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Quantum Machine Learning
A quantum tug of war between randomness and symmetries on homogeneous spaces
arXiv
Authors: Rahul Arvind, Kishor Bharti, Jun Yong Khoo, Dax Enshan Koh, Jian Feng Kong
Year
2023
Paper ID
54991
Status
Preprint
Abstract Read
~2 min
Abstract Words
166
Citations
N/A
Abstract
We explore the interplay between symmetry and randomness in quantum information. Adopting a geometric approach, we consider states as H-equivalent if related by a symmetry transformation characterized by the group H. We then introduce the Haar measure on the homogeneous space mathbb{U}/H, characterizing true randomness for H-equivalent systems. While this mathematical machinery is well-studied by mathematicians, it has seen limited application in quantum information: we believe our work to be the first instance of utilizing homogeneous spaces to characterize symmetry in quantum information. This is followed by a discussion of approximations of true randomness, commencing with t-wise independent approximations and defining t-designs on mathbb{U}/H and H-equivalent states. Transitioning further, we explore pseudorandomness, defining pseudorandom unitaries and states within homogeneous spaces. Finally, as a practical demonstration of our findings, we study the expressibility of quantum machine learning ansatze in homogeneous spaces. Our work provides a fresh perspective on the relationship between randomness and symmetry in the quantum world.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- We explore the interplay between symmetry and randomness in quantum information.
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