Quantum Machine Learning
89 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
Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data
Quantum Machine Learning (QML) has come into the limelight due to the exceptional computational abilities of quantum computers.
Understanding quantum machine learning also requires rethinking generalization
In this work, through systematic randomization experiments, we show that traditional approaches to understanding generalization fail to explain the behavior of such quantum models.
Enhancing variational quantum state diagonalization using reinforcement learning techniques
iitis/RL_for_VQSD_ansatz_optimization
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We demonstrate that the circuits proposed by the reinforcement learning methods are shallower than the standard variational quantum state diagonalization algorithm and thus can be used in situations where hardware capabilities limit the depth of quantum circuits.
Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms
jannatulshapna/QML-Software-Supply-Chain
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Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively.
Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning
anthonysmaldone/qcnn-multi-channel-supervised-learning
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As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to provide speed up for machine learning algorithms is becoming increasingly desirable.
Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs.
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning
kclip/quantum-cp
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In this work, we aim at augmenting the decisions output by quantum models with "error bars" that provide finite-sample coverage guarantees.
Generalization with quantum geometry for learning unitaries
Generalization is the ability of quantum machine learning models to make accurate predictions on new data by learning from training data.
Spacetime-Efficient Low-Depth Quantum State Preparation with Applications
When our protocol is compiled into CNOT and arbitrary single-qubit gates, it prepares an $N$-dimensional state in depth $O(\log(N))$ and spacetime allocation (a metric that accounts for the fact that oftentimes some ancilla qubits need not be active for the entire circuit) $O(N)$, which are both optimal.
Fourier series weight in quantum machine learning
In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model.
