861 research outputs found
Quantum states with a positive partial transpose are useful for metrology
We show that multipartite quantum states that have a positive partial
transpose with respect to all bipartitions of the particles can outperform
separable states in linear interferometers. We introduce a powerful iterative
method to find such states. We present some examples for multipartite states
and examine the scaling of the precision with the particle number. Some
bipartite examples are also shown that possess an entanglement very robust to
noise. We also discuss the relation of metrological usefulness to Bell
inequality violation. We find that quantum states that do not violate any Bell
inequality can outperform separable states metrologically. We present such
states with a positive partial transpose, as well as with a non-positive
positive partial transpose.Comment: 6 pages including two figures + three-page supplement including two
figures using revtex 4.1, with numerically obtained density matrices as text
files; v2: published version; v3: published version, typo in the 4x4 bound
entangled state is corrected (noticed by Peng Yin
Learning and Testing Variable Partitions
Let be a multivariate function from a product set to an
Abelian group . A -partition of with cost is a partition of
the set of variables into non-empty subsets such that is -close to
for some with
respect to a given error metric. We study algorithms for agnostically learning
partitions and testing -partitionability over various groups and error
metrics given query access to . In particular we show that
Given a function that has a -partition of cost , a partition
of cost can be learned in time
for any .
In contrast, for and learning a partition of cost is NP-hard.
When is real-valued and the error metric is the 2-norm, a
2-partition of cost can be learned in time
.
When is -valued and the error metric is Hamming
weight, -partitionability is testable with one-sided error and
non-adaptive queries. We also show that even
two-sided testers require queries when .
This work was motivated by reinforcement learning control tasks in which the
set of control variables can be partitioned. The partitioning reduces the task
into multiple lower-dimensional ones that are relatively easier to learn. Our
second algorithm empirically increases the scores attained over previous
heuristic partitioning methods applied in this context.Comment: Innovations in Theoretical Computer Science (ITCS) 202
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