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 (opens in a new tab)

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

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• 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.

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• AQC-Tensor (opens in a new tab)

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

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• Multi-product formulas (opens in a new tab)

  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 (opens in a new tab)

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

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Learn noise models

Use the following tools for learning and refining noise models as part of your error mitigation strategy

• Noise learning

  Build randomization-based quantum noise characterization experiments and model-fitting analysis pipelines

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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 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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• Qiskit Mitigation

  Build custom error mitigation pipelines with techniques such as PEC, ZNE, and TREX directly with Samplomatic.

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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 (opens in a new tab)

  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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• Postselection with bit-flip checks (opens in a new tab)

  Filter out non-Markovian noise in sampled bitstrings by incorporating bit-flip checks into a circuit.

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• Matrix-free Measurement Mitigation (M3) (opens in a new tab)

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

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• Paulice

  Boost the fidelity of sampled distributions using hardware-efficient Pauli checks based on spacetime codes.

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Supporting capabilities

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

• Addon utilities (opens in a new tab)

  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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