Computation in generalised probabilistic theories
Ciar\'an M. Lee, Jonathan Barrett

TL;DR
This paper explores the computational limits of generalized probabilistic theories, showing that under certain physical assumptions, their computational power aligns with known complexity classes like AWPP and PP, even without causality.
Contribution
It establishes that theories with tomographic locality and without assuming causality still contain efficient computation within AWPP and PP, extending quantum computational bounds to more general theories.
Findings
Efficient computation in general theories is contained in AWPP under tomographic locality.
Post-selected computation in general theories is contained in PP with only tomographic locality.
There exists a classical oracle where theories satisfying certain assumptions do not include NP.
Abstract
From the existence of an efficient quantum algorithm for factoring, it is likely that quantum computation is intrinsically more powerful than classical computation. At present, the best upper bound known for the power of quantum computation is that BQP is in AWPP. This work investigates limits on computational power that are imposed by physical principles. To this end, we define a circuit-based model of computation in a class of operationally-defined theories more general than quantum theory, and ask: what is the minimal set of physical assumptions under which the above inclusion still holds? We show that given only an assumption of tomographic locality (roughly, that multipartite states can be characterised by local measurements), efficient computations are contained in AWPP. This inclusion still holds even without assuming a basic notion of causality (where the notion is, roughly,…
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