative algorithms that adapt based on intermediate results, while batch mode is useful for running many independent circuits simultaneously.

Qiskit Serverless

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Qiskit Serverless is an extension of the Qiskit ecosystem that enables running quantum–classical workloads in a distributed, cloud-native fashion. It provides a simple interface to manage compute resources and execute portions of a quantum workflow on remote servers, effectively allowing users to deploy quantum programs and their accompanying classical processing steps to the IBM Quantum cloud or even across multiple cloud environments. The goal of Qiskit Serverless is to facilitate utility-scale quantum applications by handling the allocation of both quantum processing units and classical computing resources as needed, which is especially useful for hybrid algorithms and large‑scale experiments. Qiskit Serverless can be used to:

  • Parallelize classical tasks that are part of a quantum workflow, such as pre‑processing of input data or post‑processing of quantum results. By distributing these classical computations to cloud resources, users can speed up overall execution when working with many circuits or large datasets in tandem with quantum hardware.
  • Persist long-running workloads in the cloud, allowing quantum jobs to continue executing and collecting results even if the user’s local environment (e.g., a laptop) is offline. This means a researcher can start a complex quantum experiment and not worry about maintaining a constant connection; the serverless system will handle the execution and retain the results until the user retrieves them.
  • Deploy reusable programs (quantum applications or algorithmic routines) on the IBM Quantum Platform to invoke as needed. This turns custom quantum algorithms into services, enabling collaboration and reuse: for example, an optimization algorithm or a chemistry simulation routine could be uploaded once and then repeatedly executed with different parameters on demand.

By abstracting away the details of managing cloud resources, Qiskit Serverless aims to make it easier to scale up quantum experiments and integrate them into larger computing workflows.

Qiskit Add-ons

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Qiskit Add-ons (stylized Qiskit addons) are modular, separately‑installed tools designed to extend Qiskit’s capabilities for quantum algorithm development. They build on Qiskit’s core framework and can be plugged into the user’s workflow to help scale or design new algorithms. Each add‑on is distributed as an independent Python package (for example, qiskit-addon-sqd for the SQD tool) that can be installed via the Python Package Index (PyPI) and then used alongside Qiskit’s SDK. These official add-ons enhance tasks like circuit mapping, optimization, and result post‑processing while integrating seamlessly with Qiskit’s functionalities.

  • Approximate Quantum Compilation (AQC) – qiskit-addon-aqc-tensor. AQC uses tensor‑network methods to compress a segment of a quantum circuit into a shorter equivalent circuit with high fidelity.
  • Multi‑Product Formulas (MPF) – qiskit-addon-mpf. MPF reduces Trotter error in Hamiltonian simulations by combining the results of multiple circuit executions in a weighted formula.
  • Operator Backpropagation (OBP) – qiskit-addon-obp. OBP shortens circuit depth by trimming off the final set of gate operations and compensating through additional measurements on the remaining circuit.
  • Sample‑based Quantum Diagonalization (SQD) – qiskit-addon-sqd. SQD is a post‑processing tool that classically analyzes bitstring samples from quantum circuit runs to estimate eigenvalues and eigenvectors of large operators (such as molecular Hamiltonians) more accurately under noise.

Each of these add-ons is maintained as an open-source extension in the GitHub Qiskit organization, with documentation and tutorials provided by IBM Quantum. This modular approach allows researchers to opt into advanced algorithmic techniques as needed, keeping Qiskit’s core lightweight while still enabling cutting-edge features through add-ons.

Qiskit Ecosystem

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The Qiskit ecosystem refers to the broader collection of open-source projects that interface with Qiskit without being part of its core SDK. These projects, developed by IBM Quantum or the wider community, provide additional functionality and specialized tools that complement the Qiskit framework. By designing Qiskit with a modular and extensible architecture, IBM has enabled external packages to integrate easily and extend its capabilities in areas like simulation, job management, and error mitigation. This ecosystem enhances the usability of Qiskit by addressing tasks that lie beyond the scope of the base library, allowing users to tailor their quantum computing workflows with community‑driven solutions. Some notable projects in the Qiskit ecosystem include:

