DeeperML - #quantumai

AI research is rapidly advancing, with new tools and techniques emerging regularly. Johns Hopkins University and AMD have introduced 'Agent Laboratory', an open-source framework designed to accelerate scientific research by enabling AI agents to collaborate in a virtual lab setting. These agents can automate tasks from literature review to report generation, allowing researchers to focus more on creative ideation. The system uses specialized tools, including mle-solver and paper-solver, to streamline the research process. This approach aims to make research more efficient by pairing human researchers with AI-powered workflows.

Carnegie Mellon University and Meta have unveiled a new method called Content-Adaptive Tokenization (CAT) for image processing. This technique dynamically adjusts token count based on image complexity, offering flexible compression levels like 8x, 16x, or 32x. CAT aims to address the limitations of static compression ratios, which can lead to information loss in complex images or wasted computational resources in simpler ones. By analyzing content complexity, CAT enables large language models to adaptively represent images, leading to better performance in downstream tasks.


References :

• pub.towardsai.net: Build your own personalized Fitness RAG Agent using Python!
• THE DECODER: AI agents team up in Agent Laboratory to speed scientific research
• www.marktechpost.com: Content-Adaptive Tokenizer (CAT): An Image Tokenizer that Adapts Token Count based on Image Complexity, Offering Flexible 8x, 16x, or 32x Compression

Classification:

• HashTags: #AIResearch #MachineLearning #AgenticAI
• Feature: AI Research
• Type: Research
• Severity: Informative

Recent developments highlight advancements in quantum computing, artificial intelligence, and cryptography. Classiq Technologies, in collaboration with Sumitomo Corporation and Mizuho-DL Financial Technology, achieved up to 95% compression of quantum circuits for Monte Carlo simulations used in financial risk analysis. This project explored the use of Classiq’s technology to generate more efficient quantum circuits for a novel quantum Monte Carlo simulation algorithm incorporating pseudo-random numbers proposed by Mizuho-DL FT, evaluating the feasibility of implementing quantum algorithms in financial applications.

Oxford researchers demonstrated a fast, 99.8% fidelity two-qubit gate using a simplified circuit design, achieving this using a modified coaxmon circuit architecture. Also, a collaborative team from JPMorganChase, Quantinuum, Argonne National Laboratory, Oak Ridge National Laboratory, and the University of Texas at Austin demonstrated a certified randomness protocol using a 56-qubit Quantinuum System Model H2 trapped-ion quantum computer. This is a major milestone for real-world quantum applications, with the certified randomness validated using over 1.1 exaflops of classical computing power, confirming the quantum system’s ability to generate entropy beyond classical reach.

The 2025 IEEE International Conference on Quantum Artificial Intelligence will be held in Naples, Italy, from November 2-5, 2025, with a paper submission deadline of May 15, 2025. Vanderbilt University will host a series of workshops devoted to Groups in Geometry, Analysis and Logic starting May 28, 2025.


References :

• computational-intelligence.blogspot.com: 2025 IEEE International Conference on Quantum Artificial Intelligence
• computational-intelligence.blogspot.com: The paper submission deadline for the is 15 May 2025 . This conference will be held in , Italy, 2-5 November 2025.

Classification:

• HashTags: #QuantumAI #Cryptography #MathResearch
• Company: University
• Target: Advancement
• Product: Cryptography
• Feature: Conference
• Type: Research
• Severity: Major