Ashwin Nayak

2.9k citations
48 papers · 1.3k indexed · h-index 16

Ashwin Nayak

47 papers receiving 1.2k citations

Peers

Ashwin Nayak
Comparison fields: 5 of 46
  • Artificial Intelligence 1.2k
  • Computational Theory and Mathematics 592
  • Atomic and Molecular Physics, and Optics 503
  • Statistics and Probability 27
  • Statistical and Nonlinear Physics 33
Replace Alain Tapp with:
Alain Tapp Canada
David Gosset United States
Wim van Dam United States
A. Shen China
Frédéric Magniez France
Peter Selinger Canada
Gianpiero Cattaneo Italy
Ross Duncan United Kingdom
Héctor Bombín Spain
Paweł Wocjan United States
Ashwin Nayak relative to Alain Tapp Canada Alain Tapp's profile →
Citations per field
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Citations per year

Countries citing papers authored by Ashwin Nayak

Since Specialization
Citations

This map shows the geographic impact of Ashwin Nayak's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ashwin Nayak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ashwin Nayak more than expected).

Fields of papers citing papers by Ashwin Nayak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ashwin Nayak. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ashwin Nayak. The network helps show where Ashwin Nayak may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Ashwin Nayak, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ashwin Nayak Line = papers co-authored together Ashwin Nayak links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20252
2 20235
3 20222
4 20212
5 20201
6
Quantum Analogs of Markov Chains
20151
7 20145
8
20110
9
The space complexity of recognizing well-parenthesized expressions
20101
10 201034
11 20102
12 200912
13
On the hitting times of quantum versus random walks
200842
14
Direct Product Theorems for Communication Complexity via Subdistribution Bounds.
20071
15 200617
16 200414
17 200397
18 2001303
19 199911
20 19985

About Ashwin Nayak

Ashwin Nayak is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics and Statistics and Probability, having authored 48 papers that have together received 1.3k indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (38 papers), Quantum Information and Cryptography (31 papers), Quantum Mechanics and Applications (13 papers), Quantum-Dot Cellular Automata (11 papers), Machine Learning and Algorithms (8 papers), Complexity and Algorithms in Graphs (6 papers), Computability, Logic, AI Algorithms (6 papers) and Cryptography and Data Security (3 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Computational Theory and Mathematics (592 citations), Atomic and Molecular Physics, and Optics (503 citations), Statistics and Probability (27 citations) and Statistical and Nonlinear Physics (33 citations). Ashwin Nayak has collaborated with scholars based in Canada, United States and France. Frequent co-authors include Andris Ambainis, Frédéric Magniez, Amnon Ta‐Shma, Umesh Vazirani, John Watrous, Ashvin Vishwanath, Eric Bach, Miklós Sántha, Jérémie Roland and Hartmut Klauck. Their work appears in journals such as IEEE Transactions on Information Theory, Quantum Information and Computation, SIAM Journal on Computing, Journal of the ACM and Random Structures and Algorithms.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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