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Quantinuum with QESEM

QESEM extends Quantinuum's trapped-ion systems with error suppression and mitigation designed for accurate quantum execution at large circuit depths. Residual hardware noise accumulates as workloads grow; QESEM reduces its impact on measured observables while giving users control over the resources used for mitigation.

The Quantinuum workflow combines application-agnostic mitigation with a staged HQC planning process. It supports quantum workloads in areas such as chemistry, materials science, optimization, machine learning and more through Qedma's cloud service.

Description

On Quantinuum, QESEM uses a characterization-free mitigation flow tailored to trapped-ion hardware. The workflow separates job creation, analytical estimation, empirical resource optimization and estimation, and full mitigation. This lets you:

  • Submit a circuit, observables, target precision, and backend.
  • Use analytical estimation to determine the exact HQC cost of the empirical resource estimation.
  • Transpile the circuit for optimal mitigation performance.
  • Use empirical resource estimation to determine the exact HQC required for the remaining mitigation at the specified precision, as well as optimize the mitigation parameters to minimize these resources.
  • Launch the final execution with an explicit max_hqc budget. Note that max_hqc is a best effort budget, not a strict execution limit. A run may exceed it by several hundred HQC units. Setting max_hqc below the value returned by resource estimation may prevent QESEM from reaching the target precision.

This staged flow makes resource planning visible before the full execution begins.

Core Advantages

  • Characterization-free execution: Avoids HQC spent on the characterization workflow used for other hardware types and robustifies the mitigation against noise drifts or out-of-model errors.
  • Comprehensive error mitigation: Mitigates all major error sources, including non-Markovian effects such as leakage and ion-shuttling errors.
  • Predictable execution planning: Determine the empirical resource estimation HQC cost and the recommended mitigation HQC cost before choosing a concrete max_hqc limit.
  • Application-agnostic mitigation: Supports periodic and unstructured circuits and hybrid quantum-classical workflows without application-specific tuning.
  • Quantinuum-native execution: Works alongside native techniques such as fractional-angle gates, dynamical decoupling, and twirling.
  • Execution transparency: Returns mitigated and unmitigated results, HQC consumption, and execution metadata so you can reason about both accuracy and cost.

Next Steps