Novel Encoding method for Quantum Error Correction

Swathi, Mummadi; Rudra, Bhawana

doi:10.1109/ccwc54503.2022.9720880

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…Majumdar et al [23] offer an error estimation technique through sparse scheduling, that can help lower the quantity of error correcting blocks needed. Swathi et al [24] proposed a qubit encoding method for the correction of quantum errors. Kukulski et al [8] used probabilistic error correction methods to correct the measurement errors.…”

Section: Related Workmentioning

confidence: 99%

Real-time hybrid facial expression analysis model using quantum distance-based classifier and classical artificial neural networks: QUADCANN

Rengasamy,

Joshi,

Raveendra

2024

Quantum Inf Process

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Facial expressions bring human interactions to life with nonverbal cues that convey hues of emotions, feelings, and cultural intent. Analyzing these expressions is essential in the age of digital transformation. Traditional approaches to real-time facial expression analysis have limitations in capturing the complexity of these expressions and consume enormous computational power. The promising computing capacity of quantum computers is poised to solve current problems and set new standards by meeting growing demands. We have created a hybrid model that leverages the capabilities of the classical and quantum domains. The proposed model uses a quantum distance-based classifier along with classical artificial neural networks to perform real-time facial expression analysis and classification. Based on a thorough review of the literature, this novel and comprehensive work is, to the best of our knowledge, one of the few that addresses eight different emotions in the quantum domain. The implementation of a novel quantum error correction method has improved the accuracy of this hybrid model. Our model was trained on the CK+ facial expression database. To ensure fairness of the study, we tested our model on the largest facial expression database, AffectNet-8, Sponsored by TATA Consultancy Services

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Section: Related Workmentioning

confidence: 99%

Real-time hybrid facial expression analysis model using quantum distance-based classifier and classical artificial neural networks: QUADCANN

Rengasamy,

Joshi,

Raveendra

2024

Quantum Inf Process

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“…Next, quantum error detection and correction (QED/C) methods, like stabilizer codes, continuously measure the quantum waves, called syndromes [2]. While the quantum states are not directly measured since that would cause the amplitudinal wave to collapse into a particle [3], the parity of qubits is automatically adjusted based on measurements to correct the quantum state [4]. The QI is then decoded, and the transmission is complete.…”

Section: Quantum Information Processingmentioning

confidence: 99%

“…Phase-flip errors in turn represent the most prevalent type of QC error, and as such most QED/C research focuses on pfe prevention [20]. However, the third and most difficult type of quantum error to detect and correct is the combined bpfe, in which both the original and quantum states are altered during QIP [3]. While combination errors are rare, they represent one of the most difficult QED/C problems to solve.…”

Section: Common Causes Of Decoherence On Nisq Devicesmentioning

confidence: 99%

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Mechanizing Quantum Error Correction Through Entangled Quantum Machine Learning Techniques

Melvin

2023

Quantum Computing - Innovations and Applications in Modern Research

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Noisy intermediate scale quantum (NISQ) systems are susceptible to errors that culminate in near-one hundred percent data loss. This is due to quantum state fragility and the incredibly high quantum communication error rates caused by decoherence, or quantum noise. As such, stabilizing qubit operational imprecision in quantum information processing is a critical area of research in quantum computing. Adaptive quantum machine learning (QML) methods, like unsupervised and fully entangled quantum generative adversarial networks is one such technology theorized to provide a breakthrough in quantum error suppression. Mechanizing the quantum error detection and correction process with QML provides a path forward from today’s monolithic quantum computers running almost exclusively single-core quantum processing unit (QPU) designs, to the next generation of federated quantum computers using multi-core QPUs. Automating the detection and correction of quantum errors in powerful NISQ devices will pave the way for fault-tolerant quantum computing, making quantum speeds at quantum scale suddenly achievable.

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“…This research focuses on BER analysis and reduction in quantum communication, an area concomitantly connected with quantum error correction advancements. Notably, innovative encoding methods [19] and the development of deep quantum error decoders [20] directly contribute to BER reduction, enhancing communication reliability. This study underscores the crucial interplay between BER reduction efforts and quantum error correction strategies, aiming to improve quantum communication fidelity and integrity.…”

Section: Introductionmentioning

confidence: 99%

Exploiting OFDM method for quantum communication

Sabaawi,

Almasaoodi,

Imre

2024

Quantum Inf Process

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Orthogonal frequency-division multiplexing (OFDM) is a crucial modulation method used in contemporary digital communication systems for its significant spectral efficiency, low latency, and robustness in challenging environments. This work examines the novel use of OFDM in quantum communication, an area that offers exceptional security and efficiency in information transfer using quantum mechanics principles. In the rapidly evolving field of quantum computing, understanding, and mitigating quantum bit errors is paramount. This paper presents a rigorous analysis of bit error rates (BER) in quantum circuits, focusing on the impact of the quantum Fourier transform and its inverse, contrasted against quantum circuits employing dynamic gate sequences. Our research methodology encompasses simulations over a diverse set of parameters, including varying qubit counts ranging from 2 to 8 and theta angles (15, 30, 45, and 60°), as well as random theta values, utilizing the advanced capabilities of the Qiskit framework. Our findings indicate that quantum OFDM substantially improves quantum communication, lowering errors and boosting security. The quantum model outperforms the reference model in BER, with further enhancements as qubits increase.

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Novel Encoding method for Quantum Error Correction

Cited by 13 publications

References 33 publications

Real-time hybrid facial expression analysis model using quantum distance-based classifier and classical artificial neural networks: QUADCANN

Real-time hybrid facial expression analysis model using quantum distance-based classifier and classical artificial neural networks: QUADCANN

Mechanizing Quantum Error Correction Through Entangled Quantum Machine Learning Techniques

Exploiting OFDM method for quantum communication

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