2014 - ACM Senior Member
Krysta M. Svore focuses on Quantum computer, Theoretical computer science, Data mining, Quantum algorithm and Quantum. Her Quantum computer research incorporates themes from Quantum noise, Qubit, Communication channel and Algorithm, Computation. Her Theoretical computer science study combines topics from a wide range of disciplines, such as Quantum sort, Relevance and Feature vector.
Her research in Data mining intersects with topics in Ranking, Learning to rank, Machine learning, Search engine and Artificial intelligence. Krysta M. Svore combines subjects such as Programming language, Quantum entanglement, Macroscopic quantum phenomena and Quantum logic with her study of Quantum algorithm. The study incorporates disciplines such as Restricted Boltzmann machine, Deep learning, Computer engineering and Boltzmann constant in addition to Quantum.
Quantum computer, Quantum algorithm, Quantum, Algorithm and Qubit are her primary areas of study. Particularly relevant to Quantum circuit is her body of work in Quantum computer. Her study in Quantum algorithm is interdisciplinary in nature, drawing from both Quantum network, Theoretical computer science and Computer engineering.
Her Theoretical computer science study integrates concerns from other disciplines, such as Quantum sort and Artificial intelligence. Her work in the fields of Quantum, such as Quantum entanglement, intersects with other areas such as Context. The Algorithm study combines topics in areas such as Polynomial, Set and k-nearest neighbors algorithm.
Krysta M. Svore mostly deals with Quantum algorithm, Quantum computer, Quantum, Algorithm and Computation. Her Quantum algorithm research includes themes of Discrete mathematics, Matrix, Set, Rank and Arithmetic. Krysta M. Svore studies Quantum programming which is a part of Quantum computer.
The various areas that Krysta M. Svore examines in her Quantum study include Optimization problem, Theoretical computer science and Computer engineering. Krysta M. Svore combines subjects such as Machine learning and Polynomial with her study of Algorithm. Computation is often connected to Qubit in her work.
Her primary scientific interests are in Quantum algorithm, Quantum, Quantum computer, Theoretical computer science and Quantum circuit. Her Quantum algorithm research is multidisciplinary, incorporating perspectives in Programming language, Matrix, State, Rank and Density matrix. Her studies deal with areas such as Language model, Optimization problem and Discrete mathematics as well as Quantum.
Krysta M. Svore studies Quantum gate, a branch of Quantum computer. Her Theoretical computer science study deals with Quantum logic intersecting with Domain-specific language, Quantum programming and Functional programming. Her biological study spans a wide range of topics, including Electronic circuit, Computer engineering and Quantum machine learning.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Adapting boosting for information retrieval measures
Qiang Wu;Christopher J. Burges;Krysta M. Svore;Jianfeng Gao.
Information Retrieval (2010)
Elucidating reaction mechanisms on quantum computers
Markus Reiher;Nathan Wiebe;Krysta M. Svore;Dave Wecker.
Proceedings of the National Academy of Sciences of the United States of America (2017)
Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources
Krysta Svore;Lucy Vanderwende;Christopher Burges.
empirical methods in natural language processing (2007)
LIQUi|>: A Software Design Architecture and Domain-Specific Language for Quantum Computing.
Dave Wecker;Krysta M. Svore.
arXiv: Quantum Physics (2014)
One Class Support Vector Machines for Detecting Anomalous Windows Registry Accesses
Katherine Heller;Krysta Svore;Angelos D. Keromytis;Salvatore Stolfo.
Workshop on Data Mining for Computer Security (DMSEC), Melbourne, FL, November 19, 2003 (2003)
Circuit-centric quantum classifiers
Maria Schuld;Alex Bocharov;Krysta M. Svore;Nathan Wiebe;Nathan Wiebe;Nathan Wiebe.
Physical Review A (2020)
A logarithmic-depth quantum carry-lookahead adder
Thomas G. Draper;Samuel A. Kutin;Eric M. Rains;Krysta M. Svore.
Quantum Information & Computation (2006)
Understanding temporal query dynamics
Anagha Kulkarni;Jaime Teevan;Krysta M. Svore;Susan T. Dumais.
web search and data mining (2011)
A layered software architecture for quantum computing design tools
K.M. Svore;A.V. Aho;A.W. Cross;I. Chuang.
IEEE Computer (2006)
Learning to Rank Using an Ensemble of Lambda-Gradient Models
Christopher J. C. Burges;Krysta Marie Svore;Paul N. Bennett;Andrzej Pastusiak.
Proceedings of the Learning to Rank Challenge (2011)
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