DNA Computing Models for Boolean Circuits and Logic Gates

2015 ◽  
Author(s):  
Kuntala Boruah ◽  
Jiten Ch. Dutta
Keyword(s):  
Dna Computing ◽  
Logic Gates ◽  
Boolean Circuits ◽  
Computing Models

scholarly journals Additive-error fine-grained quantum supremacy

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 329
Author(s):  
Tomoyuki Morimae ◽  
Suguru Tamaki
Keyword(s):  
Quantum Computing ◽  
Complexity Theory ◽  
Polynomial Time ◽  
Boolean Circuits ◽  
Exponential Time ◽  
Fine Grained ◽  
Additive Error ◽  
Universal Quantum ◽  
The One ◽  
Computing Models

It is known that several sub-universal quantum computing models, such as the IQP model, the Boson sampling model, the one-clean qubit model, and the random circuit model, cannot be classically simulated in polynomial time under certain conjectures in classical complexity theory. Recently, these results have been improved to ``fine-grained" versions where even exponential-time classical simulations are excluded assuming certain classical fine-grained complexity conjectures. All these fine-grained results are, however, about the hardness of strong simulations or multiplicative-error sampling. It was open whether any fine-grained quantum supremacy result can be shown for a more realistic setup, namely, additive-error sampling. In this paper, we show the additive-error fine-grained quantum supremacy (under certain complexity assumptions). As examples, we consider the IQP model, a mixture of the IQP model and log-depth Boolean circuits, and Clifford+T circuits. Similar results should hold for other sub-universal models.

DNA Computing

2010 ◽  
Vol 2 (4) ◽  
pp. 12-37
Author(s):  
Tao Song ◽  
Xun Wang ◽  
Shudong Wang ◽  
Yun Jiang
Keyword(s):  
Dna Computing ◽  
Medical Science ◽  
Theoretical Framework ◽  
Biomolecular Computing ◽  
Dna Encoding ◽  
Research Directions ◽  
Hamilton Path ◽  
Complete Problems ◽  
Intractable Problems ◽  
Computing Models

DNA computing is widely accepted as a new computing framework all over the world. In this chapter, the background of DNA computing is firstly introduced by solving a Hamilton Path problem. Then three research directions are proposed according to the current development of it, including the theoretical framework, practical DNA computing models and DNA encoding. In each part of the three research directions, many recent results are involved. In the theoretical framework, DNA computing is proved to be computationally universal by four formal DNA computing models. In practical DNA computing models, DNA computing is shown to solve NP-complete problems and work well in other fields, such as medical science. In DNA encoding, some DNA codes and encoding methods are introduced to avoid the false positive phenomenon. And they have a final purpose in common: constructing a universal Biomolecular computing model, which is also called as biomolecular computer, to solve intractable problems for electrical computers. Finally, some further research directions are shown in each part for the design of biomolecular computer.

Performing Balanced Ternary Logic and Arithmetic Operations with Spiking Neural P Systems with Anti-Spikes

2012 ◽  
Vol 505 ◽  
pp. 378-385 ◽  
Author(s):  
Xian Wu Peng ◽  
Xiao Ping Fan ◽  
Jian Xun Liu
Keyword(s):  
Logic Gates ◽  
P Systems ◽  
P System ◽  
Ternary Logic ◽  
Arithmetic Operations ◽  
Spiking Neural P Systems ◽  
Addition And Subtraction ◽  
Distributed And Parallel Computing ◽  
Computing Models ◽  
Natural Way

Spiking neural P systems are a class of distributed and parallel computing models inspired by P systems and spiking neural networks.Spiking neural P system with anti-spikes can encode the balanced ternary three digits in a natural way using three states called anti-spikes, no-input and spikes. In this paper we use this variant of SN P system to simulate universal balanced ternary logic gates including AND,OR and NOT gate and to perform some basic balanced ternary arithmetic operations like addition and subtraction on balanced ternary integers. This paper provides an applicational answer to an open problem formulated by L.Pan and Gh. Păun.

A Review on DNA Computing Models

2007 ◽  
Vol 4 (7) ◽  
pp. 1219-1230 ◽  
Author(s):  
Jin Xu ◽  
Gangjun Tan
Keyword(s):  
Dna Computing ◽  
Computing Models

scholarly journals DNA Computing: Models and Implementations

2002 ◽  
Vol 7 (3) ◽  
pp. 177-198 ◽  
Author(s):  
Mark J. Daley ◽  
Lila Kari
Keyword(s):  
Dna Computing ◽  
Computing Models

Plasmid Resolving the Satisfiability Problem with DNA Computing Models

2007 ◽  
Vol 4 (7) ◽  
pp. 1243-1248
Author(s):  
Zhi-Xiang Yin ◽  
Jian-Zhong Cui ◽  
Wenbin Liu ◽  
Xiao-Hong Shi ◽  
Jin Xu
Keyword(s):  
Dna Computing ◽  
Satisfiability Problem ◽  
Computing Models

scholarly journals Photochemical NOT Gate for DNA Computing

2020 ◽  
Author(s):  
Cole Emanuelson ◽  
Anirban Bardhan ◽  
Alexander Deiters
Keyword(s):  
Output Signal ◽  
Dna Computing ◽  
Logic Gates ◽  
Specific Pattern ◽  
Boolean Logic ◽  
Dna Computation ◽  
Circuit Performance ◽  
Optical Activation ◽  
Computational Circuits ◽  
Time Point

AbstractDNA-based Boolean logic gates (AND, OR and NOT) can be assembled into complex computational circuits that generate an output signal in response to specific patterns of oligonucleotide inputs. However, the fundamental nature of NOT gates, which convert the absence of an input into an output, makes their implementation within DNA-based circuits difficult. Premature execution of a NOT gate before completion of its upstream computation introduces an irreversible error into the circuit. We developed a novel DNA gate design utilizing photocaging groups that prevents gate function until irradiation at a certain time-point. Optical activation provides temporal control over circuit performance by preventing premature computation and is orthogonal to all components of DNA computation devices. Using this approach, we designed NAND and NOR logic gates that respond to synthetic microRNA inputs. We further demonstrate the utility of the NOT gate within multi-layer circuits in response to a specific pattern of four microRNAs.

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