Reversible Logic Gates and Applications – A Low Power Solution to VLSI Chips
Bibliographic record
Abstract
In recent years, reversible logic gates have garnered significant interest because of their potential to decrease energy consumption and meet the growing need for low-power computing systems. Unlike conventional logic gates, reversible logic gates ensure that no information loss happens during computation, allowing for the reversal of the entire computation process. This unique characteristic opens up new avenues for developing energy-efficient digital circuits. This review paper serves as a vital contribution to the field by addressing a noticeable gap in the existing literature regarding reversible logic gates. The study not only comprehensively analyzes the array of reversible logic gates available but also underscores their practical applications and significance. It encompasses a wide variety of reversible logic gates, including Toffoli gates, Fredkin gates, and newer innovations. It is found that Toffoli gates outperformed in terms of gate count and quantum cost reduction, making them a preferred choice for quantum circuit optimization. Additionally, Fredkin gates showed exceptional performance in specific applications, like data swapping and quantum state control. The digital circuits like adders, multiplexers, ALU etc. are successfully designed using reversible gates like HNG, DKG etc. The significant gap this study fills lies in the need for a consolidated and in-depth analysis of the state-of-the-art reversible logic gates and their real-world utility. While prior research has discussed these gates individually, this paper takes a novel approach by offering a holistic assessment of their performance, quantum cost, gate count, and practical applications, thereby presenting a comprehensive resource for researchers, engineers, and designers in the field. This innovative contribution plays a pivotal role in shaping the progress of energy-efficient and quantum computing systems as well as in optimizing VLSI chip designs for various applications, with a particular emphasis on enhancing cryptographic and data processing capabilities. The findings of this review aim to stimulate further research and development in reversible computing, contributing to the advancement of energy-efficient and information-preserving computing systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".