Chu Lahlou

💥💥💥 Crafting Qubits: Harnessing Quantum Mechanics for Computation How do we actually create and manipulate qubits, essential for realizing quantum computation? Chief Scientist of Hardware Technology Development at Quantinuum, Patty Lee, joins Brian Greene to discuss various quantum strategies, their achievements to date and pathways forward. This program is part of the Big Ideas series, supported by the John Templeton Foundation. Participant: Patty Lee Moderator: Brian Greene 00:00 - Introduction 01:51 - Participant Introduction 02:44 - Approaches To Quantum Computing 07:19 - The Trapped Ion Approach In Practice 22:58 - Obstacles In Quantum Computing 35:14 - The Future Of Quantum Computing 40:10 - Credits 👉 https://lnkd.in/dvyXVKaf #quantumcomputing

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The final chapter of my book is up in Manning Early Access Program (MEAP)! Chapter 9 wraps up the discussion of solving classical problems using quantum algorithms. It tackles an important topic: how do you assess the performance of a quantum algorithm to compare it to that of a classical solution? Comparing the asymptotic behavior of the algorithms in terms such as the number of oracle calls for search problems is not enough! This weekend, Manning Publications Co. is running Thanksgiving sales with great discounts, so it's a good time to pick up a copy of the book for your holiday break reading! https://lnkd.in/gwnke93p

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Interesting findings, but I'm not sure they do a great job of defining QRC. See the link in my first comment with a better definition and citation. #quantumcomputing https://lnkd.in/g4n8xHct

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In summary: The performance of quantum computers compared to classical ones can be likened to a race, where winning depends on both hardware speed and algorithms. Classical computers generally have faster hardware, but quantum computers sometimes possess vastly superior algorithms. This can be compared to a ship's speed (hardware) and its route (algorithm). Classical computers excel in general tasks, but quantum computers can outperform in specific cases due to unique algorithms, such as Shor’s algorithm in cryptography. When quantum computers do not have better algorithms, classical computers will dominate. However, when quantum algorithms are superior, a framework called "Quantum Economic Advantage" is proposed. This framework suggests that quantum economic advantage occurs when quantum computers can solve a problem more efficiently than any similarly priced classical computer. To achieve this, the quantum computer must be powerful enough, and its algorithmic benefit must outweigh the speed advantage of classical computers.

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Did you check out Google's latest quantum processor, Willow? It performed a standard benchmark computation in under five minutes that would take one of today's fastest supercomputers 10 septillion (that is, 1025) years — a number that vastly exceeds the age of the Universe(https://lnkd.in/g-Ccse_s ) This technology has the potential to revolutionize fields like pharmaceuticals, energy, and materials science, solving problems that are currently beyond our reach. I'm particularly excited and curious to see how Willow will impact the future of AI . Imagine Models that can be trained faster and with greater efficiency, leading to new breakthroughs in natural language processing, computer vision, and more. Must watch 6 min Video :https://lnkd.in/g64a5Eyz #quantumcomputing #AI #LLM #innovation"

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Join the ATARC launch of the Generative AI Working Group with a virtual meeting on Wednesday June 26th at 12:00 PM EST! Please email the working group team at workinggroups@atarc.org to receive an invitation. Join us at our Generative AI Working Group kickoff meeting, where we seek to leverage industry and government experience with the procurement and deployment of Generative AI capabilities to mitigate business risk, improve employee productivity, and enhance the quality of the customer experience of #government services. The working group will explore four areas in the Generative #AI landscape:(1) Governance, (2) Human Capital, (3) Procurement, Planning, and Piloting, and (4) Effective Use of Generative AI. Our working group is lead by: -Government Chair: Frank Indiviglio, NOAA -Government Vice Chair: Nathan Manzotti, GSA -Carahsoft Chair: Michael Adams More information about the working group can be found here - https://ow.ly/SKtY50S84iQ We are looking forward to having you join us!

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Just published in #RSC #DigitalDiscovery: our DIEP, which is our attempt to make physics-informed ML more "physics-y" https://lnkd.in/g45ZPAbQ

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Have a look at the new blog about improving quantum optimisation efficiency!

