Advanced techniques - Qiskit addons

Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These modular software components build on Qiskit’s performant foundation and can plug into a workflow to scale or design new quantum algorithms. This page highlights the available tools across key functional categories to help you choose relevant capabilities when building your workflows.

Map domain problems

These capabilities specialize in mapping domain problems into quantum operators and circuits for execution on a quantum computer.

Optimization mapper

Model optimization problems and map them into representations that can be understood by a quantum computer.

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Fermionic mapper

Model fermionic quantum systems and map them to qubit operators and circuits.

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AQC-Tensor

Construct high-fidelity circuits with reduced depth using approximate quantum compilation with tensor networks.

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Multi-product formulas

Reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.

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Optimize circuits for hardware execution

These capabilities are useful for reducing circuit depth and typically come with an increased sampling overhead.

Operator backpropagation

Reduce circuit depth in expectation value estimations by backpropagating observables through the circuit.

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Circuit cutting

Reduce the depth of transpiled circuits by decomposing entangling gates between non-adjacent qubits.

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Manage noise for expectation value estimation

Use the following addons to manage noise when building quantum workloads that estimate expectation values of observables.

Propagated noise absorption

Mitigate expectation values by measuring a target observable that has absorbed noise model information.

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Shaded lightcones

Reduce probabilistic error cancellation (PEC) sampling overhead by removing noise model terms that have low impact on the observable estimation.

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Manage noise for sampling results

These techniques are useful for managing noise on sampling results.

Sample-based quantum diagonalization

Estimate the spectrum of quantum Hamiltonians by processing noisy samples and diagonalizing in a reduced subspace.

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SQD for HPC

An HPC-ready implementation of the SQD addon, written in modern C++17 standards and designed to enable HPC workflows and applications.

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Measurement-based postselection

Filter out non-Markovian noise in circuits by incorporating measurement-based postselection transpiler passes.

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Matrix-free Measurement Mitigation (M3)

Mitigate measurement errors by processing in a reduced subspace defined by noisy bitstrings.

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Supporting capabilities

Use these capabilities to support and compose your workflows that leverage other addons.

Addon utilities

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.

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Pauli propagation

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.

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