23 citations · 39 across the 5 of their papers we have counts for
5 papers
Variational Quantum Generative Modeling by Sampling Expectation Values of Tunable Observables
Kevin Shen, Andrii Kurkin, Adrián Pérez-Salinas +3
Expectation Value Samplers (EVSs) are quantum generative models that can learn high-dimensional continuous distributions by measuring the expectation values of parameterized quantu…
Classification of the Fashion-MNIST Dataset on a Quantum Computer
Kevin Shen, Bernhard Jobst, Elvira Shishenina +1
The potential impact of quantum machine learning algorithms on industrial applications remains an exciting open question. Conventional methods for encoding classical data into quan…
Evaluating Ground State Energies of Chemical Systems with Low-Depth Quantum Circuits and High Accuracy
Shuo Sun, Chandan Kumar, Kevin Shen +2
Solving electronic structure problems is considered one of the most promising applications of quantum computing. However, due to limitations imposed by the coherence time of qubits…
Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
Bernhard Jobst, Kevin Shen, Carlos A. Riofrío +2
Machine learning tasks are an exciting application for quantum computers, as it has been proven that they can learn certain problems more efficiently than classical ones. Applying…
Let Quantum Neural Networks Choose Their Own Frequencies
Ben Jaderberg, Antonio A. Gentile, Youssef Achari Berrada +2
Parameterized quantum circuits as machine learning models are typically well described by their representation as a partial Fourier series of the input features, with frequencies u…