What this makes me think is that quantum computing is mostly doomed. The killer app for quantum computing is predicting molecules and electronic structures. (Perhaps someone would pay for Shor’s algorithm, but its coolness far outstrips its economic value). But it’s probably a lot cheaper to train a machine-learning based approximation on a bunch of painstakingly assembled data than it is to build enough 50 milliKelvin cyostats. According to this view, the physics labs that will win at superconductor prediction are not the ones working on quantum computers or on theoretical breakthroughs, they’re going to be the guys converting every phonon spectrum from the last 50 years into a common data format so they can spend $30K to train a big 3D transformer on it.
What this makes me think is that quantum computing is mostly doomed. The killer app for quantum computing is predicting molecules and electronic structures. (Perhaps someone would pay for Shor’s algorithm, but its coolness far outstrips its economic value). But it’s probably a lot cheaper to train a machine-learning based approximation on a bunch of painstakingly assembled data than it is to build enough 50 milliKelvin cyostats. According to this view, the physics labs that will win at superconductor prediction are not the ones working on quantum computers or on theoretical breakthroughs, they’re going to be the guys converting every phonon spectrum from the last 50 years into a common data format so they can spend $30K to train a big 3D transformer on it.