Faster quantum computers can learn from their own mistakes

[Saka31]

Quantum computers promise to solve problems that would take even the fastest conventional supercomputers a vast amount of time, but the quantum information they store and process is extremely sensitive to tiny disturbances from their surroundings. To keep these systems operating reliably, they normally need to be constantly recalibrated, which means interrupting their calculations entirely while it happens

In a new experiment published in Nature, researchers led by Volodymyr Sivak at Google Quantum AI developed a machine learning approach that continuously adjusts a quantum computer while it works, rather than pausing it. Their approach could let quantum calculations run far longer without those costly interruptions

Qubits, the building blocks of quantum information, are notoriously fragile, even tiny changes in temperature, electrical currents or gradual drift in control electronics can significantly increase the likelihood of errors. Modern quantum systems handle this by undergoing regular calibration, where the settings controlling each qubit get carefully adjusted to minimize mistakes, but that process requires calculations to stop completely while it happens, a real obstacle for the long calculations researchers hope to eventually run

Sivak's team found a way around this by reusing information the computer already collects. Quantum systems already monitor for errors as they run, using specialized qubits that can detect when something's gone wrong without disturbing the actual calculation. Instead of only using that information to flag errors, the team fed it into a reinforcement learning algorithm, which made tiny adjustments to thousands of control settings and observed how the resulting pattern of detected errors changed, gradually learning which adjustments actually improved stability. In effect, the quantum computer learns from its own mistakes while the calculation keeps running

Testing the approach on Google's Willow superconducting processor, the team deliberately introduced drift to simulate subtle environmental changes, and found the system became roughly 3.5 times more stable than existing error correction methods, with that improved stability holding up even while the processor kept running. Further simulations suggested the method could scale to systems with tens of thousands of adjustable control parameters without becoming significantly slower. Today's quantum computers aren't yet large enough for the recalibration problem to be a serious limitation, but the result addresses a challenge that will matter increasingly as the technology matures, letting future machines continuously refine their own operation instead of pausing to recalibrate

Faster Quantumphys.org