Qiskit addons

September 2026

• New tools have been built on top of the directed execution model framework, which was released earlier this year:

  • qiskit-noise-learning is a toolkit for randomization-based quantum noise characterization. You can use it to construct a custom noise-learning experiment, validate it with local simulation, and analyze the raw results.

  • qiskit-mitigation is a package of quantum error mitigation methods for building mitigation pipelines with the new execution model. It includes implementations of well-known methods, including PEC, PEA, TREX, and gate-folding ZNE, as well as post-selection for filtering out non-Markovian errors.

August 2026

• Qiskit Fermions had its first release this month! This package provides tools for working with fermionic systems, including building fermionic operators and circuits, implementing fermion-to-qubit encodings, and circuit synthesis through the Qiskit transpiler integration. Qiskit Fermions can be used with other complementary tools in the ecosystem, such as ffsim (see this documentation and blog for more information) and the SQD addon, to build advanced workflows for fermionic systems.

See how these tools are applied in practice with the SqDRIFT algorithm for ground state estimation tutorial and the following guide: Building constant-depth circuits to simulate the dynamics of the 1- and 2-D Fermi-Hubbard model. Check out the blog and documentation to get started.

June 2026

• Introducing Qiskit Paulice: a new addon that integrates low-overhead quantum error detection into quantum circuits to boost the fidelity of measured distributions. It provides single-shot access to the quantum state and so can be used in both sampling-based and expectation value-based workflows. Check out the blog and documentation for how to get started today.

Previous updates

• We recently released two new addons that use classical resources to reduce the quantum resources needed for error mitigation:

  • Propagated noise absorption (PNA) is a technique for mitigating errors in expectation values by absorbing the inverses of the learned noise channels into observables using Pauli propagation.

  • The Shaded lightcones (SLC) addon uses classical simulations to more tightly bound the sensitivity to errors throughout the circuit. This allows for more efficient, targeted applications of Probabilistic Error Cancellation (PEC) with reduced variance.