SU(d)-Symmetric Random Unitaries: Quantum Scrambling, Error Correction, and Machine Learning

• Published in: arXiv.org
• Main Authors: Li, Zimu, Han, Zheng, Wang, Yunfei, Jiang, Liang, Zi-Wen, Liu, Liu, Junyu
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 04.10.2023
• Subjects: Data processing; Decay; Error correction; Hilbert space; Machine learning; Quantum computing; Quantum phenomena; Qubits (quantum computing); Subspaces; Symmetry
• ISSN: 2331-8422

Quantum information processing in the presence of continuous symmetry is of wide importance and exhibits many novel physical and mathematical phenomena. SU(d) is a continuous symmetry group of particular interest since it represents a fundamental type of non-Abelian symmetry and also plays a vital role in quantum computation. Here, we explicate the applications of SU(d)-symmetric random unitaries in three different contexts ranging from physics to quantum computing: information scrambling with non-Abelian conserved quantities, covariant quantum error correcting random codes, and geometric quantum machine learning. First, we show that, in the presence of SU(d) symmetry, the local conserved quantities would exhibit residual values even at \(t \rightarrow \infty\) which decays as \(\Omega(1/n^{3/2})\) under local Pauli basis for qubits and \(\Omega(1/n^{(d+2)^2/2})\) under local symmetric basis for general qudits with respect to the system size, in contrast to \(O(1/n)\) decay for U(1) case and the exponential decay for no-symmetry case in the sense of out-of-time ordered correlator (OTOC). Second, we show that SU(d)-symmetric unitaries can be used to construct asymptotically optimal (in the sense of saturating the fundamental limits on the code error that have been called the approximate Eastin-Knill theorems) SU(d)-covariant codes that encodes any constant \(k\) logical qudits, extending [Kong \& Liu; PRXQ 3, 020314 (2022)]. Finally, we derive an overpartameterization threshold via the quantum neural tangent kernel (QNTK) required for exponential convergence guarantee of generic ansatz for geometric quantum machine learning, which reveals that the number of parameters required scales only with the dimension of desired subspaces rather than that of the entire Hilbert space. We expect that our work invites further research on quantum information with continuous symmetries.