Quantum Architectures and Computation Group (QuArC)
Microsoft Research
One Microsoft Way
Redmond, WA 98052, USA
E-mail: ksvore at microsoft dot com
Tel: (425) 421-6996 Fax: (425) 936-7329
About Me
I am a Researcher and the manager of the Quantum Architectures and Computation Group (QuArC) at Microsoft Research in Redmond, WA. I am passionate about quantum computation and determining what problems can be better solved on a quantum computer. My research focuses on quantum algorithms and how to implement them on a quantum device, from how to code them in a high-level programming language, to how to optimize the resources they require, to how to implement them on the hardware. Our team works on designing a scalable, fault-tolerant software architecture for translating a high-level quantum program into a low-level, device-specific quantum implementation, which we call LIQUi|>; (pronounced "liquid", language-integrated quantum operations). We work in collaboration with Microsoft Station Q, Microsoft Research's center for topological quantum computation.
Other research interests include machine learning algorithms, both classical and quantum. I am interested in learning to rank algorithms, Web search and information retrieval, including features and training methods, and the dynamics of the Web and its users over time.
I received my Ph.D. with Honors in Computer Science from Columbia University in 2006 under Dr. Alfred Aho and Dr. Joseph Traub. I spent one year as a visiting researcher at MIT under Dr. Isaac Chuang, and several months at Caltech under Dr. John Preskill and Dr. Panos Aliferis. I interned for several years at IBM Research under Dr. David DiVincenzo and Dr. Barbara Terhal. I received a B.A. in Mathematics, with a minor in Computer Science and French, from Princeton University in 2001.
2013
- Krysta M. Svore, Matthew B. Hastings, and Michael Freedman, Faster Phase Estimation, April 2013
- Alex Bocharov, Yuri Gurevich, and Krysta M. Svore, Efficient Decomposition of Single-Qubit Gates into V Basis Circuits , 6 March 2013
- Kira Radinsky, Krysta M. Svore, Susan T. Dumais, Milad Shokouhi, Jaime Teevan, Alex Bocharov, and Eric Horvitz, Behavioral Dynamics on the Web: Learning, Modeling and Prediction, in ACM Transactions on Information Systems, ACM, 2013
2012
- Alex Bocharov and Krysta M. Svore, Resource-Optimal Single-Qubit Quantum Circuits, in Physical Review Letters, vol. 109, no. 190501, pp. 5, American Physical Society, 8 November 2012
- Karthik Raman, Krysta M. Svore, Ran Gilad-Bachrach, and Chris Burges, Learning from Our Mistakes: Towards a Correctable Learning Algorithm, in 21st International Conference on Information and Knowledge Management (CIKM), ACM, 27 October 2012
- Guillaume Duclos-Cianci and Krysta M. Svore, A State Distillation Protocol to Implement Arbitrary Single-qubit Rotations, , October 2012
- Krysta M. Svore, Jaime Teevan, Susan Dumais, and Anagha Kulkarni, Creating Temporally Dynamic Web Search Snippets, in Proceedings of SIGIR, Association for Computing Machinery, Inc., August 2012
- Paul Pham and Krysta M. Svore, A 2D Nearest-Neighbor Quantum Architecture for Factoring, in Reversible Computing 2012, , July 2012
- Jagadeesh Jagarlamudi, Paul N. Bennett, and Krysta M. Svore, Leveraging Interlingual Classification to Improve Web Search, in Proceedings of the 21st International World Wide Web (WWW) Conference, International World Wide Web Conference, 16 April 2012
- Kira Radinsky, Krysta M. Svore, Susan T. Dumais, Jaime Teevan, Alex Bocharov, and Eric Horvitz, Modeling and Predicting Behavioral Dynamics on the Web, in Proceedings of the 21st International World Wide Web (WWW) Conference, International World Wide Web Conference, 16 April 2012
2011
- Paul N. Bennett, Khalid El-Arini, Thorsten Joachims, and Krysta M. Svore, Enriching Information Retrieval, in ACM Sigir Forum, vol. 45, no. 2, pp. 60-65, ACM, December 2011
- Krysta M. Svore and Christopher J.C. Burges, Large-scale Learning to Rank using Boosted Decision Trees, in Scaling Up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, May 2011
