A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning

Lindsay, Joseph; Zand, Ramtin

doi:10.1109/isqed57927.2023.10129344

Cited by 3 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section introduces the VQC architecture tailored for QLSTM models, drawing inspiration from previous works by Chen et al (Chen et al, 2022) and Lindsay et al (Lindsay and Zand, 2023). Subsequently, the description regarding the generation process of sine wave data, which serves as a comprehensive yet straightforward dataset for evaluating QLSTM model effectiveness.…”

Section: Proposed Architecture and Methodologymentioning

confidence: 99%

Evaluating the Influence of Adam and Ada-delta Optimizers on Varied Qubit Configurations in QLSTM Models

Namdeo,

Nene

2024

Preprint

View full text Add to dashboard Cite

Quantum Machine Learning (QML) holds the promise of making significant changes to how Artificial Intelligence (AI) functions. A notable breakthrough in this field is the development of Quantum Long Short- Term Memory (QLSTM) networks, which exhibit faster learning compared to standard Long Short-Term Memory (LSTM) networks. QLSTM builds upon classical LSTM networks but incorporates principles of quantum computation, allowing it to explore superposition and entanglement for parallel information processing. By using quantum bits (qubits), QLSTM potentially offers advantages for specific machine learning tasks. Despite its potential, QLSTM remains a relatively unexplored area within QML, particularly regarding its dependency on the number of qubits and the impact of different optimizers on its performance. This study aims to reveal valuable insights to enhance QLSTM and explore its potential applications in AI. The study focuses on two main aspects. Firstly, exploring how the Adam and Ada-delta optimization methods improve QLSTM models, playing a role in refining the performance of the quantum model. Secondly, the study delves into the consequences of using varying numbers of qubits in the QLSTM model. Understanding how the quantity of qubits influences performance is essential for optimizing the network's efficiency. By addressing these aspects, the study seeks to contribute to advancing QLSTM and offering valuable knowledge for its potential applications in the AI field. The findings may suggest exciting opportunities for future research on QLSTM models, prompting exploration into optimization strategies, regularization techniques, and quantum data augmentation. Additionally, the study emphasizes the importance of leveraging ensemble learning and validating applicability in real-world quantum computing scenarios. These efforts are crucial for advancing understanding and maximizing potential in the continually evolving landscape of quantum computing applications.

show abstract

Section: Proposed Architecture and Methodologymentioning

confidence: 99%

Evaluating the Influence of Adam and Ada-delta Optimizers on Varied Qubit Configurations in QLSTM Models

Namdeo,

Nene

2024

Preprint

View full text Add to dashboard Cite

show abstract

Quantum deep neural networks for time series analysis

Padha,

Sahoo

2024

Quantum Inf Process

View full text Add to dashboard Cite

Quantum machine learning with Qiskit: Evaluating regression accuracy and noise impact

Kumar,

Sharma,

Marriwala

et al. 2024

IET Quantum Communication

View full text Add to dashboard Cite

Quantum machine learning (QML) can be employed in solving complicated machine learning tasks although the performance in examining the regression processes is only barely understood. Knowledge gaps are intended to be closed by studying modelling performance of QML in regression tasks, with emphasis being dedicated to scaling up and ability to resist noise. The regression part offers the following functions that include straight line and complex operations. Furthermore, the authors employ quantum neural networks generated using Qiskit to perform experiments. The results demonstrate that QML has a remarkable level of accuracy in basic regressions, reaching a maximum of 97%. Nevertheless, there are difficulties in representing intricate functions, such as 5 × cos(x), which results in a noticeable decline in performance. The work deals with the influence of noise and IERs from imperfect hardware on the efficiency of QML algorithms providing insight into the core obstacles. The result of a detailed examination of the results that have tested the powers and limits of QML in the development of regression applications is represented. The future direction of research and development will be defined by the results obtained in it.

show abstract

A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning

Cited by 3 publications

References 29 publications

Evaluating the Influence of Adam and Ada-delta Optimizers on Varied Qubit Configurations in QLSTM Models

Evaluating the Influence of Adam and Ada-delta Optimizers on Varied Qubit Configurations in QLSTM Models

Quantum deep neural networks for time series analysis

Quantum machine learning with Qiskit: Evaluating regression accuracy and noise impact

Contact Info

Product

Resources

About