  • Qiskit Aer (qiskit-aer) – A high‑performance simulator for quantum circuits, featuring multiple simulation backends and support for realistic noise models. It is maintained by IBM Quantum as the official simulation engine for Qiskit and is frequently used to validate algorithms before running on actual quantum processors.
  • qBraid SDK (qbraid) – A platform‑agnostic quantum runtime and cloud management framework that streamlines the full lifecycle of quantum jobs. Developed and maintained by the company qBraid, this tool abstracts away much of the complexity in managing quantum computing workloads across different providers, complementing Qiskit’s development tools with cloud deployment capabilities.
  • mthree (mthree) – A library for Matrix‑free Measurement Mitigation (M3) that improves the accuracy of qubit measurement outcomes on real hardware. This IBM Quantum‑developed package tackles readout errors by constructing a reduced model of the noise in the measurement process and then solving for corrected outcome probabilities without needing to invert large calibration matrices.

Through such ecosystem projects, Qiskit users can access extended functionality that is not included in the core SDK, thereby enhancing research and application development. For example, Aer enables thorough testing of algorithms in simulation, qBraid facilitates running Qiskit programs on diverse cloud hardware, and mthree helps in obtaining more accurate experimental data. A comprehensive catalog of Qiskit ecosystem projects is available on the Qiskit Ecosystem page, where users can discover other tools and libraries (and even contribute their own) that work in conjunction with Qiskit. All these ecosystem efforts contribute to a more versatile and robust quantum computing toolkit centered around Qiskit.

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Qiskit

View on Grokipedia
from Grokipedia
Qiskit is an open-source software development kit (SDK) for quantum computing, enabling users to build, optimize, and execute quantum circuits, algorithms, and applications on both simulators and real quantum hardware. Developed primarily by IBM in collaboration with an active global open-source community, it provides a modular, extensible framework that supports quantum research across domains such as algorithms, high-performance simulation, and quantum information science. As of 2024, Qiskit has achieved widespread adoption, with over 13 million downloads and preferred by 74% of quantum developers according to a Unitary Foundation survey. Originally released on March 7, 2017, Qiskit has evolved through iterative updates, culminating in the stable Qiskit 1.0 release on February 15, 2024, which marked a milestone for production-ready quantum programming with enhanced stability and performance. As of November 2025, the latest stable version is 2.2.3, released on October 30, 2025, featuring improvements in compiler efficiency, such as 83 times faster transpilation compared to TKET and 29% fewer two-qubit gates in circuit optimization. Qiskit's development emphasizes backend-agnostic compatibility, allowing seamless integration with quantum hardware from providers like IBM Quantum, IonQ, and Amazon Braket. Key components of Qiskit include the core Qiskit SDK for circuit construction and execution, Qiskit Aer for high-performance noise-aware simulation, and ecosystem extensions such as Qiskit Algorithms for variational and methods, Qiskit Nature for solving problems, and Qiskit Machine Learning for hybrid quantum-classical models. Additional tools like Qiskit Metal support superconducting quantum hardware design, while Qiskit Runtime facilitates scalable cloud-based execution of quantum primitives. With over 7,000 dependent projects, Qiskit serves as a foundational platform for advancing quantum utility and real-world applications.

Overview

Definition and Purpose

Qiskit is an open-source, Python-based (SDK) developed by for quantum information science and engineering. It serves as a comprehensive software stack that enables users to design, simulate, compile, and execute quantum circuits on both simulators and real quantum hardware. The primary purposes of Qiskit include facilitating research in quantum algorithms, supporting educational initiatives in quantum computing, and accelerating the development of practical applications, particularly hybrid quantum-classical systems. By providing tools for circuit construction, optimization, and execution, Qiskit lowers the barrier to entry for exploring noisy intermediate-scale quantum (NISQ) devices and advancing the field toward fault-tolerant quantum computing. Qiskit's modular architecture forms a layered stack designed for flexibility and extensibility, originally comprising core elements such as Terra for quantum circuit construction and manipulation, Aer for high-performance simulation, Ignis for quantum device characterization and error mitigation, and Aqua for quantum algorithms and applications. Over time, components like Ignis and Aqua have evolved into specialized add-ons, such as Qiskit Experiments and domain-specific libraries (e.g., Qiskit Nature for ), allowing users to tailor the toolkit to particular needs while maintaining . This structure supports seamless integration with Quantum hardware and other backends, enabling scalable workflows from prototyping to production. The toolkit targets a diverse user base, including quantum developers building applications, researchers prototyping algorithms, educators teaching quantum concepts, and enterprises developing hybrid solutions for optimization, , and simulation challenges. As of November 2025, Qiskit has surpassed 16 million downloads and holds a 74% preference among quantum developers according to the 2024 Unitary Foundation survey, underscoring its pivotal role in the NISQ era by democratizing access to quantum resources and fostering innovation.

