AI Could Finally Make Quantum Computers Work

00:07:01
https://www.youtube.com/watch?v=1aBfNMisTbE

Resumo

TLDRThe video explores the synergy between artificial intelligence (AI) and quantum computing, specifically highlighting a new approach called 'atoms in tweezers.' It addresses the current limitations of quantum computers, such as high error rates and size constraints, and how AI has recently made significant strides in error correction. The speaker discusses Google's AI advancements and the potential of neutral atoms for scalable quantum computing. The underlying research implications are also touched upon, posing questions about the randomness of quantum mechanics and its relationship with AI's error correction performance. Furthermore, viewers are introduced to educational resources on quantum mechanics and AI via the platform Brilliant.

Conclusões

  • 🤖 AI is enhancing the performance of quantum computing.
  • 🔍 The 'atoms in tweezers' method shows great potential for future applications.
  • 📈 Error rates can be reduced with AI techniques like Google's Alpha cubid.
  • 🔌 Quantum computers need to scale in size while managing errors.
  • 🚀 Investment in quantum technology is driven by its financial and practical implications.

Linha do tempo

  • 00:00:00 - 00:07:01

    The video discusses a lesser-known approach to Quantum Computing that could rapidly advance due to advancements in artificial intelligence (AI). It highlights how AI is currently outperforming other methods in controlling quantum errors. The speaker emphasizes the growing interest in Quantum Computing, driven largely by its potential financial applications, particularly in stock market optimization. A new AI method called Alpha cubid from Google's AI group is noted for significantly reducing error rates in superconducting qubits. Meanwhile, the atoms in tweezers approach shows promise for scalability in building practical quantum computers, as AI is being utilized to arrange atoms systematically. There is an ongoing exploration into whether quantum mechanics is truly random, and how AI might challenge existing beliefs about error correction beyond theoretical limits.

Mapa mental

Vídeo de perguntas e respostas

  • What is the main focus of the video?

    The video focuses on how AI can enhance quantum computing, particularly through the 'atoms in tweezers' approach.

  • What challenges does quantum computing face?

    Quantum computing faces challenges such as high error rates and the need for larger, more efficient quantum devices.

  • What recent advancements in AI are mentioned?

    Recent advancements include AI algorithms like Google's Alpha cubid, which improve error correction in quantum computations.

  • What is the 'atoms in tweezers' approach?

    It is a method that uses electromagnetic fields to trap and organize atoms for quantum computations.

  • What is the significance of neutral atoms in quantum computing?

    Neutral atoms are believed to hold promise for practical quantum applications due to their scalability.

