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Found 10 papers, 4 papers with code
Automating quantum feature map design via large language models
Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces.
Quantum Kernel t-Distributed Stochastic Neighbor Embedding
Since quantum states are higher dimensional objects that can only be seen via observables, our visualization method, which inherits the similarity of quantum data, would be useful in understanding the behavior of quantum circuits and algorithms.
Continuous optimization by quantum adaptive distribution search
In this paper, we introduce the quantum adaptive distribution search (QuADS), a quantum continuous optimization algorithm that integrates Grover adaptive search (GAS) with the covariance matrix adaptation - evolution strategy (CMA-ES), a classical technique for continuous optimization.
MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era
In fact, also the Machine Learning research related to quantum computers undertakes a dual challenge: enhancing machine learning exploiting the power of quantum computers, while also leveraging state-of-the-art classical machine learning methodologies to help the advancement of quantum computing.
noisy quantum circuit classification (quantum ML, error mitigation)
quantum circuit classification (classical ML)
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The cross-sectional stock return predictions via quantum neural network and tensor network
In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions.
Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network
In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data.
Quantum tangent kernel
In this work, we explore a quantum machine learning model with a deep parameterized quantum circuit and aim to go beyond the conventional quantum kernel method.
Variational Quantum Algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost.
Methodology for replacing indirect measurements with direct measurements
However, in certain cases, the indirect measurement can be reduced to the direct measurement, where a quantum state is destructively measured.
Quantum Physics
Quantum Circuit Learning
Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.
Quantum Physics
