My journey: 'race', the sciences, and gender

The paper starts from the problem that politics and everyday interaction to counter discrimination on grounds of racial or gender difference use the categories of race and gender, thus possibly reinforcing the belief that these differences are real not only in a social but also in a biological sense, thus implying an unchangeable difference between humans. Using examples from 1920’s pre-Nazi Germany popularisation of racial thought it argues for a conscious and context dependent use and critique of “race” and for a critical investigation of the historical contingencies of the creation and use of “race” as a scientific category

Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network

Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor for distance 3 and 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk, leakage, and analog readout signals, and sustains its accuracy far beyond the 25 cycles it was trained on. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers