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Found 15 papers, 3 papers with code
Adiabatic Quantum Support Vector Machines
Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with increasing number of features and number of data points in the training dataset.
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments
We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment.
A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices
The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers.
SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing
Currently available simulators are catered to either neuroscience workflows (such as NEST and Brian2) or deep learning workflows (such as BindsNET).
Encoding Integers and Rationals on Neuromorphic Computers using Virtual Neuron
They are expected to be indispensable for energy-efficient computing in the future.
A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification
On simulated hardware, we observe that the hybrid neural network achieves roughly 10% higher classification accuracy and 20% better minimization of cost than an individual variational quantum circuit.
Discriminating Quantum States with Quantum Machine Learning
Quantum machine learning (QML) algorithms have obtained great relevance in the machine learning (ML) field due to the promise of quantum speedups when performing basic linear algebra subroutines (BLAS), a fundamental element in most ML algorithms.
Neuromorphic Computing is Turing-Complete
Given that the {\mu}-recursive functions and operators are precisely the ones that can be computed using a Turing machine, this work establishes the Turing-completeness of neuromorphic computing.
Quantum Discriminator for Binary Classification
Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces -- this is sought to give them a competitive advantage over classical computers.
Training Deep Neural Networks with Constrained Learning Parameters
We believe that deep neural networks (DNNs), where learning parameters are constrained to have a set of finite discrete values, running on neuromorphic computing systems would be instrumental for intelligent edge computing systems having these desirable characteristics.
Adiabatic Quantum Optimization Fails to Solve the Knapsack Problem
In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer.
Balanced k-Means Clustering on an Adiabatic Quantum Computer
We present a quantum approach to solving the balanced $k$-means clustering training problem on the D-Wave 2000Q adiabatic quantum computer.
QUBO Formulations for Training Machine Learning Models
In this paper, we formulate the training problems of three machine learning models---linear regression, support vector machine (SVM) and equal-sized k-means clustering---as QUBO problems so that they can be trained on adiabatic quantum computers efficiently.
Adiabatic Quantum Linear Regression
A major challenge in machine learning is the computational expense of training these models.
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware.
