arXiv:2607. 23645v1 Announce Type: cross Abstract: Communication complexity provides a natural framework for quantifying the classical resources required to reproduce quantum statistics.
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arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
arXiv:2512. 01317v3 Announce Type: replace-cross Abstract: Measurement-induced entanglement (MIE) captures how local measurements generate long-range quantum correlations and drive dynamical phase transitions in many-body systems.
The paper investigates quantum measurement PAC learning, where a learner selects a measurement from a hypothesis class to predict classical labels of new quantum states. It demonstrates that learning rules based on entangled measurements can outperform single‑copy (LOCC) learning rules, showing that single‑copy rules are asymptotically suboptimal in certain scenarios. The authors further prove that entangled‑measurement rules offer at most a polynomial improvement in sample complexity over single‑copy rules under a natural joint measurability assumption.
The paper investigates how postprocessing routines in quantum neural network software can inadvertently discard a large portion of valid measurement data when run on real quantum hardware. In a case study of Qiskit’s “SamplerQNN”, a filter that assumes virtual qubit space caused 85–99.6% of measurement shots to be lost on IBM backends, leading to unnormalised probability vectors, degraded inference accuracy (from 0.94 to 0.39), and a 22–27× compression of the training loss signal. The authors provide a layout‑based marginalisation fix that has been merged into the library to ensure forward‑compatibility with current and future hardware.
The paper investigates how postprocessing routines in quantum neural network software can cause significant data loss when run on large quantum hardware. In a case study of Qiskit’s “SamplerQNN”, a filter that assumes measurement bit‑strings are in virtual qubit space removed 85–99.6% of valid shots on IBM backends, leading to unnormalised probability vectors and distorted predictions. This loss caused inference accuracy to drop from 0.94 to 0.39 and compressed training loss signals by 22–27×, severely reducing optimizer sensitivity. The authors implemented a layout‑based marginalisation fix that was merged into the library to make “SamplerQNN” forward‑compatible with current and future hardware.