---
title: Tools index
description: Directory page for all IBM-maintained tooling for developing quantum workloads
source: https://eu-de.quantum.cloud.ibm.com/docs/en/guides/tools-index
---

# Tools index

- [**Addon utilities**](https://qiskit.github.io/qiskit-addon-utils/) — Build addons-powered workflows faster by using this collection of functions for creating Hamiltonians, generating Trotter time-evolution circuits, and applying the latest error mitigation capabilities.
- [**AI-QEM by LTM**](/docs/functions/ltm-ai-qem) — Mitigate Estimator expectation values with an AI model, with no additional QPU time.
- [**AQC-Tensor**](https://qiskit.github.io/qiskit-addon-aqc-tensor/) — Construct high-fidelity circuits with reduced depth using approximate quantum compilation with tensor networks.
- [**Qiskit Bosons**](/docs/addons/qiskit-bosons) — Work with bosonic systems using data structures for bosonic operators and circuits, as well as frameworks for boson-to-qubit encodings and circuit synthesis through the Qiskit transpiler integration.
- [**Constrained Quantum Optimizer by Aqarios**](/docs/functions/aqarios-constrained-quantum-optimizer) — Solve constrained binary optimization problems on IBM Quantum hardware by using the iterative warm-starting QAOA with XY-mixers.
- [**Dynamics Simulator by Q-CTRL**](/docs/functions/q-ctrl-dynamics-simulator) — Simulate the time evolution of quantum many-body models and estimate time traces of expectation values, with automated error suppression.
- [**Qiskit Fermions**](/docs/addons/qiskit-fermions) — Work with fermionic systems using data structures for fermionic operators and circuits, as well as frameworks for fermion-to-qubit encodings and circuit synthesis through the Qiskit transpiler integration.
- [**ffsim**](https://qiskit-community.github.io/ffsim/) — Simulate fermionic quantum circuits which conserve particle number and the z component of spin.
- [**Qiskit Fulqrum**](https://qiskit-community.github.io/fulqrum/) — A set of tools for enabling the solution to general large-scale Hamiltonian subspace eigenproblems.
- [**HI-VQE Chemistry by Qunova Computing**](/docs/functions/qunova-chemistry) — Solve for the ground state and ground state energy of chemical systems with Qunova Computing's HI-VQE Quantum Function.
- [**IBM Quantum Compute**](/docs/ibm-quantum-compute) — Run workloads on IBM quantum hardware using Qiskit primitives and execution modes.
- [**Iskay Quantum Optimizer by Kipu Quantum**](/docs/functions/kipu-optimization) — Efficiently solve optimization problems with Kipu Quantum's Iskay Quantum Optimizer.
- [**Managed workflows**](/docs/managed-workflows) — Deploy your own quantum-classical workflows to IBM Quantum Functions and run them on managed classical compute.
- [**Matrix-free Measurement Mitigation (M3)**](https://qiskit.github.io/qiskit-addon-mthree/) — Mitigate measurement errors by processing in a reduced subspace defined by noisy bitstrings.
- [**Qiskit Mitigation**](/docs/addons/qiskit-mitigation) — Build custom error mitigation pipelines with techniques such as Probabilistic Error Cancellation (PEC), Zero-Noise Extrapolation (ZNE), Twirled Readout Error eXtinction (TREX), and Probabilistic Error Amplification (PEA) directly with Samplomatic.
- [**Multi-product formulas**](https://qiskit.github.io/qiskit-addon-mpf/) — Reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.
- [**Qiskit noise learning**](/docs/addons/qiskit-noise-learning) — Build randomization-based quantum noise characterization experiments and model-fitting analysis pipelines
- [**Operator backpropagation**](/docs/addons/qiskit-addon-obp) — Reduce circuit depth in expectation value estimations by backpropagating observables through the circuit.
- [**Optimization mapper**](https://qiskit.github.io/qiskit-addon-opt-mapper/) — Model optimization problems and map them into representations that can be understood by a quantum computer.
- [**Optimization Solver by Q-CTRL Fire Opal**](/docs/functions/q-ctrl-optimization-solver) — Start solving utility-scale optimization problems with the Fire Opal Optimization Solver by Q-CTRL.
