Qiskit addons
Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These capabilities build on Qiskit's performant foundation of tools for creating and running quantum algorithms. They are provided as modular software components that can plug into a workflow to scale or design new quantum algorithms.
Addons for mapping
Approximate quantum compilation with tensor networks
Approximate quantum compilation with tensor networks (AQC-Tensor) enables the construction of high-fidelity circuits with reduced depth.
- Visit the GitHub repository.
- Read the documentation.
- Read the tutorial on using AQC to improve Trotterized time evolution.
Multi-product formulas
Multi-product formulas (MPF) reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.
- Visit the GitHub repository.
- Read the documentation.
Addons for optimizing
Operator backpropagation
Operator backpropagation (OBP) reduces circuit depth by trimming operations from the end at the cost of more operator measurements.
- Visit the GitHub repository.
- Read the documentation.
- Read the tutorial on using OBP to improve expectation values.
Circuit cutting
Circuit cutting reduces the depth of transpiled circuits by decomposing entangling gates between non-adjacent qubits.
- Visit the GitHub repository.
- Read the documentation.
Propagated noise absorption
Propagated noise absorption (PNA) uses Pauli propagation to absorb information from a noise model into a target observable. Measuring this modified observable has the effect of mitigating the noise as represented by the model.
Visit the GitHub repository.
Shaded lightcones
The shaded lightcones addon uses Pauli propagation to reduce the number of error terms accounted for in a noise model according to the specifics of the target observable. This has the effect of reducing the sampling overhead for probabilistic error cancellation (PEC) workflows.
Visit the GitHub repository.
Addons for post-processing
Sample-based quantum diagonalization
Sample-based quantum diagonalization (SQD) classically post-processes noisy quantum samples to yield more accurate eigenvalue estimations of quantum system Hamiltonians, for example in chemistry applications.
- Visit the GitHub repository.
- Read the documentation.
- Read the tutorial on Sample-based quantum diagonalization of a chemistry Hamiltonian.