As this is scaled up, it has the potential to significantly broaden the range of problems to which quantum computers can be applied. Imagine a traditional computer that could only run computations of 20 computation steps, versus one that could run arbitrary length programs.
INNOVATION
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Vision-Language Models Achieve SOTA Video Classification Performance
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We use pre-trained vision-language models to improve video classification, achieving SOTA performance on Kinetics-400 and surpassing previous methods by 20-50% on five popular video datasets. #AAAI2023 Github: https://
github.com/whwu95/Text4Vis -
Scaling quantum error correction for longer computations
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With further scaling, the error rates on "logical" qubits can hopefully be driven down sufficiently that we can run arbitrary-length quantum computations on a quantum computer, instead of ones with a limited number of steps.
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LandingLens: Computer Vision Projects Now Completed in Days
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3/But building and deploying ML systems is hard. After years of work, I’m thrilled to finally offer LandingLens to everyone. Computer vision projects that used to take me a year are now done in days/weeks on LandingLens. Please try it out and also help me spread the word!
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Democratizing AI: Custom ML Models for Everyone’s Data
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2/We’re on a mission to democratize access to AI creation, so everyone can easily train and deploy custom ML models on their own data. Every business and every person now has their own data, so using only models that someone else had trained on their data is not good enough.
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Landing AI Releases Free Computer Vision Tool for Image Labeling and Model Deployment
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1/Landing AI is releasing our computer vision tool for anyone to use! You can label images, train a model, and deploy your model to production in minutes. Our data-centric tool then helps you improve your model quickly. Start for free at https://
landing.ai -
Physical Qubits Combined Into Noise-Resistant Logical Qubits
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What this work shows is that it is possible to group together many physical qubits and use all of their states to make a "logical" qubit, whose state is significantly less noisy (see the discussion in the blog post on surface codes, phase-flip errors and bit-flip errors).
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Google Demonstrates Logical Qubit Prototype for Fault-Tolerant Computing
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Developing useful large-scale quantum computers will require qubits with much lower error rates. Our latest results, published in @Nature
, demonstrate a prototype of a logical qubit, a milestone on the path toward scalable fault-tolerant quantum computing: https://
goo.gle/3KvE25V -
Physical Qubits Noise Limits Quantum Computation Steps
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The reason this is important is that single physical qubits are noisy, and on a computation running on physical qubits, this probability of noise accumulates over time, and therefore you are limited to running programs that are no more than a modest handful of computation steps.
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Google Quantum AI Achieves Logical Qubit Error Correction Breakthrough
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Our @GoogleQuantumAI team has made an important step towards development of a large-scale quantum computer: building a prototype logical qubit & showing that quantum error correction makes that logical qubit better as it gets bigger. Congrats to the team! https://
ai.googleblog.com/2023/02/suppre
ssing-quantum-errors-by-scaling.html
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