- [**Orbit by Quantum Elements**](/docs/functions/quantum-elements-orbit) — Apply low-overhead dynamical decoupling to improve circuit performance on IBM quantum hardware.
- [**Parity Twine Optimizer by ParityQC**](/docs/functions/parity-twine-optimizer) — Solve optimization problems on quantum computers using Parity Twine, which minimizes gate counts and circuit depths.
- [**Pauli propagation**](/docs/addons/pauli-prop/) — A framework to approximate operator evolution, which can be used to simulate expectation values and implement error mitigation techniques, such as propagated noise absorption (PNA) and shaded lightcones.
- [**Qiskit Paulice**](/docs/addons/qiskit-addon-paulice) — Boost the fidelity of sampled distributions using hardware-efficient Pauli checks based on spacetime codes.
- [**Performance Management by Q-CTRL**](/docs/functions/q-ctrl-performance-management) — Apply automated error suppression using Fire Opal Performance Management by Q-CTRL.
- [**Propagated noise absorption (PNA)**](/docs/addons/qiskit-addon-pna) — Technique for mitigating errors in observable expectation values by 'absorbing' the inverses of learned noise channels into an observable using Pauli propagation.
- [**QESEM by Qedma**](/docs/functions/qedma-qesem) — Produce unbiased expectation values with lower variance and resource overhead for circuits with fractional or parameterized gates and high-weight observables.
- [**Qiskit SDK**](/docs/qiskit) — Build, optimize, and execute quantum programs using the open-source software for working with quantum circuits and running them on quantum hardware.
- [**QUICK-PDE by ColibriTD**](/docs/functions/colibritd-pde) — Use quantum computation for multi-physics PDEs, preparing simulation workflows for quantum hardware while keeping full control over quantum and physical modeling parameters.
- [**Quantum Portfolio Optimizer by Global Data Quantum**](/docs/functions/global-data-quantum-optimizer) — Solve financial optimization problems, seeking optimal portfolio strategies over time while minimizing risk and maximizing returns.
- [**Rimay Quantum Feature Extraction by Kipu Quantum**](/docs/functions/kipu-rimay) — Extract quantum-transformed features for classical machine learning pipelines using digitized counterdiabatic evolution of a spin-glass Hamiltonian.
- [**Sample-based quantum diagonalization (SQD)**](/docs/addons/qiskit-addon-sqd) — Estimate the spectrum of quantum Hamiltonians by processing noisy samples and diagonalizing in a reduced subspace.
- [**Samplomatic**](/docs/addons/samplomatic) — Support customized sampling randomizations for the directed execution model, using annotated circuit boxes to capture design intents such as twirling and noise injection on the client side.
- [**Selected Basis Diagonalization (SBD) eigensolver**](https://github.com/Qiskit/sbd-eigensolver-python) — SBD (Selected Basis Diagonalization) is a high-performance library for quantum chemistry calculations.
- [**Shaded lightcones (SLC)**](/docs/addons/qiskit-addon-slc) — Reduce probabilistic error cancellation (PEC) sampling overhead by removing noise model terms that have low impact on the observable estimation.
- [**Singularity Machine Learning by Multiverse Computing**](/docs/functions/multiverse-computing-singularity) — Improve the accuracy and computational efficiency of classical machine learning classification workflows by using quantum optimization executed on IBM hardware.
- [**SQD for HPC**](https://qiskit.github.io/qiskit-addon-sqd-hpc/) — An HPC-ready implementation of the SQD addon, written in modern C++17 standards and designed to enable HPC workflows and applications.
- [**SQOA Solver by JIJ**](/docs/functions/jij-sqoa-solver) — Solve large combinatorial optimization problems formulated as QUBOs by combining quantum random access optimization with sample-based quantum diagonalization and classical postprocessing.
- [**Tensor-network error mitigation by Algorithmiq**](/docs/functions/algorithmiq-tem) — Reduce measurement overhead and variance for workloads with low-weight observables and loop-free circuits, outperforming standard error mitigation baselines such as ZNE and PEC.