scholarly journals Hybrid Quantum-Classical Neural Networks for Text Classification

2021 ◽  
Author(s):  
Dhruv Baronia
Keyword(s):  
Neural Networks ◽  
Quantum Computing ◽  
Text Classification ◽  
Classical Model ◽  
Classification Problem ◽  
Quantum Circuit ◽  
Hybrid Models ◽  
Proof Of Concept ◽  
Intermediate Scale

Quantum Computing presents an interesting paradigm where it can possibly offer certain improvements and additions to a classical network while training. This method is particularly prevalent in the current Noisy Intermediate-Scale Quantum era, where we can test these theories using libraries such as Pennylane in conjunction with robust ML frameworks such as TensorFlow. This paper presents a proof-of-concept for the same, using a hybrid quantum-classical model to solve a text classification problem on the IMDB Movie Sentiment Dataset. These hybrid models utilize precalculated embeddings and dense layers alongside a variational quantum circuit layer. We created 4 such models, utilizing various kinds of embeddings, namely NNLM-128, NNLM-50, Swivel and USE, using TFHub and Pennylane. We also trained classical versions of these models, without the variational quantum layer to evaluate the performances. All models were trained on the same data, keeping the parameters constant.

scholarly journals Hybrid Quantum-Classical Neural Networks for Text Classification

2021 ◽  
Author(s):  
Dhruv Baronia
Keyword(s):  
Neural Networks ◽  
Quantum Computing ◽  
Text Classification ◽  
Classical Model ◽  
Classification Problem ◽  
Quantum Circuit ◽  
Hybrid Models ◽  
Proof Of Concept ◽  
Intermediate Scale

Quantum Computing presents an interesting paradigm where it can possibly offer certain improvements and additions to a classical network while training. This method is particularly prevalent in the current Noisy Intermediate-Scale Quantum era, where we can test these theories using libraries such as Pennylane in conjunction with robust ML frameworks such as TensorFlow. This paper presents a proof-of-concept for the same, using a hybrid quantum-classical model to solve a text classification problem on the IMDB Movie Sentiment Dataset. These hybrid models utilize precalculated embeddings and dense layers alongside a variational quantum circuit layer. We created 4 such models, utilizing various kinds of embeddings, namely NNLM-128, NNLM-50, Swivel and USE, using TFHub and Pennylane. We also trained classical versions of these models, without the variational quantum layer to evaluate the performances. All models were trained on the same data, keeping the parameters constant.

scholarly journals Quantum Computing for Dealing with Inaccurate Knowledge using the Certainty Factors Model

2021 ◽  
Author(s):  
Vicente Moret-Bonillo ◽  
Samuel Magaz-Romero ◽  
Eduardo Mosqueira-Rey
Keyword(s):  
Quantum Computing ◽  
Procedural Knowledge ◽  
Classical Model ◽  
Quantum Probability ◽  
Quantum Circuit ◽  
Point Of View ◽  
Use Case ◽  
Strongly Correlated ◽  
Inexact Reasoning ◽  
Probabilistic Nature

In this paper we try to demonstrate that the classical model of certainty factos for dealing with innacurate knowledge can be efficiently implemented in a quantum environment. For this, we assume that certainty factors are strongly correlated with the quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. We then build a use case: an inferential network to be implemented in both, the classical approach and the corresponding quantum circuit. Both implementations have been used to compare the behavior of the classical and the quantum approaches when confronted with the same hypothetical case. We analyze three different situations: (1) Only Imprecision (which refers to inaccuracy in declarative knowledge or facts) is present in the use case, (2) Only Uncertainty (which refers to inaccuracy in procedural knowledge or rules) is present in the use case, and (3) Both Imprecision and Uncertainty are present in the use case. Finally, we analyze the results to reach a conclusion about the eventually intrinsic probabilistic nature of the certainty factors model and to pave the way for future quantum implementations of this method for handling inaccurate knowledge.

scholarly journals Tensor Graph Convolutional Networks for Text Classification

2020 ◽  
Vol 34 (05) ◽  
pp. 8409-8416
Author(s):  
Xien Liu ◽  
Xinxin You ◽  
Xiao Zhang ◽  
Ji Wu ◽  
Ping Lv
Keyword(s):  
Neural Networks ◽  
Text Classification ◽  
Contextual Information ◽  
Classification Problem ◽  
Heterogeneous Information ◽  
Convolutional Networks ◽  
Benchmark Datasets ◽  
Graph Propagation ◽  
Single Graph ◽  
New Framework

Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate graph-based neural networks for text classification problem. A new framework TensorGCN (tensor graph convolutional networks), is presented for this task. A text graph tensor is firstly constructed to describe semantic, syntactic, and sequential contextual information. Then, two kinds of propagation learning perform on the text graph tensor. The first is intra-graph propagation used for aggregating information from neighborhood nodes in a single graph. The second is inter-graph propagation used for harmonizing heterogeneous information between graphs. Extensive experiments are conducted on benchmark datasets, and the results illustrate the effectiveness of our proposed framework. Our proposed TensorGCN presents an effective way to harmonize and integrate heterogeneous information from different kinds of graphs.

scholarly journals Higgs analysis with quantum classifiers

2021 ◽  
Vol 251 ◽  
pp. 03070
Author(s):  
Vasilis Belis ◽  
Samuel González-Castillo ◽  
Christina Reissel ◽  
Sofia Vallecorsa ◽  
Elías F. Combarro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Quantum Circuit ◽  
Feature Reduction ◽  
Support Vector ◽  
Proof Of Concept ◽  
Quantum Devices ◽  
Training Samples ◽  
Quantum Machine Learning ◽  
Reduction Methods

We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limitations in both simulation hardware and real quantum hardware — we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification

2021 ◽  
pp. 1-10
Author(s):  
Zhipeng Tan ◽  
Jing Chen ◽  
Qi Kang ◽  
MengChu Zhou ◽  
Abdullah Abusorrah ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification

scholarly journals Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3087
Author(s):  
Sandi Ljubic ◽  
Franko Hržić ◽  
Alen Salkanovic ◽  
Ivan Štajduhar
Keyword(s):  
Magnetic Field ◽  
Neural Networks ◽  
Curve Fitting ◽  
Absolute Error ◽  
Interaction Space ◽  
Text Entry ◽  
Proof Of Concept ◽  
Field Sensor ◽  
Device Interaction ◽  
Input Techniques

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).

scholarly journals Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 135-149
Author(s):  
James Flynn ◽  
Cinzia Giannetti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Urban Areas ◽  
Proof Of Concept ◽  
Major Step ◽  
Dominant Form ◽  
Residential Properties ◽  
The Uk ◽  
Ev Charging ◽  
Unique Dataset

With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.

scholarly journals Deep neural networks for quantum circuit mapping

2021 ◽  
Author(s):  
Giovanni Acampora ◽  
Roberto Schiattarella
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Machine Learning Techniques ◽  
Quantum Computers ◽  
Physical Constraints ◽  
Proper Mapping ◽  
Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

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