AI & Machine Learning

Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design

25 sec read39 views0 listens

Researchers propose Reinforcement Learning for Variational Quantum Circuits (RLVQC) to separate ansatz discovery from deployment in quantum circuit design, enabling the use of modular circuit blocks learned on small systems and applied to larger problems. This approach advances practical applications of quantum computing by overcoming scalability issues with classical machine learning methods as qubit numbers increase.

Read the full article at arXiv cs.LG (ML)

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.

Build Your Own Lab: Virtualization for DevOps Beginners

Virtualization for DevOps Beginners Virtual Machines (VMs) are essential tools in the DevOps toolkit, allowing developers and operations teams to create isolated environments that mimic production systems on their local machines. This article will gu...

Ali Nemati

AI & Machine LearningMar 3025 sec read

Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation

Researchers have developed concurrent training methods for Kolmogorov-Arnold networks (KANs) using Newton-Kaczmarz algorithms, achieving over 40 times faster training compared to traditional neural networks on CPUs while maintaining accuracy. This ad...

Ali Nemati

AI & Machine LearningMar 527 sec read

YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency

YuanLab AI released Yuan 3.0 Ultra, a multimodal MoE model that achieves state-of-the-art performance while reducing total parameters by 33.3% and boosting pre-training efficiency by 49%. Key innovations include Layer-Adaptive Expert Pruning (LAEP) f...

Ali Nemati

AI & Machine LearningJan 820 sec read

Delivering Massive Performance Leaps for Mixture of Experts Inference on NVIDIA Blackwell

NVIDIA announced significant performance improvements for Mixture of Experts inference on its Blackwell platform, enhancing token throughput per watt. This advancement is crucial for content creators as it reduces costs and improves efficiency in AI ...

Ali Nemati

AI & Machine LearningMay 15, 202540 sec read

DeepSeek-V3 New Paper is coming! Unveiling the Secrets of Low-Cost Large Model Training through Hardware-Aware Co-design

The article discusses DeepSeek's approach to designing and training a large-scale MoE model called DeepSeek-V3 on NVIDIA H800 GPUs. Key strategies include hardware-aware parallelization techniques (avoiding Tensor Parallelism, enhancing Pipeline Para...

Ali Nemati

Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design

Related Articles

Build Your Own Lab: Virtualization for DevOps Beginners

Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation

YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency

Delivering Massive Performance Leaps for Mixture of Experts Inference on NVIDIA Blackwell

DeepSeek-V3 New Paper is coming! Unveiling the Secrets of Low-Cost Large Model Training through Hardware-Aware Co-design