Quantum-Classical Sentiment Analysis

• URL: http://arxiv.org/abs/2409.16928v1
• Date: Wed, 25 Sep 2024 13:40:19 GMT
• Title: Quantum-Classical Sentiment Analysis
• Authors: Mario Bifulco, Luca Roversi,
• Abstract summary: We investigate the application of a hybrid classical-quantum classifier (HCQC) for sentiment analysis. Our findings indicate that while the HCQC underperforms relative to the Transformer in terms of classification accuracy, but it requires significantly less time to converge to a reasonably good approximate solution. This experiment also reveals a critical bottleneck in the HCQC, whose architecture is partially undisclosed by the D-Wave property.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we initially investigate the application of a hybrid classical-quantum classifier (HCQC) for sentiment analysis, comparing its performance against the classical CPLEX classifier and the Transformer architecture. Our findings indicate that while the HCQC underperforms relative to the Transformer in terms of classification accuracy, but it requires significantly less time to converge to a reasonably good approximate solution. This experiment also reveals a critical bottleneck in the HCQC, whose architecture is partially undisclosed by the D-Wave property. To address this limitation, we propose a novel algorithm based on the algebraic decomposition of QUBO models, which enhances the time the quantum processing unit can allocate to problem-solving tasks.

Related papers

• Projective Quantum Eigensolver with Generalized Operators [0.0]
  We develop a methodology for determining the generalized operators in terms of a closed form residual equations in the PQE framework. With the application on several molecular systems, we have demonstrated our ansatz achieves similar accuracy to the (disentangled) UCC with singles, doubles and triples.
  arXiv  Detail & Related papers  (2024-10-21T15:40:22Z)
• Compressed-sensing Lindbladian quantum tomography with trapped ions [44.99833362998488]
  Characterizing the dynamics of quantum systems is a central task for the development of quantum information processors. We propose two different improvements of Lindbladian quantum tomography (LQT) that alleviate previous shortcomings.
  arXiv  Detail & Related papers  (2024-03-12T09:58:37Z)
• Benchmarking hybrid digitized-counterdiabatic quantum optimization [2.983864486954652]
  Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of near-term quantum computers. In this study, we analyze the convergence behavior and solution quality of various classicals when used in conjunction with the digitized-counterdiabatic approach.
  arXiv  Detail & Related papers  (2024-01-18T10:05:07Z)
• Real-time error mitigation for variational optimization on quantum hardware [45.935798913942904]
  We define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. Our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function.
  arXiv  Detail & Related papers  (2023-11-09T19:00:01Z)
• Variational Quantum Approximate Spectral Clustering for Binary Clustering Problems [0.7550566004119158]
  We introduce the Variational Quantum Approximate Spectral Clustering (VQASC) algorithm. VQASC requires optimization of fewer parameters than the system size, N, traditionally required in classical problems. We present numerical results from both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-09-08T17:54:42Z)
• An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees [71.57324258813675]
  We propose a novel methodology for addressing the hyperspectral image deconvolution problem. A new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network. The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium framework.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• Toward Neural Network Simulation of Variational Quantum Algorithms [1.9723551683930771]
  Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of optimization. We ask if classical optimization algorithms can be constructed paralleling other VQAs, focusing on the example of the variational quantum linear solver (VQLS) We find that such a construction can be applied to the VQLS, yielding a paradigm that could theoretically extend to other VQAs of similar form.
  arXiv  Detail & Related papers  (2022-11-05T15:46:47Z)
• Faster variational quantum algorithms with quantum kernel-based surrogate models [0.0]
  We present a new method for small-to-intermediate scale variational algorithms on noisy quantum processors. Our scheme shifts the computational burden onto the classical component of these hybrid algorithms, greatly reducing the number of queries to the quantum processor.
  arXiv  Detail & Related papers  (2022-11-02T14:11:25Z)
• Oracle separations of hybrid quantum-classical circuits [68.96380145211093]
  Two models of quantum computation: CQ_d and QC_d. CQ_d captures the scenario of a d-depth quantum computer many times; QC_d is more analogous to measurement-based quantum computation. We show that, despite the similarities between CQ_d and QC_d, the two models are intrinsically, i.e. CQ_d $nsubseteq$ QC_d and QC_d $nsubseteq$ CQ_d relative to an oracle.
  arXiv  Detail & Related papers  (2022-01-06T03:10:53Z)
• Quantum Machine Learning with SQUID [64.53556573827525]
  We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
  arXiv  Detail & Related papers  (2021-04-30T21:34:11Z)
• An optimal quantum sampling regression algorithm for variational eigensolving in the low qubit number regime [0.0]
  We introduce Quantum Sampling Regression (QSR), an alternative hybrid quantum-classical algorithm. We analyze some of its use cases based on time complexity in the low qubit number regime. We demonstrate the efficacy of our algorithm for a benchmark problem.
  arXiv  Detail & Related papers  (2020-12-04T00:01:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.