Quantum Wasserstein GANs for State Preparation at Unseen Points of a Phase Diagram
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Abstract (arXiv)
Generative models and in particular Generative Adversarial Networks (GANs) have become very popular and powerful data generation tool. In recent years, major progress has been made in extending this concept into the quantum realm. However, most of the current methods focus on generating classes of states that were supplied in the input set and seen at the training time. In this work, we propose a new hybrid classical-quantum method based on quantum Wasserstein GANs that overcomes this limitation. It allows to learn the function governing the measurement expectations of the supplied states and generate new states, that were not a part of the input set, but which expectations follow the same underlying function.
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References · 12
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017Improved Training of Wasserstein GANscited by 2,235
- 2018Quantum generative adversarial networkscited by 128
- 2019Quantum Adversarial Machine Learningcited by 67
- 2020Quantum State Tomography with Conditional Generative Adversarial Networkscited by 41
- 2019Quantum Wasserstein Generative Adversarial Networkscited by 33
- 2018Learning and Inference on Generative Adversarial Quantum Circuitscited by 22
- 2018Adversarial quantum circuit learning for pure state approximationcited by 16
- 2021Learning quantum data with the quantum Earth Mover's distancecited by 13
- 2013Matrix-valued Monge-Kantorovich Optimal Mass Transportcited by 4
- 2016Matrix Optimal Mass Transport: A Quantum Mechanical Approachcited by 4
- 2020Quantum statistical learning via Quantum Wasserstein natural gradientcited by 1
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