QBalance: A Reproducible Multi-Objective Workflow for Quantum Compilation, Noise Suppression, and Error-Mitigation Strategy Selection

TL;DR

QBalance is a Python workflow library that optimizes quantum compilation, noise suppression, and error mitigation strategies through a multi-objective, reproducible approach within the Qiskit ecosystem.

Contribution

It formulates a finite multi-objective strategy-selection problem and introduces novel diagnostics and surrogate models for quantum workflow optimization.

Findings

QBalance enables reproducible quantum workflow orchestration.

The system provides a finite, multi-objective strategy selection framework.

Limitations include evaluation order without candidate reduction and partial topology awareness.

Abstract

Near-term quantum workloads are shaped by coupled compilation and execution choices: qubit layout, routing, basis translation, gate suppression, measurement mitigation, shot budget, and artifact reproducibility. This paper analyzes QBalance, a Python workflow library for dataset-level selection among quantum compilation, noise-suppression, and error-mitigation strategies built on the Qiskit ecosystem. The contribution is formulated as a finite multi-objective strategy-selection problem over circuits, backends, and transformation policies. The manuscript derives the implemented weighted objective, non-dominated selection rule, survival-product error proxy, Bayesian linear candidate-ordering surrogate, and distributional diagnostics. It also positions the system relative to established work on Qiskit pass-manager compilation, SABRE-style routing, randomized compiling, dynamical decoupling, zero-noise extrapolation, matrix-free measurement mitigation, circuit cutting, and Thompson sampling. The analysis shows that QBalance provides a reproducible orchestration and artifact model for quantum workflow studies. It also establishes precise limitations: the current bandit mechanism orders candidates but does not reduce the number of candidate evaluations, the custom layout heuristic is greedy and only partially topology-aware, the implemented ZNE helper is parity-centered, and the cutting integration is a hook rather than a full reconstruction pipeline.