Riverlane publishes landmark paper towards useful, scalable quantum computing

In our recent work, Riverlane solved a long-standing quantum error correction problem, paving the way for scalable fault tolerant quantum computation.

As the quantum computation we want to execute gets longer, bigger and bigger error correcting codes are required, generating an ever-increasing amount of data. It is expected that a utility-scale quantum computer will generate terabytes of error correction data each second, a quantity of data on the order of Netflix global streaming data or that produced by the ALICE detector at CERN. All of this data needs to be processed as quickly as it is generated, otherwise, we risk the computation grinding to a halt.

In our work, we present a new methodology that parallelises the data processing problem and achieves almost arbitrary processing speed without sacrificing on the accuracy.

Read the paper here: Parallel window decoding enables scalable fault tolerant quantum computation

In addition to our Parallel Window Decoding paper, our technical whitepaper details the speed and scalability of our decoder solution, as well as an accuracy comparison to an industry standard decoder.

Read the whitepaper here: Deltaflow.Decode Technical Whitepaper