uniqc.algorithms.workflows package

Submodules

• uniqc.algorithms.workflows.classical_shadow_workflow module
  • ShadowWorkflowResult
    • ShadowWorkflowResult.expectations
    • ShadowWorkflowResult.n_snapshots
    • ShadowWorkflowResult.snapshots
  • run_classical_shadow_workflow()
  • run_classical_shadow_workflow_example()
• uniqc.algorithms.workflows.qaoa_workflow module
  • QAOAResult
    • QAOAResult.betas
    • QAOAResult.energy
    • QAOAResult.gammas
    • QAOAResult.history
    • QAOAResult.message
    • QAOAResult.n_iter
    • QAOAResult.success
  • run_qaoa_workflow()
  • run_qaoa_workflow_example()
• uniqc.algorithms.workflows.readout_em_workflow module
  • apply_readout_em()
  • run_readout_em_workflow()
• uniqc.algorithms.workflows.vqe_workflow module
  • VQEResult
    • VQEResult.energy
    • VQEResult.history
    • VQEResult.message
    • VQEResult.n_iter
    • VQEResult.params
    • VQEResult.success
  • run_vqe_workflow()
  • run_vqe_workflow_example()
• uniqc.algorithms.workflows.xeb_workflow module
  • run_1q_xeb_workflow()
  • run_2q_xeb_workflow()
  • run_parallel_cz_xeb_workflow()
  • run_parallel_xeb_workflow()

Module contents

Higher-level workflows that combine calibration, execution, and mitigation.

Currently includes: - readout_em_workflow — calibrate + apply readout-error mitigation - xeb_workflow — 1q / 2q / parallel cross-entropy benchmarking - vqe_workflow — scipy-driven VQE on a Pauli-string Hamiltonian - qaoa_workflow — scipy-driven QAOA on a cost Hamiltonian - classical_shadow_workflow — collect shadow snapshots + multi-observable estimates

VQE/QAOA workflows here are deliberately torch-free so they work in the base install. For autodiff / parameter-shift / batched-gradient flavours, use uniqc.algorithms.core.training (requires [pytorch] + torchquantum).