Implementation of Reversible Logic Gates with Quantum Gates

Swathi, Mummadi; Rudra, Bhawana

doi:10.1109/ccwc51732.2021.9376060

Cited by 29 publications

(7 citation statements)

References 8 publications

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“…Qubits differ from conventional bits as they can exist in any of 0 and 1 states at the same time, a behavior called superposition. We define a quantum state

\mid \phi \Big\rangle =\left(\gamma |0\Big\rangle +\delta |1\Big\rangle \right)

, where

p(0)

{\left|\gamma \right|}^2,\kern0.5em p(1)

{\left|\delta \right|}^2

and

{\left|\gamma \right|}^2+{\left|\delta \right|}^2=1

, and

\gamma

and

\delta

are complex vectors 43 …”

Section: Methodsmentioning

confidence: 99%

“…{U}^{\dagger }=I $$, where

U

is conjugate transpose of a matrix

{U}^{\dagger }

and

I

is identity matrix. Based on number of qubits, quantum gates can divided into three categories: single‐qubit, double‐qubit, and multiple‐qubit gates 43 …”

Section: Methodsmentioning

confidence: 99%

“…Based on number of qubits, quantum gates can divided into three categories: single-qubit, double-qubit, and multiple-qubit gates. 43…”

Section: Quantum Gatesmentioning

confidence: 99%

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Quantum‐inspired hybrid algorithm for image classification and segmentation: Q‐Means++ max‐cut method

Roy,

Rudra

2024

Int J Imaging Syst Tech

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Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)‐based method for brain tumor extraction and detection based on quantum computing. To implement our strategy, we use a Hybrid Quantum‐Classical Convolutional Neural Network (HQC‐CNN) that has been trained using a collection of brain MRI images. Additionally, we employ Batchwise Q‐Means++ Clustering for segmenting the images and a Max‐cut approach with Adiabatic Quantum Computation (AQC) to extract the tumor region from the segmented MRI image. Our results highlight the strength of Quanvolutional Layer in Neural Network and reduced time complexity exponentially or quadratically in clustering and max‐cut algorithms respectively and see the potential of quantum computing for improving the accuracy and speed of medical diagnosis and have implications for the future of healthcare technology.

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“…Qubits differ from conventional bits as they can exist in any of 0 and 1 states at the same time, a behavior called superposition. We define a quantum state

\mid \phi \Big\rangle =\left(\gamma |0\Big\rangle +\delta |1\Big\rangle \right)

, where

p(0)

{\left|\gamma \right|}^2,\kern0.5em p(1)

{\left|\delta \right|}^2

and

{\left|\gamma \right|}^2+{\left|\delta \right|}^2=1

, and

\gamma

and

\delta

are complex vectors 43 …”

Section: Methodsmentioning

confidence: 99%

“…{U}^{\dagger }=I $$, where

U

is conjugate transpose of a matrix

{U}^{\dagger }

and

I

is identity matrix. Based on number of qubits, quantum gates can divided into three categories: single‐qubit, double‐qubit, and multiple‐qubit gates 43 …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Quantum‐inspired hybrid algorithm for image classification and segmentation: Q‐Means++ max‐cut method

Roy,

Rudra

2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Quantum information is measured in the form of qubits. A qubit is a superposition of multiple quantum states [42]. Quantum states are represented with a state matrix which is the combination of complex state vectors.…”

Section: Preliminaries For Quantum Computation a Structure Of A Quant...mentioning

confidence: 99%

A Novel Approach for Asymmetric Quantum Error Correction With Syndrome Measurement

Swathi

Rudra

2022

IEEE Access

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Most of the quantum error correction methods are symmetric. Symmetric methods are implemented by considering the amplitude of bit flip(X) and phase flip(Z) errors as same. With the quantum experiments, it is observed that the amplitude of Z errors are more compared to X errors. Due to which the need of asymmetric error correction has increased. This paved a path for the development of asymmetric error correction methods. In this paper, we discussed the concept of asymmetric quantum error correction (AQEC) and proposed an efficient approach for AQEC with encoding, syndrome measurement and decoding operations with increased fidelity to 85.89% and reduced circuit depth to 48%.

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“…Many studies about the application of quantum logic circuits [1]- [3] have been introduced and proved ever before. Two main problems solved by quantum logic circuits are reducing circuit power consumption and increasing the density of transistors in an area of layout circuit.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Reversible and Quantum Circuit Using ROCBDD and Mixed-Polarity Toffoli Gate

Nguyen

Tran

2021

IEEE Access

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In the last decade, the synthesis of reversible logic circuits has become a trending topic because of its future necessity and importance. Many methods have been studied and proposed, for instance, transformation-based, search-based, cycle-based, ESOP-based and BDD-based methods. Each of them has its limitation related to time processing, ancilla and garbage line, quantum cost.This study develops an algorithm that could synthesize quantum circuits based on mixed-polarity Toffoli gate and a variant of binary decision diagram (BDD) called reduced-ordered-complemented edge-bdd (ROCBDD). It is an optimized method of BDD to reduce nodes in the representation of Boolean functions, which leads to adding more lines and quantum gates in the reversible circuit. First, the differences between synthesis using ROCBDD, the traditional BDD and others methods are introduced. Then, we define a new structure consisting of mixed-polarity Toffoli gates relied on nodes of ROCBDD. An efficient algorithm to match BDD representation to the reversible logic circuit is also mentioned. Finally, the experimental results show that our algorithm has better synthesizing costs than previous BDD-based methods.

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Implementation of Reversible Logic Gates with Quantum Gates

Cited by 29 publications

References 8 publications

Quantum‐inspired hybrid algorithm for image classification and segmentation: Q‐Means++ max‐cut method

Quantum‐inspired hybrid algorithm for image classification and segmentation: Q‐Means++ max‐cut method

A Novel Approach for Asymmetric Quantum Error Correction With Syndrome Measurement

Synthesis of Reversible and Quantum Circuit Using ROCBDD and Mixed-Polarity Toffoli Gate

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