History and Development

Qiskit was initially released in March 2017 by as an integral part of the IBM Quantum Experience platform, providing an open-source Python-based for creating and executing quantum circuits on cloud-accessible quantum hardware. This launch marked a pivotal step in democratizing access to tools, enabling developers worldwide to experiment with quantum algorithms without proprietary barriers. Shortly thereafter, Qiskit transitioned to a fully open-source project under the Apache 2.0 license, hosted on , which facilitated broader collaboration and rapid iteration. Key milestones in Qiskit's evolution include its integration with the IBM Q Network in 2018, which expanded enterprise access to quantum resources and fostered joint research initiatives between and industry partners. In 2020, IBM deprecated the Qiskit Aqua library—a higher-level component for domain-specific applications—and shifted focus toward modular primitives for more efficient algorithm execution, streamlining the framework for scalable . The introduction of Qiskit Runtime in May 2021 enhanced cloud-based execution by optimizing quantum-classical hybrid workflows, achieving up to 120x speedups in simulations compared to prior methods. This was followed by the launch of Qiskit Serverless in December 2023, enabling distributed execution of hybrid quantum-classical tasks across remote compute resources. Qiskit's development model emphasizes collaborative open-source contributions, primarily through its repositories managed by alongside a global community of developers, resulting in over 7,000 dependent projects that extend its functionality. Recent advancements reflect a strategic shift from Noisy Intermediate-Scale Quantum (NISQ)-era tools to preparations for fault-tolerant , incorporating support for modular quantum-classical orchestration to handle utility-scale workloads. In October 2025, the release of Qiskit SDK v2.2 included quantum-high-performance computing (HPC) integration demonstrations and enhanced primitives for operators and circuits. Benchmarks from November 2025 show Qiskit v2.2 is 83x faster at transpilation than TKET 2.6.0 for large circuits. A patch release, v2.2.3, followed on October 30, 2025, with bug fixes and minor improvements.

Core Components

Qiskit SDK

The Qiskit SDK serves as the foundational Python-based software development kit for constructing, simulating, and optimizing quantum circuits locally. It enables developers to build quantum programs using high-level abstractions while providing low-level control over quantum operations. The SDK is modular, allowing users to focus on algorithm design without immediate concern for hardware constraints during initial development. At its core, the SDK includes the qiskit.circuit module, which facilitates construction through classes like QuantumCircuit. This module represents computational routines that operate on qubits using unitary gates, measurements, and resets, supporting the creation of circuits for quantum processing units (QPUs). Complementing this is the qiskit.quantum_info module, designed for manipulating quantum states and operators. It offers tools for representing and analyzing , including statevectors, density matrices, Pauli operators, and channels, enabling tasks such as computing expectation values or evolving states under unitaries. Simulation capabilities are provided primarily via the separate Qiskit Aer package, a high-performance simulator. Aer supports multiple methods, including statevector simulation for ideal, noiseless circuits that track the full ; density matrix simulation for handling noisy, open-system dynamics; and shot-based noise models that approximate real hardware imperfections by sampling outcomes over multiple executions. These features allow for accurate local validation of circuits before hardware deployment. The transpilation process in the Qiskit SDK transforms abstract circuits into executable forms tailored to specific hardware topologies. This involves stages such as initialization (unrolling custom ), layout mapping (assigning virtual qubits to physical ones), (inserting swaps for connectivity), translation (converting to basis ), optimization (reducing depth and gate count), and scheduling (managing execution timing). The transpile function orchestrates these via pass managers, with optimization levels from 0 (minimal) to 3 (aggressive). In the v2.2 release of October 2025, transpilation received performance enhancements, achieving 10-20% faster compilation on average through improved C support and a standalone transpiler function. Installation of the Qiskit SDK is straightforward as a pip-installable Python package, requiring Python 3.9 or later. Users create a virtual environment, activate it, and run pip install qiskit to obtain the core SDK; additional dependencies like qiskit[visualization] can be included for plotting tools. Basic usage follows a workflow of circuit creation, simulation, and result analysis. For example, to create and simulate a Bell state:

