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Posts with #QuantumMachineLearning

#QuantumMachineLearning

Public posts on X that use #QuantumMachineLearning.

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  1. Jens Eisert✓@jenseisert
    Single-shot quantum machine learning This work brings together notions of quantum machine learning with those of quantum metrology. In detail, #quantummachinelearning aims to improve learning methods through the use of quantum computers. If it is to ever realize its potential, many obstacles need to be overcome. A particularly pressing one arises at the prediction stage because the outputs of quantum learning models are inherently random. This creates an often considerable overhead, as many executions of a quantum learning model have to be aggregated to obtain an actual prediction. In this work, we analyze when quantum learning models can evade this issue and produce predictions in a near-deterministic way—paving the way to single-shot quantum machine learning. We give a rigorous definition of #singleshotness in quantum classifiers and show that the degree to which a quantum learning model is near deterministic is constrained by the distinguishability of the embedded quantum states used in the model. Opening the black box of the embedding, we show that if the embedding is realized by quantum circuits, a certain depth is necessary for single shotness to be even possible. We conclude by showing that quantum learning models cannot be single shot in a generic way and trainable at the same time. Warm thanks to @jj_xyz and Erik Recio Armengol.
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  3. Jens Eisert✓@jenseisert
    The exploitation of #symmetry is much at home in theoretical physics, specifically in quantum many-body theory. Here, we explore how symmetry can be made use of in variational #quantummachinelearning, where symmetries can also enter on the data level.
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  9. Jens Eisert✓@jenseisert
    Are the output distributions of #quantumcircuits actually #learnable? We systematically look at this question, within two different oracle models, presenting a positive and a negative result.
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    116115
  10. Jens Eisert✓@jenseisert
    @jenseisert
    As a positive result, show that in a more powerful oracle model, namely when directly given access to samples, the output distributions of local Clifford circuits are computationally efficiently PAC learnable by a classical learner.
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  13. Anjali✓@AnjaliOnGaia
    Light is already the Universal Basic Language — it’s our very own Universal Information Network WiFi Service. #DARPA #NHI #QuantumMachineLearning #Ai
    Mehdi (e/λ) @BetterCallMedhi
    I believe DARPA just launched the most audacious bioengineering program of the decade & nobody’s talking about it loud enough I spent hours reading through the Generative Optogenetics program documentation & honestly I’m still processing the implications of what they’re trying to build this is about creating molecular machines that can be expressed in living cells to translate optical signals directly into DNA and RNA sequences on demand think about what this means…. you shine specific wavelengths of light at a cell & it writes its own genetic code in realtime without any external DNA delivery no viral vectors no CRISPR cassettes just light triggering nucleotide incorporation based on photon wavelength this is a FOUNDATIONAL shift in how we interface with biology bc right now if you want to deliver genetic instructions to cells you need massive supply chains synthesis facilities cold storage delivery mechanisms that break down over distance DARPA GO wants to turn living cells into programmable biological systems where you can transmit genetic information massively via optical signals & the cell synthesizes the exact sequence you need using its own machinery the implications are absolutely wild : single-cell spatial resolution temporal precision to deliver different messages sequentially remote scalable dissemination of genetic instructions Imagine regenerative medicine where you shine light patterns on damaged tissue & cells reprogram themselves in situ manufacturing where bacterial cultures synthesize complex molecules on command via optical programming agriculture where crops adapt their genome in response to environmental light signals… what fascinates me most is that this high risk program acknowledges we’re building a direct interface between computers that design genetic sequences and living cells that execute them we’re not just editing genes anymore we’re creating a bidirectional communication protocol between silicon & carbon where light becomes the universal language If GO succeeds it unlocks programmable biology at a SCALE we’ve never seen before this is how you get to foundational capabilities for extended spaceflight for on demand therapeutics for manufacturing without brittle supply networks this is the kind of moonshot that changes EVERYTHING
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  14. Mehdi (e/λ)✓@BetterCallMedhi
    I believe DARPA just launched the most audacious bioengineering program of the decade & nobody’s talking about it loud enough I spent hours reading through the Generative Optogenetics program documentation & honestly I’m still processing the implications of what they’re trying to build this is about creating molecular machines that can be expressed in living cells to translate optical signals directly into DNA and RNA sequences on demand think about what this means…. you shine specific wavelengths of light at a cell & it writes its own genetic code in realtime without any external DNA delivery no viral vectors no CRISPR cassettes just light triggering nucleotide incorporation based on photon wavelength this is a FOUNDATIONAL shift in how we interface with biology bc right now if you want to deliver genetic instructions to cells you need massive supply chains synthesis facilities cold storage delivery mechanisms that break down over distance DARPA GO wants to turn living cells into programmable biological systems where you can transmit genetic information massively via optical signals & the cell synthesizes the exact sequence you need using its own machinery the implications are absolutely wild : single-cell spatial resolution temporal precision to deliver different messages sequentially remote scalable dissemination of genetic instructions Imagine regenerative medicine where you shine light patterns on damaged tissue & cells reprogram themselves in situ manufacturing where bacterial cultures synthesize complex molecules on command via optical programming agriculture where crops adapt their genome in response to environmental light signals… what fascinates me most is that this high risk program acknowledges we’re building a direct interface between computers that design genetic sequences and living cells that execute them we’re not just editing genes anymore we’re creating a bidirectional communication protocol between silicon & carbon where light becomes the universal language If GO succeeds it unlocks programmable biology at a SCALE we’ve never seen before this is how you get to foundational capabilities for extended spaceflight for on demand therapeutics for manufacturing without brittle supply networks this is the kind of moonshot that changes EVERYTHING
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    2989383.7K500K
  16. Nathan Benaich✓@nathanbenaich
    💥We're live with @stateofai 2020💥 For the 3rd year, @soundboy and I spent 2 months analyzing the most interesting developments in AI. We aim to trigger an informed conversation about the #stateofai and its implication for the future. Thread 👇
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  17. Rahko@rahkoAI
    We had a blast filming this short video for #LondonTechWeek with @merckgroup in London and Darmstadt! Our work in unlocking discovery with #quantummachinelearning is only made possible through our close partnerships and collaborations. #QuantumComputing
    London Tech Week @LDNTechWeek
    Today's Tech in Action video explorers Collaboration in Quantum @rahkoAI is a #quantum machine learning startup that models drugs faster, cheaper and with far greater precision, in partnership with leading quantum manufacturers 💊 Learn more 👉🏻
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  18. London Tech Week@LDNTechWeek
    Today's Tech in Action video explorers Collaboration in Quantum @rahkoAI is a #quantum machine learning startup that models drugs faster, cheaper and with far greater precision, in partnership with leading quantum manufacturers 💊 Learn more 👉🏻
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  23. Google AI✓@GoogleAI
    Today we present the first demonstration of a provable exponential advantage in #QuantumMachineLearning over classical algorithms that is robust even on today's noisy hardware. Learn all about it at
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