Google's Quantum Error Correction Method Enables Constant Recalibration

There are obvious big picture issues standing between us and useful quantum computing. Issues like whether we can make enough high-quality hardware qubits to connect into the error-corrected logical qubits we need, and how we generate the states needed to perform universal computation on those logical qubits. But there are also many less prominent challenges that will need to be solved before we can perform calculations.

One of those challenges, which only affects some types of hardware, is calibration. For devices we manufacture, like superconducting qubits, there are always subtle variations among individual qubits. This is not true when we use something like an atom to hold the qubit, but the lasers that control them can drift.

As a result, this hardware is put through a process called calibration, where we test different frequencies and amplitudes of the microwave pulses that control them to find the combination that produces the lowest error rates, and then save those settings for use in calculations. However, you can't perform the typical calibration process while you're doing calculations, which means drift becomes an issue for long and complicated algorithms. Google has figured out that it's possible to do calibration using the same data that's used for error correction.

The ability to constantly recalibrate a processor using error correction data could significantly improve the reliability and performance of quantum computers. Google's approach could help mitigate the effects of drift and other forms of noise that can degrade qubit quality over time. Why this matters: The development could have far-reaching implications for the development of practical quantum computing applications.

By enabling constant recalibration, Google's method could help ensure that quantum computers can maintain their performance over longer periods, which is essential for complex calculations and simulations. This could be particularly important for industries like chemistry and materials science, where quantum computers are being explored for their potential to simulate complex systems. For developers, this could mean that they can focus on building more sophisticated algorithms and applications, rather than worrying about the underlying hardware stability.

However, there are still questions about how this approach will scale as quantum computers grow in size and complexity, and whether it can be adapted to work with different types of quantum hardware.