194,176 research outputs found
Non-adiabatic Fast Control of Mixed States based on Lewis-Riesenfeld Invariant
We apply the inversely-engineered control method based on Lewis-Riesenfeld
invariants to control mixed states of a two-level quantum system. We show that
the inversely-engineered control passages of mixed states - and pure states as
special cases - can be made significantly faster than the conventional
adiabatic control passages, which renders the method applicable to quantum
computation. We devise a new type of inversely-engineered control passages, to
be coined the antedated control passages, which further speed up the control
significantly. We also demonstrate that by carefully tuning the control
parameters, the inversely-engineered control passages can be optimized in terms
of speed and energy cost.Comment: 9 pages, 9 figures, version to appear in J. Phys. Soc. Jp
Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud
The increasing massive data generated by various sources has given birth to
big data analytics. Solving large-scale nonlinear programming problems (NLPs)
is one important big data analytics task that has applications in many domains
such as transport and logistics. However, NLPs are usually too computationally
expensive for resource-constrained users. Fortunately, cloud computing provides
an alternative and economical service for resource-constrained users to
outsource their computation tasks to the cloud. However, one major concern with
outsourcing NLPs is the leakage of user's private information contained in NLP
formulations and results. Although much work has been done on
privacy-preserving outsourcing of computation tasks, little attention has been
paid to NLPs. In this paper, we for the first time investigate secure
outsourcing of general large-scale NLPs with nonlinear constraints. A secure
and efficient transformation scheme at the user side is proposed to protect
user's private information; at the cloud side, generalized reduced gradient
method is applied to effectively solve the transformed large-scale NLPs. The
proposed protocol is implemented on a cloud computing testbed. Experimental
evaluations demonstrate that significant time can be saved for users and the
proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was
done when Wei Du was at the University of Arkansas. 2018 EAI International
Conference on Security and Privacy in Communication Networks (SecureComm
Non-universal size dependence of the free energy of confined systems near criticality
The singular part of the finite-size free energy density of the O(n)
symmetric field theory in the large-n limit is calculated at finite
cutoff for confined geometries of linear size L with periodic boundary
conditions in 2 < d < 4 dimensions. We find that a sharp cutoff
causes a non-universal leading size dependence
near which dominates the universal scaling term . This
implies a non-universal critical Casimir effect at and a leading
non-scaling term of the finite-size specific heat above .Comment: RevTex, 4 page
- …