Daniel K. Mark

814 citations
10 papers · 462 · 1 hit paper · h-index 8

Impact in

Papers in

Daniel K. Mark

9 papers receiving 458 citations

Hit Papers

Preparing random states and benchmarking with many-body quantum chaos 2023 · 97 citations
970+1+2Years since publication255075

Peers

Daniel K. Mark
Comparison fields: 5 of 34
  • Computational Mathematics 11
  • Atomic and Molecular Physics, and Optics 403
  • Condensed Matter Physics 79
  • Statistical and Nonlinear Physics 82
  • Artificial Intelligence 180
Replace Colin Rylands with:
Colin Rylands Italy
Katja Klobas United Kingdom
Yijian Zou United States
Tibor Rakovszky United States
Matteo Ippoliti United States
Oliver Buerschaper Germany
Marek Gluza Germany
Federica Maria Surace United States
Duan-Lu Zhou China
Filiberto Ares Italy
Daniel K. Mark relative to Colin Rylands Italy Colin Rylands's profile →
Citations per field
00.5×1.5×2.3×
Colin Rylands · 1×
Citations per year

Countries citing papers authored by Daniel K. Mark

Since Specialization
Citations

This map shows the geographic impact of Daniel K. Mark'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 Daniel K. Mark with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel K. Mark more than expected).

Fields of papers citing papers by Daniel K. Mark

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel K. Mark. 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 Daniel K. Mark. The network helps show where Daniel K. Mark may publish in the future.

Co-authors

The 24 scholars most cited alongside Daniel K. Mark, 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 Daniel K. Mark Line = papers co-authored together Daniel K. Mark links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 2020119
2
Preparing random states and benchmarking with many-body quantum chaos
Hit paper breakdown →
202397
3 202082
4 202363
5 202326
6 202425
7 202323
8 202021
9
Emergent Randomness and Benchmarking from Many-Body Quantum Chaos
20216
10 20250

About Daniel K. Mark

Daniel K. Mark is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence, Condensed Matter Physics, Statistical and Nonlinear Physics and Spectroscopy, having authored 10 papers that have together received 462 indexed citations. Recurring topics across this work include Quantum many-body systems (9 papers), Quantum Computing Algorithms and Architecture (4 papers), Quantum Information and Cryptography (3 papers), Cold Atom Physics and Bose-Einstein Condensates (3 papers), Quantum and electron transport phenomena (2 papers), Physics of Superconductivity and Magnetism (2 papers), Theoretical and Computational Physics (1 paper) and Quantum chaos and dynamical systems (1 paper). The work is most often cited by research in Computational Mathematics (11 citations), Atomic and Molecular Physics, and Optics (403 citations), Condensed Matter Physics (79 citations), Statistical and Nonlinear Physics (82 citations) and Artificial Intelligence (180 citations). Daniel K. Mark has collaborated with scholars based in United States, Singapore and Canada. Frequent co-authors include Olexei I. Motrunich, Cheng-Ju Lin, Soonwon Choi, Manuel Endres, Adam L. Shaw, Joonhee Choi, Xueyue Zhang, Eunjong Kim, Oskar Painter and Ran Finkelstein. Their work appears in journals such as Physical review. B., Nature, Physical Review Letters, Physical Review X and Science.

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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