python

from qiskit import QuantumCircuit from qiskit_aer import AerSimulator from qiskit.visualization import plot_histogram # Create a Bell state circuit qc = QuantumCircuit(2, 2) qc.h(0) qc.cx(0, 1) qc.measure([0, 1], [0, 1]) # Simulate using Aer simulator = AerSimulator() job = simulator.run(qc, shots=1000) result = job.result() counts = result.get_counts(qc) # Visualize results plot_histogram(counts)

from qiskit import QuantumCircuit from qiskit_aer import AerSimulator from qiskit.visualization import plot_histogram # Create a Bell state circuit qc = QuantumCircuit(2, 2) qc.h(0) qc.cx(0, 1) qc.measure([0, 1], [0, 1]) # Simulate using Aer simulator = AerSimulator() job = simulator.run(qc, shots=1000) result = job.result() counts = result.get_counts(qc) # Visualize results plot_histogram(counts)

This code constructs an entangled two-qubit state, simulates 1000 shots, and displays the outcome distribution, typically showing equal probabilities for '00' and '11'. Performance features in the SDK include GPU acceleration for Aer simulations, enabled via CUDA 11.2 or higher and the qiskit-aer-gpu package, which leverages NVIDIA cuQuantum for faster statevector and density matrix computations on compatible hardware. Additionally, the SDK integrates seamlessly with NumPy and SciPy for hybrid classical-quantum workflows, allowing efficient handling of classical data processing alongside quantum operations, such as tensor manipulations or optimization loops.

Qiskit Runtime

Qiskit Runtime is a cloud-based service introduced by in May 2021, designed to enable scalable execution of quantum workloads on IBM Quantum hardware. It optimizes performance by co-locating quantum and classical code in a containerized environment, achieving up to 120x speedups in iterative computations compared to traditional methods. The service incorporates resilient execution mechanisms, including automatic error suppression and mitigation techniques such as dynamical decoupling and zero-noise extrapolation, to improve result accuracy on noisy quantum processors. At the core of Runtime are its : the Sampler, which generates quasi-probability distributions from quantum circuits to sample outcomes, and the , which computes expectation values of observables relative to quantum states. These abstract low-level details, allowing users to focus on algorithmic design. With the release of version 2 in 2024, enhancements were added for advanced operator support—enabling multiple observables in a single job—and batching capabilities, which permit simultaneous execution of multiple circuits or parameter sets for efficient parallel processing. Job management in Qiskit Runtime facilitates efficient handling of complex workloads through sessions, which enable coherent multi-job execution by maintaining backend state across iterations to reduce overhead and improve reproducibility. Parameter resolvers support sweeps over input parameters, allowing users to bind values to parameterized circuits for exploring optimization landscapes without repeated transpilation. The service integrates seamlessly with the IBM Quantum Platform for job submission, monitoring, and result retrieval via APIs. Qiskit Runtime supports hybrid quantum-classical workflows, where classical feedback loops iteratively refine quantum circuits based on prior results, essential for algorithms like variational quantum eigensolvers. In 2025, enhancements expanded capabilities for large-scale circuit execution, including support for fractional gates and dynamic circuits (client version 0.42.0, September 2025) and a new version of dynamic circuits (July 2025) offering up to 75x speedup and parallel execution; simulations up to thousands of qubits are possible using advanced methods like with the Qiskit Aer simulator as a backend. Access is tiered: a free tier provides limited monthly execution time on open-plan quantum devices with over 100 qubits, while premium access for enterprise users is available through the Quantum Network, offering priority queuing and dedicated resources.