- Krysta M. Svore, Maksims N. Volkovs, and Christopher J.C. Burges, Learning to Rank with Multiple Objective Functions, in Proceedings of WWW 2011, International World Wide Web Conference, March 2011
- Anagha Kulkarni, Jaime Teevan, Krysta M. Svore, and Susan T. Dumais, Understanding Temporal Query Dynamics, in Web Search and Data Mining (WSDM) 2011, Association for Computing Machinery, Inc., February 2011
- Christopher J.C. Burges, Krysta M. Svore, Paul N. Bennett, Andrzej Pastusiak, and Qiang Wu, Learning to Rank using an Ensemble of Lambda-Gradient Models, in Journal of Machine Learning Research: Workshop and Conference Proceedings, vol. 14, pp. 25-35, Journal of Machine Learning Research, February 2011
2010
- Krysta M. Svore and Christopher J.C. Burges, Learning to Rank on a Cluster using Boosted Decision Trees, in Learning to Rank on Cores, Clusters, and Clouds Workshop at NIPS 2010, December 2010
- Krysta M. Svore, Pallika Kanani, and Nazan Khan, How good is a span of terms? Exploiting Proximity to Improve Web Retrieval, in Proceedings of SIGIR, Association for Computing Machinery, Inc., July 2010
- Grace Hui Yang, Anton Mityagin, Krysta M. Svore, and Sergey Markov, Collecting High Quality Overlapping Labels at Low Cost, in Proceedings of SIGIR, Association for Computing Machinery, Inc., July 2010
- Paul N. Bennett, Krysta M. Svore, and Susan T. Dumais, Classification-enhanced Ranking, in Proceedings of World Wide Web, Association for Computing Machinery, Inc., 26 April 2010
2009
- Krysta Svore and Chris Burges, A Machine Learning Approach for Improved BM25 Retrieval, in Conference on Information Knowledge Management (CIKM), Association for Computing Machinery, Inc., November 2009
- Qiang Wu, Chris Burges, Krysta Svore, and Jianfeng Gao, Adapting boosting for information retrieval measures, in Information Retrieval, Springer Verlag, 19 August 2009
- Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Svore, Yi Su, Nazan Khan, Shalin Shah, and Hongyan Zhou, Model Adaptation via Model Interpolation and Boosting for Web Search Ranking, in EMNLP, Association for Computational Linguistics, August 2009
- Krysta M. Svore and Christopher J.C. Burges, A Machine Learning Approach for Improved BM25 Retrieval, no. MSR-TR-2009-92, 30 July 2009
- Pinar Donmez, Krysta M. Svore, and Christoper J.C. Burges, On the Local Optimality of LambdaRank, in SIGIR, Association for Computing Machinery, Inc., July 2009
2008
- Pinar Donmez, Krysta Svore, and Christopher J.C. Burges, On the Optimality of LambdaRank, no. MSR-TR-2008-179, November 2008
- Qiang Wu, Christopher J.C. Burges, Krysta Svore, and Jianfeng Gao, Ranking, Boosting, and Model Adaptation, no. MSR-TR-2008-109, October 2008
- Krysta M. Svore, Lucy Vanderwende, and Christopher J.C. Burges, Using Signals of Human Interest to Enhance Single-document Summarization, in Association for the Advancement of Artificial Intelligence (AAAI), Association for the Advancement of Artificial Intelligence, July 2008
2007
- Krysta Svore, Lucy Vanderwende, and Chris Burges, Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources, in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Association for Computational Linguistics, 2007
- Krysta M. Svore, David P. DiVincenzo, and Barbara M. Terhal, Noise threshold for a fault-tolerant two-dimensional lattice architecture, in Quantum Information and Computation, vol. 7, no. 4, pp. 297-318, Rinton Press, January 2007
- K.M. Svore, Q. Wu, C.J.C. Burges, and A. Raman, Improving Web Spam Classification using Rank-time Features, in Proceedings of Adversarial Information Retrieval on the Web (AIRWeb), January 2007
2006
- Krysta M. Svore, Andrew W. Cross, Isaac L. Chuang, and Alfred V. Aho, A flow-map model for analyzing pseudothresholds in fault-tolerant quantum computing, in Quantum Information and Computation, vol. 6, no. 3, pp. 193-212, Rinton Press, December 2006
- Krysta M. Svore, Alfred V. Aho, Andrew W. Cross, Isaac Chuang, and Igor L. Markov, A Layered Software Architecture for Quantum Computing Design Tools, in IEEE Computer, vol. 06, no. 0018-9162, pp. 58-67, IEEE Computer Society, January 2006
- Tom Draper, Samuel Kutin, Eric Rains, and Krysta M. Svore, A Logarithmic-depth Quantum Carry-Lookahead Adder, in Quantum Information and Computation, vol. 6, no. 4-5, pp. 351-369, Rinton Press, January 2006
