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.
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 API reference | 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 API reference | 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 API reference | 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 API reference | 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 API reference | 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 API reference | 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 API reference | 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). |
| Singularity Machine Learning Guide API reference | 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 API reference | 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. |
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