Measurement error mitigation (MEM) is essential for realizing reliable quantum computation. Model-free measurement error mitigation (MF-MEM) is an important class of MEM methods that employs Pauli twirling—typically with a random twirling set—to convert measurement noise into a state-independent scaling factor, thereby enabling error mitigation through simple calibration. However, such methods face prohibitive sampling overhead, limiting their scalability. To address this, we introduce subsystem-balanced Pauli twirling (SB-PT), a twirling method designed for MF-MEM that enforces Pauli operator balance on measuring subsystems to selectively suppress dominant measurement noise. Theoretically, for a weight-$\tau$ Pauli observable, SB-PT removes all independent error components using only $\mathcal{O}\left(4^{\tau }\right)$ random circuits, substantially reducing the sampling overhead over conventional Pauli twirling. This efficiency gain is most significant for sparse observables. To extend such resource-efficient mitigation to arbitrary observables, we generalize and integrate a hardware-efficient measurement transformation framework that converts high-weight Pauli operators into low-weight effective ones via linear-depth circuits. The circuit noise introduced during this transformation is jointly mitigated with native measurement noise using a unified twirling protocol, ensuring robust performance. Extensive numerical simulations demonstrate a greater than 16-fold improvement in sampling efficiency over conventional random twirling, with consistent performance gains across varying system sizes and error regimes. This work provides a resource-frugal and experimentally viable path toward high-fidelity measurement in near-term quantum devices.

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• Quantum algorithms & computation