arXiv AI
Jul 2
When AI meets quantum information: A comprehensive review
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
Neural Network Learning of One-Bit Protocols for Qubit Measurement Simulation
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 Machine Learning
Jul 30
Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
arXiv AI
Jul 13
Data-Driven Learnability Transition of Measurement-Induced Entanglement
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.
arXiv Machine Learning
Oct 7
Advantage of Entangled Learning Rules in Quantum Measurement Class Learning
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.
Hugging Face Trending Papers
Sep 4
Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks
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.
arXiv Machine Learning
Sep 7
Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks
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.
arXiv AI
Aug 7
One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization
arXiv:2608. 05240v1 Announce Type: cross Abstract: One-bit post-training quantization represents each weight using only its sign, requiring all deployment contexts to share the same binary weight matrix even when their activation statistics favor different sign patterns.
arXiv Machine Learning
Sep 25
Learning and interpreting policies for simultaneous entanglement requests in quantum networks
The paper presents a reinforcement‑learning approach to schedule link‑level entanglement in quantum networks, using a Markov Decision Process and double deep Q‑networks with message‑passing neural networks. The resulting policies achieve 100% success rates even when the link activation probability is reduced by up to 71% compared to baseline heuristics, and maintain at least 80% success when task placements are hardware‑restricted. The authors also develop metrics to interpret the learned policy and employ a large language model to generate a heuristic that matches the DQN performance, suggesting a scalable method for extracting interpretable strategies in large quantum networks.
arXiv Machine Learning
Jun 26
Tailor Made Embeddings for Quantum Machine Learning
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.
Hugging Face Trending Papers
Oct 13
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements
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 noi...
arXiv AI
Aug 12
Regression and Classification with Single-Qubit Quantum Neural Networks
arXiv:2412. 09486v2 Announce Type: replace-cross Abstract: The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other.
arXiv Machine Learning
Jun 19
Entropy Estimation in Multi-Qutrit Systems via Variational and Classical Neural Networks
arXiv:2606. 20504v1 Announce Type: cross Abstract: We present a systematic study of von Neumann entropy estimation in multi-qutrit quantum systems using two complementary approaches: variational quantum algorithms (VQAs) and classical convolutional neural networks (CNNs), evaluated using an ideal (noise-free) quantum simulator.