Quantum Advantage in Learning from Experiments
For certain types of problems, quantum computing can provide an exponential speedup for ML algorithms. This is now being tested on actual QC hardware. This strikes me as particularly concerning for hard take-off scenarios. It's a possible way to train dramatically larger AI systems faster, and it's likely to be...
What do you mean by this exactly? Does the other half come out as particles? If so, why not just dump the particles back in until everything comes out as energy?