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  • 00:00:00
    my videos about new trends always go
  • 00:00:03
    badly I think it's because if something
  • 00:00:06
    isn't much talked about already it
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    doesn't seem all that interesting but it
  • 00:00:11
    makes me so proud if I can later say see
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    I've been talking about this for years
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    today's video is one of those I want to
  • 00:00:19
    explain why a currently relatively
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    unknown approach to Quantum Computing
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    that hasn't gotten very far yet might
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    pull ahead very quickly thanks to
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    artificial intelligence artificial
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    intelligence and Quantum Computing are
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    the two currently most exciting
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    technological developments we've
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    previously discussed how they're
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    competing and how AI is winning so far
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    but in the past year we've seen another
  • 00:00:45
    Trend that's using AI to make quantum
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    computers work this has recently been
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    applied to an approach called atoms in
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    tweezers with remarkable success Quantum
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    Computing has been heralded and the next
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    Industrial Revolution and the future of
  • 00:01:03
    computing a technology that can
  • 00:01:05
    allegedly do anything from curing cancer
  • 00:01:08
    to fixing climate change to resurrecting
  • 00:01:10
    Elvis Presley I made up the last one but
  • 00:01:13
    the way things are going I wouldn't be
  • 00:01:15
    surprised if that would be next week's
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    headline what is true though is that
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    quantum computers could vastly speed up
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    the calculations necessary for financial
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    optimizations that means you could use
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    them to make money on the stock market
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    Market loads of money and that I think
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    explains a lot of the interest in
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    Quantum Computing it's no coincidence
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    that Wells Fargo City group and HSBC are
  • 00:01:42
    investing in Quantum Computing at himim
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    Israel from Bank of America
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    enthusiastically proclaimed that Quantum
  • 00:01:49
    Computing will be bigger than fire it's
  • 00:01:52
    not because he wants to cure cancer or
  • 00:01:54
    fixed climate change it's because Shing
  • 00:01:56
    us cat is more interesting when it's
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    made of gold the trouble with Quantum
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    Computing is that the current devices
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    are far away from the sizes necessary to
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    do any commercially relevant
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    calculations and the problem isn't just
  • 00:02:10
    to make them bigger the problem is to
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    make them bigger while keeping the
  • 00:02:15
    number of Errors down at least this used
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    to be the case but artificial
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    intelligence is now getting so good it's
  • 00:02:22
    learning to control the errors one
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    impressive recent example for how to use
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    AI for error correction comes from the
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    Google AI group they've trained their AI
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    on data from the Sycamore chip so that
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    superc conducting cubits then they use
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    the AI for active feedback to reduce the
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    error rate you see the results of the
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    new algorithm here in the red curves
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    it's significantly better they call
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    their system Alpha cubid and demonstrate
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    that it outperforms all other known
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    algorithms when I say it's significantly
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    better I mean it's a measurable
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    difference not a large one but it
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    generalizes better beyond the training
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    set to longer computations than previous
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    methods which will make it useful for
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    scaling up quantum computers personally
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    I don't think the superconducting cubits
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    that Google uses will bring us to
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    commercially relevant quantum computers
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    but another recent development might get
  • 00:03:18
    us there it comes from building quantum
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    computers using an approach called atoms
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    in tweezers or neutral atom arrays we've
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    talked about this previously because
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    it's one of the fastest developing
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    newcomers in Quantum Computing for this
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    one uses electromagnetic fields to trap
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    atoms in an array and entangle them with
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    lasers in traps that look like this the
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    good thing about this is that because
  • 00:03:45
    atoms are small you can pack many of
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    them closely together so this approach
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    could scale very quickly a recent survey
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    among Quantum Computing experts found
  • 00:03:56
    they rated neutral atoms as the approach
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    which holds the most promise for
  • 00:04:02
    achieving practical applications of
  • 00:04:04
    quantum Computing then again this survey
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    was done by company which works on
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    neutral atoms so take this with a pound
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    of salt one of the issues with neutral
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    atoms is though that you need them to
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    fill in the aray in an orderly way in
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    this new paper now they used AI to learn
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    how to read out the positioning of the
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    atoms and to move them into place which
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    worked like a charm in this amazing atom
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    tomography image each dot is an atom
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    there are more than 2,000 atoms of them
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    in an almost perfect array each could
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    act as a cubit for comparison the
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    currently largest quantum computer is
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    from IBM and it has about 1,000 cubits
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    let me be clear though that they haven't
  • 00:04:49
    actually done a calculation with these
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    atoms and tweezers yet but this method
  • 00:04:54
    has been catching up quickly and I
  • 00:04:57
    expect we'll hear more about this soon
  • 00:04:59
    this isn't just interesting for
  • 00:05:01
    practical purposes because it'll make
  • 00:05:04
    scating quantum computers easier I also
  • 00:05:06
    think this is interesting from a
  • 00:05:08
    research perspective because it's
  • 00:05:11
    implicitly testing how random quantum
  • 00:05:14
    mechanics really is physicists currently
  • 00:05:16
    think that quantum mechanics has this
  • 00:05:18
    irreducible random element but what if
  • 00:05:21
    it hasn't then AI might become better at
  • 00:05:24
    eliminating errors than should be
  • 00:05:26
    theoretically possible now wouldn't that
  • 00:05:29
    be interesting artificial intelligence
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    watching see you tomorrow
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