Speaker
Description
Classical supervised learning from quantum measurements can extract useful physical information. However, regimes of interest often lack reliable labels and involve imperfect state preparation, while labels may be available only in easier regimes with different distributions. Can unlabeled measurement data from imperfectly prepared states be combined with these labeled data to learn an accurate predictor?
We address this question by constructing a practical end-to-end unsupervised domain adaptation (UDA) pipeline using Pauli classical shadows and conditional domain-adversarial learning. It requires neither labels in the regime of interest nor post-measurement quantum processing. Numerical studies and a 144-qubit real-device demonstration answer this question positively.
In numerical simulations, it considerably improves phase classification in the cluster model and ANNNI model using clean labeled data from restricted parameter regions and imperfect unlabeled data from broader regions. It also improves classification of separable and entangled states when data quality and partitions defining separability differ.
Our key result is a 144-qubit demonstration on the IBM Quantum processor ibm_kawasaki, classifying topological and trivial phases on a rotated surface code. The learner receives only unlabeled shadows of states generated and measured on real quantum hardware using non-Clifford random circuits, but can classically generate labeled measurement data for analogous Clifford-circuit examples. Training only on labeled simulated data fails on the hardware data (macro-F1 0.322), whereas UDA achieves 0.958 without hardware-data labels. This success with real experimental quantum data at an application-relevant scale motivates applying UDA to extend the practical reach of imperfect experimental quantum data when reliable labels are unavailable.
| Topic | Machine learning techniques for experimental quantum information science |
|---|---|
| Additional Topic | Encoding and processing of data in quantum systems |