# Quantum data classification by dissipation. (arXiv:1811.03175v1 [quant-ph])

We investigate a general class of dissipative quantum circuit capable of

computing arbitrary Conjunctive-Normal-Form (CNF) Boolean formulas. In

particular, the clauses in a CNF formula define a local generator of Markovian

quantum dynamics which acts on a network of qubits. Fixed points of this

dynamical system encode the evaluation of the CNF formula. The structure of the

corresponding quantum map partitions the Hilbert space into sectors, according

to decoherence-free subspaces (DFSs) associated with the dissipative dynamics.

These sectors then provide a natural and consistent way to classify quantum

data (i.e. quantum states). Indeed, the attractive fixed points of the network

allow one to learn the sector(s) for which some particular quantum state is

associated. We show how this structure can be used to dissipatively prepare

quantum states (e.g. entangled states), and outline how it may be used for

quantum machine learning. Under this protocol, it is demonstrated, that one can

learn non-trivial information about a quantum state in a passive manner,

without directly measuring or disturbing the state.