We define a new model of quantum learning that we call Predictive Quantum (PQ ). This is a quantum analogue of PAC , where during the testing phase the student is only required to answer a polynomial number of testing queries. We demonstrate a relational concept class that is efficiently learnable in PQ , while in any "reasonable" classical model exponential amount of training data would be required. This is the first unconditional separation between quantum and classical learning. We show that our separation is the best possible in several ways; in particular, there is no analogous result for a functional class, as well as for several weaker versions of quantum learning. In order to demonstrate tightness of our separation we consider a special case of one-way communication that we call single-input mode, where Bob receives no input. Somewhat surprisingly, this setting becomes nontrivial when relational communication tasks are considered. In particular, any problem with two-sided input can be transformed into a single-input relational problem of equal classical one-way cost. We show that the situation is different in the quantum case, where the same transformation can make the communication complexity exponentially larger. This happens if and only if the original problem has exponential gap between quantum and classical one-way communication costs. We believe that these auxiliary results might be of independent interest.
Gavinsky REF introduced a model of learning called "Predictive Quantum" (PQ), a variation of quantum PAC learning, and exhibited a relational concept class that is polynomial-time learnable in PQ, while any "reasonable" classical model requires an exponential number of classical examples to learn the concept class.
Quantum Predictive Learning and Communication Complexity with Single Input
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Computer Science Mathematics Physics