diff --git a/docs/guides/functions.mdx b/docs/guides/functions.mdx index af82a85e7da..7cc0eb4056a 100644 --- a/docs/guides/functions.mdx +++ b/docs/guides/functions.mdx @@ -31,18 +31,18 @@ Functions are provided by IBM® and third-party partners. Each is performant | 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. | +| Tensor-Network Error Mitigation

[Guide](/docs/guides/algorithmiq-tem)
[API reference](/docs/api/functions/algorithmiq-tem)
[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. | | 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. | +| Performance Management

[Guide](/docs/guides/q-ctrl-performance-management)
[API reference](/docs/api/functions/q-ctrl-performance-management)
[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.

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. | +| QUICK-PDE

[Guide](/docs/guides/colibritd-pde)
[API reference](/docs/api/functions/colibritd-pde)
[License](https://ibm.box.com/s/eylihre0nf2lskzteyabd7y7v7cu5bb3) | 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)
[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. | +| HI-VQE Chemistry

[Guide](/docs/guides/qunova-chemistry)
[API reference](/docs/api/functions/qunova-chemistry)
[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. | +| Iskay Quantum Optimizer

[Guide](/docs/guides/kipu-optimization)
[API reference](/docs/api/functions/kipu-optimization)
[License](https://ibm.box.com/s/h7ocmsqgwjuj493b46m1nrkqkazh4g4j) | 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)
[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.

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)
[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.

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. | diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts index a7c3286f894..985bda1fb36 100644 --- a/scripts/js/lib/links/ignores.ts +++ b/scripts/js/lib/links/ignores.ts @@ -192,6 +192,13 @@ const ALWAYS_IGNORED_URLS__EXPECTED = [ "https://finance.yahoo.com/quote/META", "https://finance.yahoo.com/quote/TMBMKDE-10Y", "https://finance.yahoo.com/quote/XS2239553048", + "https://ibm.box.com/s/7d36ty2r5ecmnnu1bvk4kxx5grbz2mwz", + "https://ibm.box.com/s/cbvfdhv3i0vnwv4sxkdpp3pcvip27atd", + "https://ibm.box.com/s/eylihre0nf2lskzteyabd7y7v7cu5bb3", + "https://ibm.box.com/s/h7ocmsqgwjuj493b46m1nrkqkazh4g4j", + "https://ibm.box.com/s/ly9cs0lxy6nxqrirt5rt1xisnmvydj9z", + "https://ibm.box.com/s/oe621o30gxvpo8v0tivp31075w9wc6t8", + "https://ibm.box.com/s/t9tj2t8ey8hh1iep3jr88yzlqbqdvq78", "https://ibm.ent.box.com/s/bipgoms7gr6b6vhkoc1uw6oi4wsanfoq", "https://ibm.ent.box.com/s/blnffu0pd7yzxarq3zc3w0jv90365ny2", "https://ibm.ent.box.com/s/fh3xele1e7k0nrgd1imivvq52hy3wz9c",