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About A A year and a half agostart quantitative control Quantum machines Nvidia announced a deep partnership that will bring Nvidia together DJX Quantum Quantum Machine’s advanced quantum computing and controller platform. We haven’t heard much about the results of this partnership for a while, but it’s now starting to bear fruit and bring the industry one step closer to the holy grail of an error-correcting quantum computer.
In a presentation earlier this year, the two companies demonstrated this They are able to use an off-the-shelf reinforcement learning model It runs on Nvidia’s DGX platform to better control the qubits in the Rigetti quantum chip by maintaining system calibration.
Yonatan Cohen, co-founder and CTO of Quantum Machines, noted how his company has long sought to use general classical computing engines to control quantum processors. Those compute engines were small and limited, but that’s not a problem with Nvidia’s extremely powerful DGX platform. The holy grail, he said, is a quantum error correction procedure. We’re not there yet. Instead, this collaboration focused on calibration, specifically calibration of so-called “PulsesWhich controls the rotation of the qubit inside the quantum processor.
At first glance, calibration may seem like a one-time problem: you calibrate the processor before you start running the algorithm on it. But it’s not that simple. “If you look at the performance of quantum computers today, you get some pretty high accuracy,” Cohen said. “But then, when users use the computer, it’s usually not at the best accuracy. It’s drifting all the time. If we can recalibrate it frequently using these kinds of basic techniques and hardware, we can improve performance and maintain (high) accuracy over a long period.” , which will be needed in quantitative error correction.
Continuously fine-tuning those pulses in near real-time is a very computationally intensive task, but since a quantum system is always slightly different, it is also a control problem that can be solved with the help of reinforcement learning.
“As quantum computers expand and improve, there are all these issues that become bottlenecks, that become really compute-intensive,” said Sam Stanwick, Nvidia’s group product manager for quantum computing. “Quantum error correction is actually a huge process. This is essential to unlocking fault-tolerant quantum computing, but also how to apply exactly the right control pulses to get the most out of qubits.”
Stanwyck also stressed that there was no system before DGX Quantum that would allow the minimum latency needed to perform these calculations.
As it turns out, even a small improvement in calibration can lead to huge improvements in error correction. “The return on investment in calibration in the context of quantum error correction is enormous,” explained Ramon Zmock, Quantum Machinery Product Manager. “If you calibrate 10% better, that gives you dramatically better logic error (performance) in a logical qubit that is made up of many physical qubits. So there is a lot of motivation here to calibrate very well and very quickly.”
It should be noted that this is just the beginning of this process of improvement and collaboration. What the team actually did here was simply take a set of off-the-shelf algorithms and look at which ones work best (TD3in this case). Overall, the actual code to run the experiment was only about 150 lines long. Of course, this depends on all the work that the two teams have also done to integrate the different systems and build the software stack. For developers, all this complexity can be hidden away, and both companies expect to create more and more open source libraries over time to take advantage of this larger platform.
Zmock stressed that for this project, the team only worked with a very basic quantum circuit, but it can be generalized to deep circuits as well. If you can do it with one gate and one qubit, you can also do it with a hundred qubits and 1,000 gates.
“I say that an individual result is a small step, but it is a small step toward solving the more important problems,” Stanwyck added. “Useful quantum computing will require tight integration of accelerated supercomputing — and that may be the most difficult engineering challenge. So, to be able to do this for real on a quantum computer and set the pulse in a way that is not only optimized for a small quantum computer but is a modular platform Scalable, we think we’re really on our way to solving some of the most important problems in quantum computing with this.
Stanwyck also said that the two companies plan to continue this collaboration and put these tools in the hands of more researchers. And with Nvidia’s Blackwell chips available next year, it will also have a more powerful computing platform for this project as well.
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