Using microwave pulses to plug leaks in quantum computers makes them more reliable

Scientists have developed a new approach to correcting common quantum computing errors, which could pave the way for more ...
InfoQ

QConAI NY 2025 - Designing AI Platforms for Reliability: Tools for Certainty, Agents for Discovery

Aaron Erickson at QCon AI NYC 2025 emphasized treating agentic AI as an engineering challenge, focusing on reliability ...
GitHub

pytorch-quantization installation problem

I was trying to install version 1.5 following the steps mentioned. But getting errors in NeuroSim/pytorch-quantization$ pip install -e . step. Also, in the Readme inside the pytorch-quantization ...
GitHub

How to perform qat quantization for secondary classification of deepstream 7.0, pytorch-quantization has problems!

I want to use pytorch-quantization to perform classification quantization of Deepstream7, which can be done normally in deepstream6. The process is to use torch-tensorrt==1.4.0 and ...
IEEE

Specifying Quantization Characteristics for Required Lifetime Prognostic Performance

Abstract: The accuracy of lifetime prediction is significantly influenced by the quality of condition monitoring (CM) data, and both forward and reversed problems related to this issue have been ...
IEEE

Optimal Transport Quantization Based on Cross-X Semantic Hypergraph Learning for Fine-Grained Image Retrieval

Abstract: Large-scale fine-grained image retrieval aims to learn compact discriminative feature representations based on mining the subtle distinctions between visually similar objects. However, ...
marktechpost

VQ4DiT: A Fast Post-Training Vector Quantization Method for DiTs (Diffusion Transformers Models)

Text-to-image diffusion models have made significant strides in generating complex and faithful images from input conditions. Among these, Diffusion Transformers Models (DiTs) have emerged as ...
marktechpost

LLM-QFA Framework: A Once-for-All Quantization-Aware Training Approach to Reduce the Training Cost of Deploying Large Language Models (LLMs) Across Diverse Scenarios

Large Language Models (LLMs) have made significant advancements in natural language processing but face challenges due to memory and computational demands. Traditional quantization techniques reduce ...