Advanced Tools

Qiskit Serverless

Qiskit Serverless, released in 2024 as an open-source extension of the Qiskit ecosystem, provides a for executing quantum-classical hybrid workloads in a serverless manner across distributed environments, including multi-cloud platforms such as AWS and Azure, as well as (HPC) clusters. This framework abstracts resource management, allowing developers to focus on application logic while automatically handling scaling and orchestration of tasks involving quantum processing units (QPUs), CPUs, and GPUs. By leveraging containerized execution similar to cloud-native services, it enables seamless deployment of long-running programs without manual configuration, supporting the growing need for hybrid quantum at scale. Key features of Qiskit Serverless include the Quantum Runtime Middleware Interface (QRMI), a hardware-agnostic layer that facilitates plugin-based integration with diverse quantum backends, such as those from IonQ, Amazon Braket, and IBM Quantum. This middleware simplifies vendor-specific complexities by providing unified APIs for resource control, ensuring portability across providers without altering core application code. Additionally, the framework supports parallel execution and resource allocation, akin to distributed computing libraries like Dask, enabling efficient scaling of jobs across multiple nodes for compute-intensive tasks. Users benefit from built-in fault tolerance and asynchronous job handling, with options for local development and remote deployment via Docker or Kubernetes clusters. Workflow orchestration in Qiskit Serverless revolves around defining quantum functions as Python classes that encapsulate hybrid logic, which are then deployed to target environments for execution. Developers register these functions with a serverless runtime, specifying resource requirements, after which the system handles scheduling, data persistence, and result aggregation across distributed resources. This approach supports iterative workflows, such as those involving quantum primitives from Qiskit Runtime, by distributing classical pre- and post-processing alongside quantum executions. For instance, a variational quantum algorithm can be orchestrated to run quantum subroutines on remote QPUs while classical optimization loops execute on HPC nodes, minimizing latency through automated load balancing. This evolution positions Qiskit Serverless as a bridge for quantum-centric supercomputing, allowing seamless incorporation of quantum accelerators into traditional HPC pipelines without custom middleware. Practical use cases for Qiskit Serverless include large-scale optimization problems, where distributed or QAOA variants are combined with classical solvers to tackle or at unprecedented scales. In applications, it facilitates hybrid quantum-classical training pipelines, distributing quantum feature maps or kernel estimations across cloud and HPC resources to accelerate model development for high-dimensional datasets. These capabilities have been demonstrated in research settings.

Qiskit Add-ons

Qiskit Add-ons evolved from the deprecated Qiskit Aqua library, which was officially deprecated in version 0.9.0 in April 2021, with support ending by the end of that year, to provide a more modular structure for development. The former Aqua's functionalities were refactored into standalone packages, emphasizing primitives such as SamplerV2 and EstimatorV2, introduced in Qiskit 1.0 in , which enable flexible, modular building of algorithms by separating circuit execution from result interpretation and supporting batched inputs for efficient scaling. These primitives facilitate the construction of hybrid quantum-classical workflows without the monolithic approach of legacy components. Key add-ons include Qiskit Optimization, which supports modeling and solving (QUBO) and Ising models through high-level abstractions and integration with quantum algorithms like the quantum approximate optimization algorithm (QAOA). Qiskit provides tools for quantum-enhanced machine learning, featuring the quantum support vector machine (QSVM) for kernel-based classification using fidelity quantum kernels and the variational quantum classifier (VQC) for parameterized quantum circuits interpreted as neural networks. Additionally, Qiskit Nature focuses on quantum chemistry simulations, enabling the computation of molecular ground and excited states via second-quantized operators mapped to Hamiltonians. These add-ons incorporate pre-built function templates for core variational algorithms, such as the (VQE) for finding molecular ground states and QAOA for , with updates in Qiskit Algorithms as of September 2025 enhancing compatibility with V2 primitives to prepare for fault-tolerant by improving scalability and error handling in larger circuits. Integration with the core Qiskit SDK allows plug-and-play circuit generation and execution; for instance, the EstimatorV2 primitive can compute energy expectation values for molecular Hamiltonians in VQE workflows by evaluating Pauli operators derived from second-quantized Hamiltonians, as demonstrated in Qiskit Nature tutorials where it processes ansatz circuits to minimize electronic energies. For performance, the add-ons include built-in support for noise-aware algorithms through primitive options that incorporate backend noise models and error mitigation, alongside hybrid classical optimizers such as COBYLA for constrained derivative-free optimization and SPSA for stochastic gradient approximation, which are particularly effective in noisy intermediate-scale quantum environments by requiring fewer circuit evaluations.

