--- title: Introduction to Qiskit Functions description: Explore IBM Qiskit Functions Catalog, abstracted circuit function and application function services designed to accelerate research and application prototyping. source: https://eu-de.quantum.cloud.ibm.com/docs/en/guides/functions --- # Introduction to Qiskit Functions > **Notes** > > - Qiskit Functions are an experimental feature available only to IBM Quantum® Premium Plan, Flex Plan, and On-Prem (via IBM Quantum Platform API) Plan users. They are in preview release status and subject to change. Qiskit Functions simplify and accelerate utility-scale algorithm discovery and application development, by abstracting away parts of the quantum software development workflow. In this way, Qiskit Functions free up time normally spent hand-writing code and fine-tuning experiments. [Qiskit Functions Catalog](/functions) ![Overview of Qiskit Functions](https://eu-de.quantum.cloud.ibm.com/docs/images/guides/functions/functions-overview.svg) Functions come in two forms: | Type | What does it do? | Example inputs and outputs | Who is it for? | | -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Circuit function | Simplified interface for running circuits. Abstracts transpilation, error suppression, and error mitigation | **Input**: Abstract `PUB` objects **Output**: Mitigated expectation values | Researchers using Qiskit to discover new algorithms and applications, without needing to focus on optimizing for hardware or handling error. Circuit functions can be used to build custom application functions. | | Application function | Covers higher-level tasks, such as exploring algorithms and domain-specific use cases. Abstracts quantum workflow to solve tasks, with classical inputs and outputs | **Input**: Molecules, graphs **Output**: Ground + excited state energy, optimal values for cost function | Researchers in non-quantum domains, integrating quantum into existing large-scale classical workflows, without needing to map classical data to quantum circuits. | Functions are provided by IBM® and third-party partners. Each is performant for specific workload characteristics and has unique performance-tuning options. ## Overview of available functions ### Circuit functions | Name | Provider | Recommended use | Unique benefits | | ------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Tensor-Network Error Mitigation[Guide](/docs/guides/algorithmiq-tem)[API reference](/docs/api/functions/algorithmiq-tem) | Algorithmiq | Workloads that have low-weight observables and loop-free circuits. | Reduces measurement overhead and variance, outperforming standard error mitigation baselines such as Zero Noise Extrapolation (ZNE) and Probabilistic Error Cancellation (PEC) for relevant circuit classes. | | QESEM: Error Suppression and Error Mitigation[Guide](/docs/guides/qedma-qesem)[API reference](/docs/api/functions/qedma-qesem) | Qedma | Workloads that include circuits with fractional or parameterized gates, high-weight observables, and workflows that require unbiased expectation values and accurate runtime estimates. | Produces unbiased expectation values with lower variance and resource overhead, outperforming ZNE and PEC for relevant circuit classes. | | Performance Management[Guide](/docs/guides/q-ctrl-performance-management)[API reference](/docs/api/functions/q-ctrl-performance-management) | Q-CTRL | Workloads that contain parametric circuits, deep circuits, or require many circuit executions. | Automatically applies AI-driven error suppression to quantum algorithms, maximizing the performance of IBM devices to deliver accurate results while reducing the number of shots, compute time, and cost required. Zero-overhead method that improves execution accuracy for the Sampler and the Estimator primitives, compatible with any weight of observables. | ### Application functions | Name | Provider | Recommended use | Unique benefits | | ------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | QUICK-PDE[Guide](/docs/guides/colibritd-pde)[API reference](/docs/api/functions/colibritd-pde) | ColibriTD | Use quantum computation for multi-physics PDEs.Prepare simulation workflows for quantum hardware, while keeping full control over both quantum and physical modeling parameters. | Offers a robust hybrid VQA framework that delivers precise, scalable PDE solutions through advanced solution encoding and spectral methods, making it an ideal entry point for teams trying to build quantum-ready simulation capabilities. | | Quantum Portfolio Optimizer [Guide](/docs/guides/global-data-quantum-optimizer)[API reference](/docs/api/functions/global-data-quantum-optimizer) | Global Data Quantum | Workloads for financial optimization, seeking optimal portfolio strategies over time while minimizing risk and maximizing returns, enabling trading strategy back-testing. | Solves combinatorial optimization problems through a highly specialized adaptation of the VQE quantum algorithm for this financial use case, using optimized execution strategies and optimizers, along with noise-aware error mitigation techniques tailored to portfolio optimization. | | HI-VQE Chemistry[Guide](/docs/guides/qunova-chemistry)[API reference](/docs/api/functions/qunova-chemistry) | Qunova Computing | Workloads in computational chemistry, molecular simulation, materials science, or any Hamiltonian simulation that require solving many-body electronic structure problems. | Solves molecular electronic structures by using enhanced SQD with achieving chemical accuracy (1 kcal/mol, 1.6 mHa) for problems modeled with 40 to 60 qubits, outperforming some classical solutions on supercomputers or standard SQD in convergence speed or accuracy, respectively, by orders of magnitude. | | Iskay Quantum Optimizer[Guide](/docs/guides/kipu-optimization)[API reference](/docs/api/functions/kipu-optimization) | Kipu Quantum | Optimization workloads such as scheduling, logistics, routing, and QUBO/HUBO problems. | Integrated tunable classical pre- and post-processing methods for the quantum optimization routine. Delivers runtime advantage over classical solvers (CPLEX, simulated annealing, and tabu search) on selected HUBO benchmarks. Market Split `ms_5_100`, a hard challenge, solved within hours (see [this tutorial](/docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer)). | | Singularity Machine Learning[Guide](/docs/guides/multiverse-computing-singularity)[API reference](/docs/api/functions/multiverse-computing-singularity) | Multiverse Computing | Classical machine learning classification workflows that could benefit from improved accuracy or computational efficiency by leveraging quantum optimization executed on IBM hardware. | Delivers accuracy comparable to or exceeding classical models such as Random Forest or XGBoost, while operating with significantly fewer learners and a more compact ensemble. Powered by quantum-optimized voting, it selects the most informative learners and refines decision boundaries, resulting in greater efficiency, reduced model complexity, and more robust performance. | | Optimization Solver[Guide](/docs/guides/q-ctrl-optimization-solver)[API reference](/docs/api/functions/q-ctrl-optimization-solver) | Q-CTRL | Binary optimization problems or any combinatorial problem that can be mapped to a binary cost function. Cost functions of any order and problem sizes up to the maximum device scale are supported. | Noise-aware, end-to-end quantum optimization solution that enables inputs of high-level problem definitions and automatically finds accurate solutions to classically challenging combinatorial problems on utility-scale quantum hardware. It abstracts away complexity by handling error suppression, efficient mapping, and hybrid quantum-classical optimization to solve optimization tasks at full device scale without deep quantum expertise. |