Unitary Closed Timelike Curves can Solve all of NP

Abstract: Born in the intersection between quantum mechanics and general relativity, indefinite causal structure is the idea that in the continuum of time, some sets of events do not have an inherent causal order between them. Process matrices, introduced by Oreshkov, Costa and Brukner (Nature Communications, 2012), define quantum information processing with indefinite causal structure -- a generalization of the operations allowed in standard quantum information processing, and to date, are the most studied such generalization. Araujo et al. (Physical Review A, 2017) defined the computational complexity of process matrices, and showed that polynomial-time process matrix computation is equivalent to standard polynomial-time quantum computation with access to a weakening of post-selection Closed Timelike Curves (CTCs), that are restricted to be $\textit{linear}$. Araujo et al. accordingly defined the complexity class for efficient process matrix computation as $\mathbf{BQP}{\ell CTC}$ (which trivially contains $\mathbf{BQP}$), and posed the open question of whether $\mathbf{BQP}{\ell CTC}$ contains computational tasks that are outside $\mathbf{BQP}$. In this work we solve this open question under a widely believed hardness assumption, by showing that $\mathbf{NP} \subseteq \mathbf{BQP}_{\ell CTC}$. Our solution is captured by an even more restricted subset of process matrices that are purifiable (Araujo et al., Quantum, 2017), which (1) is conjectured more likely to be physical than arbitrary process matrices, and (2) is equivalent to polynomial-time quantum computation with access to $\textit{unitary}$ (which are in particular linear) post-selection CTCs. Conceptually, our work shows that the previously held belief, that non-linearity is what enables CTCs to solve $\mathbf{NP}$, is false, and raises the importance of understanding whether purifiable process matrices are physical or not.