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Search Publications by: Yi-Kai Liu (Fed)

Displaying 26 - 33 of 33

Show, don't tell: When probing the state of a quantum system, it may be faster and more useful to record observations that are themselves quantum superpositions (Nature Physics "News and Views" article)

July 27, 2014

Author(s)

Yi-Kai Liu

Measurement and computation are fundamental tools in science. However, many of our ordinary intuitions break down when we study microscopic, quantum-mechanical objects. For instance, measurements of quantum systems are limited by uncertainty principles

Building one-time memories from isolated qubits

January 14, 2014

Author(s)

Yi-Kai Liu

One-time memories (OTM's) are a simple type of tamper-resistant cryptographic hardware, that can be used to implement many forms of secure computation, such as one-time programs. Here we investigate the possibility of building OTM's using "isolated qubits"

Multilingual Summarization: Dimensionality Reduction and a Step Towards Optimal Term Coverage

August 9, 2013

Author(s)

Yi-Kai Liu, John M. Conroy, Sashka T. Davis, Jeff Kubina, Dianne P. O'Leary, Judith D. Schlesinger

In this paper we present three term weighting approaches for multi-lingual document summarization and give results on the DUC 2002 data as well as on the 2013 Multilingual Wikipedia feature articles data set. We introduce a new interval-bounded nonnegative

Testing quantum expanders is co-QMA-complete

May 31, 2013

Author(s)

Yi-Kai Liu, Stephen P. Jordan, Pawel Wocjan, Adam Bookatz

A quantum expander is a unital quantum channel that is rapidly mixing, has only a few Kraus operators, and can be implemented efficiently on a quantum computer. We consider the problem of estimating the mixing time (i.e., the spectral gap) of a quantum

Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation

December 6, 2012

Author(s)

Yi-Kai Liu, Animashree Anandkumar, Dean P. Foster, Daniel Hsu, Sham M. Kakade

The problem of topic modeling can be seen as a generalization of the clustering problem, in that it posits that observations are generated due to multiple latent factors (e.g. the words in each document are generated as a mixture of several active topics

Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators

September 27, 2012

Author(s)

Yi-Kai Liu, Steven T. Flammia, David Gross, Jens Eisert

Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We prove two complementary results that confi rm this intuition. First, we show

Continuous-variable quantum compressed sensing

July 9, 2012

Author(s)

Yi-Kai Liu, Matthias Ohliger, Vincent Nesme, David Gross, Jens Eisert

We introduce a novel method to faithfully reconstruct unknown quantum states that are approximately low-rank, using only a few measurement settings. The method is general enough to allow for measurements from a continuous family, and is also applicable to

Universal Low-rank Matrix Recovery from Pauli Measurements

December 12, 2011

Author(s)

Yi-Kai Liu

We study the problem of reconstructing an unknown matrix M of rank r and dimension d using O(rd poly log d) Pauli measurements. This has applications in quantum state tomography, and is a non- commutative analogue of a well-known problem in compressed

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