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Quantum Machines and Nvidia use machine learning to get closer to an error-corrected quantum computer
- Quantum Machines and Nvidia are getting one step closer to an error-corrected quantum computer by using machine learning on Nvidia’s DGX Quantum computing platform to keep the system calibrated. The π pulses that control the rotation of a qubit inside a quantum processor are being calibrated using an off-the-shelf reinforcement learning model. This control problem lends itself to being solved with the help of reinforcement learning because a quantum system is always slightly different. A small improvement in calibration can lead to massive improvements in error correction of a logical qubit.
- The actual code for running the experiment was only about 150 lines long. All of the work the two teams did to integrate the various systems and build out the software stack, though, can be hidden away from developers. 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.
- Quantum error correction is a huge problem that is necessary to unlock fault-tolerant quantum computing. DGX Quantum is the first system that enables the kind of minimal latency needed to perform these calculations. As quantum computers scale up, problems become bottlenecks that are really compute-intensive. Applying exactly the right control pulses to get the most out of the qubits is one of the problems.
- The team is only at the start of this optimization process and collaboration and expects to create more and more open-source libraries over time to take advantage of this larger platform. With Nvidia’s Blackwell chips becoming available next year, they’ll also have an even more powerful computing platform for this project.
- If you look at the performance of quantum computers today, you get some high fidelity. Then, the users, when they use the computer, it’s 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.
- Quantum Machine’s co-founder and CTO, Yonatan Cohen, noted how his company has long sought to use general classical compute engines to control quantum processors, but those compute engines were small and limited. The partnership with Nvidia and their DGX platform brings that computational power to calibration.
- The collaboration between Quantum Machines and Nvidia is a small step towards solving the most important problems. Useful quantum computing is going to require the tight integration of accelerated supercomputing, which may be the most difficult engineering challenge.
- Sam Stanwyck, Nvidia’s group product manager for quantum computing, stated that the two companies plan to continue this collaboration and get these tools into the hands of more researchers.
- The team used TD3 algorithm for calibration, because it worked best. The team only worked with a very basic quantum circuit, but it can be generalized to deep circuits as well.
- Constantly adjusting π pulses in near real-time is an extremely compute-intensive task requiring strong computational power, but collaborative team working between Quantum Machines and Nvidia is making it possible.
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