Qiskit

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.

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 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 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:

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 (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.