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| Name | Provider | Recommended use | Unique benefits |
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| Tensor-Network Error Mitigation<br/><br/>[Guide](/docs/guides/algorithmiq-tem)<br/>[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. |
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| Tensor-Network Error Mitigation<br/><br/>[Guide](/docs/guides/algorithmiq-tem)<br/>[API reference](/docs/api/functions/algorithmiq-tem)<br/>[License](https://ibm.box.com/s/cbvfdhv3i0vnwv4sxkdpp3pcvip27atd)| 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. |
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| QESEM: Error Suppression and Error Mitigation<br/><br/>[Guide](/docs/guides/qedma-qesem)<br/>[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. |
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| Performance Management<br/><br/>[Guide](/docs/guides/q-ctrl-performance-management)<br/>[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. <br/><br/>Zero-overhead method that improves execution accuracy for the Sampler and the Estimator primitives, compatible with any weight of observables. |
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| Performance Management<br/><br/>[Guide](/docs/guides/q-ctrl-performance-management)<br/>[API reference](/docs/api/functions/q-ctrl-performance-management)<br/>[License](https://ibm.box.com/s/ly9cs0lxy6nxqrirt5rt1xisnmvydj9z)| 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. <br/><br/>Zero-overhead method that improves execution accuracy for the Sampler and the Estimator primitives, compatible with any weight of observables. |
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### Application functions
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| Name | Provider | Recommended use | Unique benefits |
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|---|---|---|---|
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| QUICK-PDE<br/><br/>[Guide](/docs/guides/colibritd-pde)<br/>[API reference](/docs/api/functions/colibritd-pde)| ColibriTD | Use quantum computation for multi-physics PDEs.<br/><br/>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. |
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| Quantum Portfolio Optimizer <br/><br/>[Guide](/docs/guides/global-data-quantum-optimizer)<br/>[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. |
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| HI-VQE Chemistry<br/><br/>[Guide](/docs/guides/qunova-chemistry)<br/>[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. |
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| Iskay Quantum Optimizer<br/><br/>[Guide](/docs/guides/kipu-optimization)<br/>[API reference](/docs/api/functions/kipu-optimization)| Kipu Quantum | Optimization workloads such as scheduling, logistics, routing, and QUBO/HUBO problems. <br/><br/> | Integrated tunable classical pre- and post-processing methods for the quantum optimization routine. <br/><br/>Delivers runtime advantage over classical solvers (CPLEX, simulated annealing, and tabu search) on selected HUBO benchmarks. <br/><br/>Market Split `ms_5_100`, a hard challenge, solved within hours (see [this tutorial](/docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer)). |
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| Singularity Machine Learning<br/><br/>[Guide](/docs/guides/multiverse-computing-singularity)<br/>[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. <br/><br/>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. |
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| Optimization Solver<br/><br/>[Guide](/docs/guides/q-ctrl-optimization-solver)<br/>[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. <br/><br/>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. <br/><br/>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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| QUICK-PDE<br/><br/>[Guide](/docs/guides/colibritd-pde)<br/>[API reference](/docs/api/functions/colibritd-pde)<br/>[License](https://ibm.box.com/s/eylihre0nf2lskzteyabd7y7v7cu5bb3)| ColibriTD | Use quantum computation for multi-physics PDEs.<br/><br/>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. |
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| Quantum Portfolio Optimizer <br/><br/>[Guide](/docs/guides/global-data-quantum-optimizer)<br/>[API reference](/docs/api/functions/global-data-quantum-optimizer)<br/>[License](https://ibm.box.com/s/t9tj2t8ey8hh1iep3jr88yzlqbqdvq78)| 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. |
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| HI-VQE Chemistry<br/><br/>[Guide](/docs/guides/qunova-chemistry)<br/>[API reference](/docs/api/functions/qunova-chemistry)<br/>[License](https://ibm.box.com/s/7d36ty2r5ecmnnu1bvk4kxx5grbz2mwz)| 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. |
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| Iskay Quantum Optimizer<br/><br/>[Guide](/docs/guides/kipu-optimization)<br/>[API reference](/docs/api/functions/kipu-optimization)<br/>[License](https://ibm.box.com/s/h7ocmsqgwjuj493b46m1nrkqkazh4g4j)| Kipu Quantum | Optimization workloads such as scheduling, logistics, routing, and QUBO/HUBO problems. <br/><br/> | Integrated tunable classical pre- and post-processing methods for the quantum optimization routine. <br/><br/>Delivers runtime advantage over classical solvers (CPLEX, simulated annealing, and tabu search) on selected HUBO benchmarks. <br/><br/>Market Split `ms_5_100`, a hard challenge, solved within hours (see [this tutorial](/docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer)). |
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| Singularity Machine Learning<br/><br/>[Guide](/docs/guides/multiverse-computing-singularity)<br/>[API reference](/docs/api/functions/multiverse-computing-singularity)<br/>[License](https://ibm.box.com/s/oe621o30gxvpo8v0tivp31075w9wc6t8)| 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. <br/><br/>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. |
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| Optimization Solver<br/><br/>[Guide](/docs/guides/q-ctrl-optimization-solver)<br/>[API reference](/docs/api/functions/q-ctrl-optimization-solver)<br/>[License](https://ibm.box.com/s/ly9cs0lxy6nxqrirt5rt1xisnmvydj9z)| Q-CTRL | Binary optimization problems or any combinatorial problem that can be mapped to a binary cost function. <br/><br/>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. <br/><br/>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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