A quantum tug of war between randomness and symmetries on homogeneous spaces
This work provides a foundational perspective on symmetry and randomness in quantum information, potentially impacting quantum computing and machine learning, though it appears incremental in applying existing mathematical concepts to this domain.
The authors tackled the problem of characterizing symmetry and randomness in quantum information by introducing a geometric framework using homogeneous spaces and Haar measures, and demonstrated its application by studying the expressibility of quantum machine learning ansatze.
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.