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What an end to year 2024, OpenAI o3 has achieved state of the art results on ARC-AGI benchmarks. From ARC-AGI's blog (https://lnkd.in/gKw3bU4U): "To sum up – o3 represents a significant leap forward. Its performance on ARC-AGI highlights a genuine breakthrough in adaptability and generalization, in a way that no other benchmark could have made as explicit. o3 fixes the fundamental limitation of the LLM paradigm – the inability to recombine knowledge at test time – and it does so via a form of LLM-guided natural language program search. This is not just incremental progress; it is new territory, and it demands serious scientific attention." This is indeed a significant leap and in 2025, I expect few things: * OpenAI, Google and others will keep pushing the boundaries with what we can do with reasoning models, models will keep getting better - we have not yet reached the ceiling. * We will see openweights models as well which will be comparable to o3 (we already have few going in that direction like QwQ, Deepseek - both from China). * Products will make an accelerated effort for catching up both of these: https://lnkd.in/gRBuFMGi

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The reduction of matrix multiplies in LLMs is one of the most interesting areas of research right now because it enables much cheaper deployment of acceleration solutions ranging from FPGA to mobile to ARM based system accelerators. This paper, "Scalable MatMul-free Language Modeling" is going to be talked about a lot this month because it provides a demonstration for creating a scalable Matrix Multiplication Free LLM that performs as well as a Matrix Multiplication Heavy LLM. Cutting Matrix Multiplies reduces reliance on extremely expensive Nvidia GPUs and lower costs equals greater opportunity for dispersion of use. https://lnkd.in/eXMmgjMu

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"Reproducibility in Machine Learning" is the theme of our Symposium (Tuesday, 5/21), hosted by CORES, the Center for Open and Reproducible Science at Stanford University. We have a great line up, a great topic, and we hope you can join us! Agenda and registration details here: https://lnkd.in/gZX-3-uF Friends, if you are coming let me know, maybe we can grab lunch! I can fill you in on what it takes to filter through 50+ highly technical open source projects and try to figure out which one deserves to win a prize ... and other bad ideas I've had recently 😅. #openscience #machinelearning #science #ai #bayarea

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Yesterday, Google introduced Willow, a groundbreaking quantum computing chip that significantly improves both error correction and computational speed— bringing us another step closer to practical quantum computers. This Google blog post from Hartmut Neven is a great summary of how impactful this is: https://lnkd.in/g4SdDWZZ

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Interesting to compare Pre and post deployment test results..AISI...

Pathways to Tech 13!! I had the pleasure of attending a session hosted by Yasin Ehsan 🚀 and Headstarter AI yesterday, which was an incredible experience. Here are the key takeaways from the meeting: Dustin Beadle, Director at Lattice, and former Director at AMD, shared valuable insights on essential soft skills and the values necessary to excel in job interviews. Logan Havern, CEO of DataLogz, who has raised $12M, provided guidance on how to pitch oneself effectively and emphasized the importance of taking initiative to solve problems. Nabeel Alamgir🦄, CEO of LunchBox, with $100M in VC funding, stressed the importance of leadership qualities and the value of being original and genuine. Shariar Kabir & Miguel Acero from Ruby Card, YC W23, discussed the lessons learned from failure and the resilience needed during challenging times. They also highlighted the educational benefits of participating in hackathons. This session underscored the importance of strategic thinking, and I am eager to apply these insights in the future. It was truly an enlightening experience.

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💡 ByteDance's Latest Work on Neural Network-Based Molecular Force Fields 💡 I recently read ByteDance’s work on developing a new force field, and it’s pretty good! 👍 ByteFF is a molecular mechanics force field trained on a massive dataset: 2.4 million geometries and 3.2 million torsion profiles. It leverages GNN to predict bonded and non-bonded parameters, showing strong performance in predicting molecular geometry, torsion energy, and conformational energy. This model has great potential for drug discovery and molecular simulation. I do have some concerns about using torsion scan + hessian methods to train non-bonded parameters in this framework. Non-bonded interactions often involve long-range effects, which are challenging for GNNs to capture accurately. That said, the overall results presented in this work are still quite promising. It's exciting to see tech giants like ByteDance stepping into AI for Science, and their vast resources enable them to support DFT calculations on this massive scale. (🤞 Hopefully, they will release the dataset for open use, LOL!) 👉 You can find the paper here: https://lnkd.in/gjmGV4fd 👉 And check out their open-source framework on GitHub, which is well-engineered: https://lnkd.in/gkxcTi6t #AIforScience #DrugDiscovery #MolecularModeling #ForceField #ByteDance

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Bryan Castle, Director of AI Engineering at #BoozAllen, discusses how open source #AI models offer transparency, allowing users to gain a deeper understanding of a model’s capabilities, strengths, and potential vulnerabilities. https://lnkd.in/gmDt9T_U

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