# Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements

arXiv: https://arxiv.org/abs/2610.18482 · HTML: https://mlsanity.com/paper/2610.18482/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 2 of 2)

- [Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures](https://mlsanity.com/paper/2212.03895/) (2022)
- [A Compilation Framework for Quantum Circuits with Mid-Circuit Measurement Error Awareness](https://mlsanity.com/paper/2511.10921/) (2025)

## Abstract (arXiv)

Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependent control, but their reliability is limited by noise on superconducting hardware. These operations depend on accurate quantum state discrimination (QSD), which assigns measured signals to state labels. Neural network-based (NN-based) QSD improves this assignment, yet its error remains bounded below by the Bayes risk of the available readout distributions. A complementary challenge is state deterioration as successive measurements accumulate and circuit execution advances, making static calibration less representative of the workload. We present Lifting, a QSD framework that addresses both challenges through repetition encoding, neural decoding, and in situ calibration. Encoding supplies joint in-phase and quadrature (I-Q) readout signals from the target and auxiliary qubits, while contextual calibration trains the decoder for the conditions encountered during execution. Our theoretical analysis shows that retaining additional channels from a fixed joint distribution cannot increase Bayes risk and establishes sufficient conditions for contraction of an overlap-based upper bound. Experiments across three IBM processors evaluate the framework on repeated stabilizer measurement, parity-check, and teleportation workloads.