About a yr and a half in the past, quantum management startup Quantum Machines and Nvidia introduced a deep partnership that will convey collectively Nvidia’s DGX Quantum computing platform and Quantum Machine’s superior quantum management {hardware}. We didn’t hear a lot concerning the outcomes of this partnership for some time, however it’s now beginning to bear fruit and getting the business one step nearer to the holy grail of an error-corrected quantum pc.
In a presentation earlier this yr, the 2 firms confirmed that they can use an off-the-shelf reinforcement studying mannequin working on Nvidia’s DGX platform to raised management the qubits in a Rigetti quantum chip by maintaining the system calibrated.
Yonatan Cohen, the co-founder and CTO of Quantum Machines, famous how his firm has lengthy sought to make use of basic classical compute engines to manage quantum processors. Those compute engines had been small and restricted, however that’s not an issue with Nvidia’s extraordinarily highly effective DGX platform. The holy grail, he mentioned, is to run quantum error correction. We’re not there but. Instead, this collaboration centered on calibration, and particularly calibrating the so-called “π pulses” that management the rotation of a qubit inside a quantum processor.
At first look, calibration could seem to be a one-shot downside: You calibrate the processor earlier than you begin working the algorithm on it. But it’s not that straightforward. “If you look at the performance of quantum computers today, you get some high fidelity,” Cohen mentioned. “But 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, which is what’s going to be needed in quantum error correction.”
Constantly adjusting these pulses in close to actual time is a particularly compute-intensive process, however since a quantum system is at all times barely totally different, additionally it is a management downside that lends itself to being solved with the assistance of reinforcement studying.
“As quantum computers are scaling up and improving, there are all these problems that become bottlenecks, that become really compute-intensive,” mentioned Sam Stanwyck, Nvidia’s group product supervisor for quantum computing. “Quantum error correction is really a huge one. This is necessary to unlock fault-tolerant quantum computing, but also how to apply exactly the right control pulses to get the most out of the qubits”
Stanwyck additionally pressured that there was no system earlier than DGX Quantum that will allow the sort of minimal latency essential to carry out these calculations.
As it seems, even a small enchancment in calibration can result in large enhancements in error correction. “The return on investment in calibration in the context of quantum error correction is exponential,” defined Quantum Machines Product Manager Ramon Szmuk. “If 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’s a lot of motivation here to calibrate very well and fast.”
It’s price stressing that that is simply the beginning of this optimization course of and collaboration. What the staff truly did right here was merely take a handful of off-the-shelf algorithms and take a look at which one labored finest (TD3, on this case). All in all, the precise code for working the experiment was solely about 150 strains lengthy. Of course, this depends on all the work the 2 groups additionally did to combine the varied methods and construct out the software program stack. For builders, although, all of that complexity could be hidden away, and the 2 firms anticipate to create increasingly more open supply libraries over…
