Quantum Machine Learning
88 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Quantum Machine Learning
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Libraries
Use these libraries to find Quantum Machine Learning models and implementations
iris
Latest papers with no code
Learning to Program Variational Quantum Circuits with Fast Weights
This paper introduces the Quantum Fast Weight Programmers (QFWP) as a solution to the temporal or sequential learning challenge.
Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
However, challenges such as (1) the encoding of data from the classical to the quantum domain, (2) hyperparameter tuning, and (3) the integration of quantum hardware into a distributed computing continuum limit the adoption of quantum machine learning for urgent analytics.
A Quick Introduction to Quantum Machine Learning for Non-Practitioners
This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches.
Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks
Quantum Neural Networks (QNNs) are a popular approach in Quantum Machine Learning due to their close connection to Variational Quantum Circuits, making them a promising candidate for practical applications on Noisy Intermediate-Scale Quantum (NISQ) devices.
Quantum Embedding with Transformer for High-dimensional Data
Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators.
Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
In this study, we assess the efficacy of such attacks in the realm of quantum computing.
Generating Universal Adversarial Perturbations for Quantum Classifiers
In this work, we introduce QuGAP: a novel framework for generating UAPs for quantum classifiers.
Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in expressive power over classical machine learning models are believed to be infeasible as such QNNs take a time to train that is exponential in the model size.
Geometric quantum machine learning of BQP$^A$ protocols and latent graph classifiers
In this Letter we consider Simon's problem for learning properties of Boolean functions, and show that this can be related to an unsupervised circuit classification problem.
Unleashing the Expressive Power of Pulse-Based Quantum Neural Networks
Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ) devices requires the optimal utilization of limited quantum resources.