Ecosystem and Extensions

Community Plugins and Integrations

The Qiskit ecosystem encompasses a broad array of third-party extensions developed by the community, with numerous GitHub repositories listed in curated collections such as Awesome Qiskit, fostering interoperability across diverse quantum hardware providers. Key plugins enable seamless access to non-IBM quantum devices, including qiskit-ionq for IonQ's trapped-ion systems, qiskit-braket-provider for Amazon Braket's multi-provider hardware, and qiskit-rigetti for Rigetti's superconducting QPUs. These integrations allow developers to execute Qiskit circuits on heterogeneous backends without modifying core code, promoting a vendor-agnostic workflow. Qiskit Metal stands out as a specialized tool for quantum hardware design, providing an open-source framework for creating superconducting quantum chips. Introduced in 2021, it supports layout for components like resonators and couplers, with updates through 2025 enhancing compatibility for advanced superconducting architectures. The tool facilitates end-to-end design flows, from generation to electromagnetic simulations, enabling engineers to multi-qubit devices efficiently. For performance evaluation, the Benchpress toolkit offers a standardized benchmarking suite to compare transpilation, circuit execution, and overall functionality across quantum providers and SDKs. Comprising over 1,000 tests, it quantifies metrics like compilation time and fidelity, helping developers assess scalability on diverse hardware. Interoperability is further advanced through the Quantum Resource Management Interface (QRMI), a vendor-agnostic standard that abstracts backend control and monitoring for on-premises or cloud quantum systems. Qiskit also supports circuit conversions to frameworks like Cirq via translators such as Qusetta and to PennyLane through dedicated plugins, enabling hybrid workflows in quantum machine learning. In 2025, Qiskit's ecosystem saw heightened adoption in for optimization tasks and in chemistry for molecular simulations, bolstered by integrations with libraries like PennyLane for quantum ML applications and OpenFermion for fermionic system modeling.

Educational and Community Initiatives

Qiskit supports quantum education through a variety of interactive resources designed to teach foundational concepts and practical skills. The Qiskit Textbook, an open-source, Jupyter notebook-based platform, provides tutorials on quantum gates, algorithms such as Grover's search, and hands-on experiments with quantum circuits. Although the original repository was archived in 2024, its content remains accessible and has influenced subsequent learning materials, including integration into broader Quantum platforms. These resources emphasize conceptual understanding, allowing learners to simulate and execute code on quantum hardware. IBM Quantum Learning, accessible via the Qiskit ecosystem, expands on these efforts with a library of over 10 courses covering basics to advanced topics, delivered through interactive modules and expert-led videos. This platform includes pathways updated in 2024 to guide users from introductory to implementation using Qiskit. Comprehensive documentation and tutorials hosted on quantum.cloud.ibm.com offer step-by-step guides on Qiskit installation, usage of primitives like and Sampler, and advanced subjects such as , including repetition codes and the Shor code. Qiskit fosters community engagement through structured programs that promote learning and collaboration. The Qiskit Global Summer School, an annual event since 2020, is a two-week virtual program featuring lectures from Quantum experts, interactive labs, and Q&A sessions to build skills in . The 2025 edition, themed "The Past, Present, and Future of ," attracted global participants to explore quantum technology evolution through hands-on Qiskit exercises. Complementary initiatives like Qiskit Camps, held in various regions such as in prior years, provide immersive training, technical talks, and hackathons to support diverse quantum communities. The Qiskit Advocate Program, relaunched in July 2025 as version 2.0, targets active community members worldwide by offering from IBM experts, networking opportunities, and priority access to IBM Quantum resources like hardware and premium features. This global initiative, building on its 2019 origins, equips advocates with tools to advance quantum and . Qiskit's community impact is evident in its open-source model, with thousands of contributions via through issues, pull requests, and code enhancements to the core SDK and extensions. Partnerships with universities and organizations, such as the IBM-HBCU Quantum Center and the Quantum Network, integrate Qiskit into curricula and research collaborations across institutions worldwide, alongside local Qiskit user groups that host events and workshops. These efforts have cultivated a diverse ecosystem, enabling user groups in regions like and North America to organize hackathons and seminars.

