Google Quantum AI's Virtual Error Cancellation targets leftover logical errors

[BinaryMonk91]

Google Quantum AI researchers have described a new technique called Virtual Error Cancellation, or VEC, and it is a clever bit of thinking. It is a classical step that runs after a fault tolerant circuit has finished. Nothing changes about how the circuit runs on the hardware, and no separate calibration runs are needed. The aim is to strip out the small logical errors that slip past error correction

The trick is to decode every shot twice. One decoder is fast enough for real time use, such as minimum weight perfect matching, which has to meet microsecond deadlines. The other is slower but more accurate, such as a tensor network maximum likelihood decoder that can run offline. When the two agree, the shot gets a positive weight slightly above one. When they disagree, the shot is kept rather than thrown away and given a negative weight tied to the type of error

Averaged over many shots, those weights make the leftover logical error cancel out. The noise settings needed for the weights are worked out from syndrome data the experiment has already recorded, using a method called Walsh-Hadamard linear inversion. That means no extra hardware time is spent learning the noise. Quantum Computing Report points out that simply discarding disagreeing shots introduces its own bias, which VEC avoids

The numbers are striking. In simulations of a distance seven rotated surface code at a physical error rate of 0.005, VEC pushed the residual logical error rate below 7.95 x 10^-10. That is more than 1,600 times lower than plain decoding at about 1.32 x 10^-6, and more than 200 times better than throwing away the disagreeing shots. The sampling overhead was only about 1.3, compared with roughly 4 for standard logical probabilistic error cancellation

It is worth being clear that these are simulation results from a preprint, not a hardware demonstration. Still, the idea fits neatly into the way real machines will work, with a fast decoder keeping up during the run and slower analysis afterwards. If it holds up on a real chip, it could make early fault tolerant machines useful sooner than expected

What do you make of using negative weights instead of postselection? And how soon do you think we will see this tested on real hardware?

Google Quantum AI Introduces Virtual Error Cancellation for Universal Logical Circuits - Quantum Computing Reportquantumcomputingreport.com