{"id":643190,"date":"2024-11-02T20:00:00","date_gmt":"2024-11-02T17:00:00","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/quantum-machines-and-nvidia-use-machine-learning-to-get-closer-to-an-error-corrected-quantum-computer\/"},"modified":"2024-11-02T20:00:00","modified_gmt":"2024-11-02T17:00:00","slug":"quantum-machines-and-nvidia-use-machine-learning-to-get-closer-to-an-error-corrected-quantum-computer","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/quantum-machines-and-nvidia-use-machine-learning-to-get-closer-to-an-error-corrected-quantum-computer\/","title":{"rendered":"#Quantum Machines and Nvidia use machine learning to get closer to an error-corrected quantum computer"},"content":{"rendered":"
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About a year and a half ago<\/a>, quantum control startup Quantum Machines<\/a> and Nvidia announced a deep partnership that would bring together Nvidia\u2019s DGX Quantum<\/a> computing platform and Quantum Machine\u2019s advanced quantum control hardware. We didn\u2019t hear much about the results of this partnership for a while, but it\u2019s now starting to bear fruit and getting the industry one step closer to the holy grail of an error-corrected quantum computer. <\/p>\n

In a presentation earlier this year, the two companies showed that they are able to use an off-the-shelf reinforcement learning model<\/a> running on Nvidia\u2019s DGX platform to better control the qubits in a Rigetti quantum chip by keeping the system calibrated.<\/p>\n

Yonatan Cohen, the co-founder and CTO of Quantum Machines, noted how his company has long sought to use general<\/a> classical compute engines to control quantum processors. Those compute engines were small and limited, but that\u2019s not a problem with Nvidia\u2019s extremely powerful DGX platform. The holy grail, he said, is to run quantum error correction. We\u2019re not there yet. Instead, this collaboration focused on calibration, and specifically calibrating the so-called \u201c\u03c0 pulses<\/a>\u201d that control the rotation of a qubit inside a quantum processor.<\/p>\n

At first glance, calibration may seem like a one-shot problem: You calibrate the processor before you start running the algorithm on it. But it\u2019s not that simple. \u201cIf you look at the performance of quantum computers today, you get some high fidelity,\u201d Cohen said. \u201cBut then, the users, when they use the computer, it\u2019s typically not at the best fidelity. It drifts all the time. If we can frequently recalibrate it using these kinds of techniques and underlying hardware, then we can improve the performance and keep the fidelity [high] over a long time, which is what\u2019s going to be needed in quantum error correction.\u201d<\/p>\n

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Quantum Machine\u2019s all-in-one OPX+ quantum control system.<\/span>Image Credits:<\/strong>Quantum Machines<\/span><\/figcaption><\/figure>\n

Constantly adjusting those pulses in near real time is an extremely compute-intensive task, but since a quantum system is always slightly different, it is also a control problem that lends itself to being solved with the help of reinforcement learning. <\/p>\n

\u201cAs quantum computers are scaling up and improving, there are all these problems that become bottlenecks, that become really compute-intensive,\u201d said Sam Stanwyck, Nvidia\u2019s group product manager for quantum computing. \u201cQuantum error correction is really a huge one. This is necessary to unlock fault-tolerant quantum computing, but also how to app<\/a>ly exactly the right control pulses to get the most out of the qubits\u201d<\/p>\n

Stanwyck also stressed that there was no system before DGX Quantum that would enable the kind of minimal latency necessary to perform these calculations.<\/p>\n

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A quantum Computer<\/span>Image Credits:<\/strong>Quantum Machines<\/span><\/figcaption><\/figure>\n

As it turns out, even a small improvement in calibration can lead to massive improvements in error correction. \u201cThe return on investment in calibration in the context of quantum error correction is exponential,\u201d explained Quantum Machines Product Manager Ramon Szmuk. \u201cIf you calibrate 10% better, that gives you an exponentially better logical error [performance] in the logical qubit that is composed of many physical qubits. So there\u2019s a lot of motivation here to calibrate very well and fast.\u201d<\/p>\n

It\u2019s worth stressing that this is just the start of this optimization process and collaboration. What the team actually did here was simply take a handful of off-the-shelf algorithms and look at which one worked best (TD3<\/a>, in this case). All in all, the actual code for running the experiment was only about 150 lines long. Of course, this relies on all of the work the two teams also did to integrate the various systems and build out the software stack. For developers, though, all of that complexity can be hidden away, and the two companies expect to create more and more open source libraries over time to take advantage of this larger platform.<\/p>\n

Szmuk stressed that for this project, the team only worked with a very basic quantum circuit but that it can be generalized to deep circuits as well. If you can do this with one gate and one qubit, you can also do it with a hundred qubits and 1,000 gates,\u201d he said.<\/p>\n

\u201cI\u2019d say the individual result is a small step, but it\u2019s a small step towards solving the most important problems,\u201d Stanwyck added. \u201cUseful quantum computing is going to require the tight integration of accelerated supercomputing \u2014 and that may be the most difficult engineering challenge. So being able to do this for real on a quantum computer and tune up a pulse in a way that is not just optimized for a small quantum computer but is a scalable, modular platform, we think we\u2019re really on the way to solving some of the most important problems in quantum computing with this.\u201d<\/p>\n

Stanwyck also said that the two companies plan to continue this collaboration and get these tools into the hands of more researchers. With Nvidia\u2019s Blackwell chips becoming available next year, they\u2019ll also have an even more powerful computing platform for this project, too. <\/p>\n<\/div>\n

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Source<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

About a year and a half ago, quantum control startup Quantum Machines and Nvidia announced a deep partnership that would bring together Nvidia\u2019s DGX Quantum computing platform and Quantum Machine\u2019s advanced quantum control hardware. We didn\u2019t hear much about the results of this partnership for a while, but it\u2019s now starting to bear fruit and…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","footnotes":""},"categories":[18],"tags":[],"class_list":["post-643190","post","type-post","status-publish","format-standard","hentry","category-technology"],"_links":{"self":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/643190","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/comments?post=643190"}],"version-history":[{"count":0,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/643190\/revisions"}],"wp:attachment":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media?parent=643190"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/categories?post=643190"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/tags?post=643190"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}