References

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  6. https://www.ibm.com/quantum/blog/qiskit-2-2-release-summary
  7. https://qiskit.org/aer
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  9. https://qiskit.org/ecosystem/nature/
  10. https://qiskit.org/ecosystem/machine-learning/index.html
  11. https://qiskit.org/ecosystem/metal/overview.html
  12. https://medium.com/qiskit/qiskit-and-its-fundamental-elements-bcd7ead80492
  13. https://www.ibm.com/quantum/blog/qiskit-application-modules
  14. https://pepy.tech/project/qiskit
  15. https://github.com/Qiskit/qiskit
  16. https://www.hpcwire.com/2018/04/05/ibm-expands-quantum-computing-network/
  17. https://www.hpcwire.com/2021/05/11/ibm-debuts-qiskit-runtime-for-quantum-computing-reports-dramatic-speed-up/
  18. https://newsroom.ibm.com/2023-12-04-IBM-Debuts-Next-Generation-Quantum-Processor-IBM-Quantum-System-Two%2C-Extends-Roadmap-to-Advance-Era-of-Quantum-Utility
  19. https://www.ibm.com/quantum/blog/qdc-2025
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  23. https://qiskit.org/documentation/tutorials/circuits_advanced/04_transpiler_passes_and_passmanager.html
  24. https://qiskit.org/documentation/apidoc/transpiler.html
  25. https://quantum.cloud.ibm.com/docs/en/api/qiskit/release-notes/2.2
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  27. https://qiskit.org/documentation/qc_intro.html
  28. https://qiskit.github.io/qiskit-aer/getting_started.html
  29. https://www.ibm.com/quantum/blog/qiskit-runtime-capabilities-integration
  30. https://quantum.cloud.ibm.com/docs/en/guides/latest-updates
  31. https://www.ibm.com/quantum/blog/qcsc-software
  32. https://github.com/Qiskit/qiskit-serverless
  33. https://quantum.cloud.ibm.com/docs/guides/serverless
  34. https://www.ibm.com/quantum/blog/qiskit-serverless
  35. https://github.com/qiskit-community/qrmi
  36. https://qiskit.github.io/qiskit-serverless/
  37. https://www.ibm.com/quantum/blog/c-api-enables-end-to-end-hpc-demo
  38. https://www.constellationr.com/blog-news/insights/ibm-launches-qiskit-services-quantum-computing
  39. https://www.ibm.com/quantum/blog/application-functions-pf2025
  40. https://www.ibm.com/quantum/blog/quantum-serverless-programming
  41. https://github.com/qiskit-community/qiskit-aqua
  42. https://qiskit-community.github.io/qiskit-optimization/
  43. https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.QSVC.html
  44. https://qiskit-community.github.io/qiskit-nature/
  45. https://qiskit-community.github.io/qiskit-algorithms/tutorials/05_qaoa.html
  46. https://qiskit-community.github.io/qiskit-algorithms/apidocs/qiskit_algorithms.optimizers.html
  47. https://github.com/qiskit-community/awesome-qiskit
  48. https://github.com/qiskit-community/qiskit-ionq
  49. https://github.com/qiskit-community/qiskit-braket-provider
  50. https://github.com/rigetti/qiskit-rigetti
  51. https://qiskit-community.github.io/qiskit-metal/
  52. https://github.com/qiskit-community/qiskit-metal
  53. https://github.com/Qiskit/benchpress
  54. https://arxiv.org/abs/2409.08844
  55. https://github.com/qcware/qusetta
  56. https://docs.pennylane.ai/en/stable/code/api/pennylane.from_qiskit.html
  57. https://www.forbes.com/councils/forbestechcouncil/2025/08/06/how-quantum-computing-is-shaping-the-future-of-finance/
  58. https://intuitionlabs.ai/articles/ibm-quantum-drug-discovery
  59. https://github.com/PennyLaneAI/pennylane-qiskit
  60. https://github.com/qiskit-community/qiskit-textbook
  61. https://qtedu.eu/material/qiskit-textbook
  62. https://quantum.cloud.ibm.com/learning
  63. https://www.ibm.com/quantum/blog/learning-pathways
  64. https://quantum.cloud.ibm.com/docs
  65. https://quantum.cloud.ibm.com/docs/tutorials/repetition-codes
  66. https://research.ibm.com/blog/qiskit-summer-school-machine-learning
  67. https://www.hpcwire.com/off-the-wire/ibm-opens-registration-for-qiskit-global-summer-school-2025/
  68. https://medium.com/qiskit/recap-2019-qiskit-camp-europe-908c8d8d72e4
  69. https://www.youtube.com/watch?v=4-rIetauS1w
  70. https://www.hpcwire.com/2025/07/29/ibm-launches-qiskit-advocate-program-2-0/
  71. https://github.com/qiskit-advocate/application-guide
  72. https://www.ibm.com/quantum/ibm-quantum-network
  73. https://www.ibm.com/quantum/blog/ibm-hbcu-quantum-center-expands
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