·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
TikTok is testing an AI likeness detection tool38m◆How Apple’s big lawsuit could disrupt OpenAI’s IPO plans2h◆Apple’s plot to crush OpenAI2h◆Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers4h◆Patreon stops asking AI bots not to scrape — and starts blocking them4h◆Apple’s lawsuit couldn’t come at a worse time for OpenAI6h◆Why the first GPU financiers are turning to inference chips in a $400 million deal8h◆A scorecard for the AI age10h◆The risk of weather data sabotage is rising11h◆The Steering Budget: Examples beat Knobs16h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs16h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination16h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models16h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation16h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System16h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets16h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility16h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation16h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks16h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios16h◆TikTok is testing an AI likeness detection tool38m◆How Apple’s big lawsuit could disrupt OpenAI’s IPO plans2h◆Apple’s plot to crush OpenAI2h◆Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers4h◆Patreon stops asking AI bots not to scrape — and starts blocking them4h◆Apple’s lawsuit couldn’t come at a worse time for OpenAI6h◆Why the first GPU financiers are turning to inference chips in a $400 million deal8h◆A scorecard for the AI age10h◆The risk of weather data sabotage is rising11h◆The Steering Budget: Examples beat Knobs16h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs16h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination16h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models16h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation16h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System16h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets16h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility16h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation16h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks16h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios16h◆
News/An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor
arxiv
PublishedJuly 17, 2026 at 4:00 AM
—neutral

An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.23743v2 Announce Type: replace-cross Abstract: Variational quantum circuits train poorly on chaotic forecasting, usually blamed on barren plateaus (exponentially vanishing gradients). Using an exactly simulable four-qubit variational quantum physics-informed circuit fit to Lorenz-63, we s

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivThe Steering Budget: Examples beat Knobs16harxivPolestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs16harxivRxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination16harxivWhen a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models16h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews