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In modelling complex processes, the potential past data that influence futureexpectations are immense. Models that track all this data are not onlycomputationally wasteful but also shed little light on what past data mostinfluence the future. There is thus enormous interest in dimensionalreduction-finding automated means to reduce the memory dimension of our modelswhile minimizing its impact on its predictive accuracy. Here we constructdimensionally reduced quantum models by machine learning methods that canachieve greater accuracy than provably optimal classical counterparts. Wedemonstrate this advantage on present-day quantum computing hardware. Ouralgorithm works directly off classical time-series data and can thus bedeployed in real-world settings. These techniques illustrate the immediaterelevance of quantum technologies to time-series analysis and offer a rareinstance where the resulting quantum advantage can be provably established.

Provable superior accuracy in machine learned quantum models