2005
- Salvatore Stolfo, Frank Apap, Eleazar Eskin, Katherine Heller, Shlomo Hershkop, Andrew Honig, and Krysta M. Svore, A comparative evaluation of two algorithms for Windows Registry anomaly detection, in Journal of Computer Security, vol. 13, no. 4, pp. 659-693, IOS Press, October 2005
- Krysta M. Svore, Barbara M. Terhal, and David P. DiVincenzo, Local fault-tolerant quantum computation, in Physical Review A, vol. 72, no. 022317, American Physical Society, January 2005
2004
- Krysta M. Svore, Andrew Cross, Alfred V. Aho, Isaac Chuang, and Igor Markov, Toward a Software Architecture for Quantum Computing Design Tools, in Proceedings of Quantum Programming Languages (QPL), July 2004
2003
- Krysta M. Svore and Alfred V. Aho, The Design and Optimization of Quantum Circuits using the Palindrome Transform, in Proceedings of the ERATO Conference on Quantum Information Sciences (EQIS), September 2003
 |
Quantum Computation for Quantum Chemistry: Status, Challenges and Prospects - Session 5 Krysta Svore and Dave Wecker
|
|
|
Quantum Computation for Quantum Chemistry: Status, Challenges, and Prospects - Session 1 Michael Freedman, Krysta Svore, Matthias Troyer, and Markus Reiher
|
|
|
Is Scalable, Reliable Quantum Computation Possible? Krysta Svore
|
|
|
Learning to Rank on a Cluster using Boosted Decision Trees Krysta Svore 11 December 2010
NIPS 2010 Workshop: Learning to Rank on Cores, Clusters and Clouds.
We investigate the problem of learning to rank on a cluster using Web search data composed of 140,000 queries and approximately fourteen million URLs, and a boosted tree ranking algorithm called LambdaMART. We compare to a baseline algorithm that has been carefully engineered to allow training on the full dataset using a single machine, in order to evaluate the loss or gain incurred by the distributed algorithms we consider. Our contributions are two-fold: (1) we implement a method for improving the speed of training when the training data fits in main memory on a single machine; (2) we develop a training method for the case where the training data size exceeds the main memory of a single machine that easily scales to far larger datasets, i.e., billions of examples, and is based on data distribution. Results of our methods on a real-world Web dataset indicate significant improvements in training speed.
|
 |
Understanding Temporal Query Dynamics Krysta SvoreFebruary 2011 4th International Conference on Web Search and Data Mining (WSDM '11)
Web search is strongly influenced by time. The queries people issue change over time, with some queries occasionally spiking in popularity (e.g., earthquake) and others remaining relatively constant (e.g., youtube). The documents indexed by the search engine also change, with some documents always being about a particular query (e.g., the Wikipedia page on earthquakes is about the query earthquake) and others being about the query only at a particular point in time (e.g., the New York Times is only about earthquakes following a major seismic activity). The relationship between documents and queries can also change as people's intent changes (e.g., people sought different content for the query earthquake before the Haitian earthquake than they did after). In this paper, we explore how queries, their associated documents, and the query intent change over the course of 10 weeks by analyzing query log data, a daily Web crawl, and periodic human relevance judgments. We identify several interesting features by which changes to query popularity can be classified, and show that presence of these features, when accompanied by changes in result content, can be a good indicator of change in query intent |
Contents
Upcoming Events
-
Microsoft Faculty Summit 2013; July 15-16, 2013, Redmond, WA; Featured speakers include Scott Aaronson, Charlie Marcus, and Matthias Troyer
-
Aspen Winter Conference on Advances in Quantum Algorithms and Computation, March 9-14, 2014, Aspen, CO
Past Events
- WSDM 2012, Seattle, WA
- Workshop on Enriching Information Retrieval (ENIR 2011)at SIGIR 2011, Beijing, China ENIR 2011 Call for Papers
