28 citations · 57 across the 7 of their papers we have counts for
7 papers
Quantum State Preparation via Neural Network Encoding in Quantum Machine Learning
Kevin W. Aoun, Florian J. Kiwit, Carlos A. Riofrío +4
A central challenge in quantum machine learning is the state preparation bottleneck that describes the prohibitive computational cost of loading high-dimensional classical data int…
Scaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation
Jonas Jäger, Florian J. Kiwit, Carlos A. Riofrío
Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning. Contemporary quantum machine learning is generally limit…
Efficient decoupling of a non-linear qubit mode from its environment
Frederik Pfeiffer, Max Werninghaus, Christian Schweizer +21
To control and measure the state of a quantum system it must necessarily be coupled to external degrees of freedom. This inevitably leads to spontaneous emission via the Purcell ef…
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…
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
Florian J. Kiwit, Marwa Marso, Philipp Ross +3
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training t…
A performance characterization of quantum generative models
Carlos A. Riofrío, Oliver Mitevski, Caitlin Jones +6
Quantum generative modeling is a growing area of interest for industry-relevant applications. With the field still in its infancy, there are many competing techniques. This work is…
A practical overview of image classification with variational tensor-network quantum circuits
Diego Guala, Shaoming Zhang, Esther Cruz +3
Circuit design for quantum machine learning remains a formidable challenge. Inspired by the applications of tensor networks across different fields and their novel presence in the…