A quantum tug of war between randomness and symmetries on homogeneous spaces
arXiv:2309.05253 · doi:10.1103/PhysRevResearch.7.013105
Abstract
We explore the interplay between symmetry and randomness in quantum information. Adopting a geometric approach, we consider states as -equivalent if related by a symmetry transformation characterized by the group . We then introduce the Haar measure on the homogeneous space , characterizing true randomness for -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 -wise independent approximations and defining -designs on and -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.
9 + 1 pages, 3 figures
References in corpus (13)
- Quantum convolutional neural network for classical data classification
- Exploiting symmetry in variational quantum machine learning
- Group-Invariant Quantum Machine Learning
- Computational advantage of quantum random sampling
- Introduction to Haar Measure Tools in Quantum Information: A Beginner's Tutorial
- Theory for Equivariant Quantum Neural Networks
- Classical Shadow Tomography with Locally Scrambled Quantum Dynamics
- Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
- Building spatial symmetries into parameterized quantum circuits for faster training
- Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze
- Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification
- On the universality of -equivariant -body gates
- Clifford Group and Unitary Designs under Symmetry