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Quantum-Dot Cellular Automata

Quantum-Dot Cellular Automata is a research topic within Computational Theory and Mathematics. Science Explorer counts 9.4k research works in it since 1950. 19.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the design, simulation, and implementation of Quantum-dot Cellular Automata (QCA) for molecular computing and nanotechnology applications. It covers topics such as logic design, fault tolerance, adder circuits, clocking schemes, power dissipation analysis, and reversible logic. The research explores the potential of QCA as an alternative computing paradigm at the nanoscale.

  • Quantum-dot Cellular Automata
  • QCA
  • Molecular Computing
  • Nanotechnology
  • Logic Design
  • Fault Tolerance
  • Adder Circuits
  • Clocking Schemes
  • Power Dissipation Analysis
  • Reversible Logic
Research works
9.4k
fractional, since 1950
In the world top 10%
1.8k
per year above
Top-10% rate
19.1%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+44%
the tick is no change

Which countries lead Quantum-Dot Cellular Automata research?

By volume, India and China publish the most (589 and 498 works in 2022–2025).

By volume, 2022–2025

  1. 1 India 589 works
  2. 2 China 498 works
  3. 3 United States 354 works
  4. 4 Japan 107 works
  5. 5 Germany 83 works
  6. 6 Iran 76 works
  7. 7 United Kingdom 74 works
  8. 8 South Korea 69 works
  9. 9 Italy 56 works
  10. 10 Canada 51 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

India: 30.1%China: 25.4%United States: 18.1%Japan: 5.5%6 others listed: 20.9%30%largest
India589 · 30.1%China498 · 25.4%United States354 · 18.1%Japan107 · 5.5%6 others listed410 · 20.9%

Shares of the rows listed above, not of the whole node.

Which institutions lead Quantum-Dot Cellular Automata research?

By volume in 2022–2025, National University of Singapore publishes the most Quantum-Dot Cellular Automata research, followed by Hefei University of Technology and Technical University of Munich.

By volume, 2022–2025

  1. 1 National University of Singapore Singapore 17 works
  2. 2 Hefei University of Technology China 16 works
  3. 3 Technical University of Munich Germany 15 works
  4. 4 Vellore Institute of Technology University India 12 works
  5. 5 Saveetha University India 12 works
  6. 6 SRM Institute of Science and Technology India 11 works
  7. 7 Chinese Academy of Sciences China 11 works
  8. 8 Amrita Vishwa Vidyapeetham India 11 works
  9. 9 Beijing University of Posts and Telecommunications China 11 works
  10. 10 Tsinghua University China 10 works

Who are the leading researchers in Quantum-Dot Cellular Automata?

The most-cited researchers publishing on Quantum-Dot Cellular Automata include Takashi Taniguchi, Mehdi Hosseinzadeh and Leon O. Chua.

  1. 1 Takashi Taniguchi 6.2k citations
  2. 2 Mehdi Hosseinzadeh 4.2k citations
  3. 3 Leon O. Chua 4.1k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Quantum-Dot Cellular Automata research done?

The largest centres of Quantum-Dot Cellular Automata research in 2022–2025 are Beijing (China), Chennai (India), Seoul (South Korea) and Hefei (China).

Largest cities, 2022–2025

  1. 1 Beijing China 113 works
  2. 2 Chennai India 48 works
  3. 3 Seoul South Korea 39 works
  4. 4 Hefei China 38 works
  5. 5 Bengaluru India 38 works
  6. 6 Tokyo Japan 36 works
  7. 7 Nanjing China 36 works
  8. 8 Singapore Singapore 34 works
  9. 9 Shanghai China 33 works
  10. 10 Kolkata India 30 works
See Quantum-Dot Cellular Automata on the map

Where is the best place to study Quantum-Dot Cellular Automata?

Among universities, judged by research, Technical University of Munich, Hefei University of Technology and National University of Singapore score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 20.17%fractional works in this node (log) →share in the world top 10% →Technical University of Munich: 15, 49.4%Hefei University of Technology: 16, 7.0%National University of Singapore: 17, 8.7%Shri Mata Vaishno Devi University: 9, 26.2%Amrita Vishwa Vidyapeetham: 11, 12.6%Nanyang Technological University: 10, 30.6%Delft University of Technology: 9, 31.9%University of Engineering & Management: 9, 13.2%University of Dhaka: 9, 11.0%Chitkara University: 8, 11.1%Technical University…Shri Mata Vaishno De…National University …Hefei University of …
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1Technical University of Munich Germany 69.249.4%7.6×15 -48.4%
2Hefei University of Technology China 53.87.0%11.8×16 +93.7%
3National University of Singapore Singapore 50.18.7%7.2×17 -30.3%
4Shri Mata Vaishno Devi University India 49.526.2%86.1×9
5Amrita Vishwa Vidyapeetham India 46.612.6%8.0×11 +483.3%
6Nanyang Technological University Singapore 46.030.6%4.9×10 +17.4%
7Delft University of Technology Netherlands 44.031.9%5.5×9 +31.7%
8University of Engineering & Management India 40.513.2%45.2×9
9University of Dhaka Bangladesh 40.411.0%16.0×9 +41.1%
10Chitkara University India 31.811.1%8.4×8

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Quantum-Dot Cellular Automata research growing?

Output in 2018–2022 was 44% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Quantum-Dot Cellular Automata.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.