1. Holographic deep thermalization
- Author
-
Zhang, Bingzhi, Xu, Peng, Chen, Xiaohui, and Zhuang, Quntao
- Subjects
Quantum Physics ,Condensed Matter - Disordered Systems and Neural Networks ,Condensed Matter - Statistical Mechanics ,Nonlinear Sciences - Chaotic Dynamics - Abstract
Random quantum states play a critical role in quantum information processing. While random quantum circuits typically provide pseudo-random states, deep thermalization introduces quantum measurement to generate genuinely random states. However, the requirement of large ancillae in conventional deep thermalization poses a challenge to scale up the system size. We introduce holographic deep thermalization to substantially reduce the required ancillae to a system-size independent constant. Our circuit design trades space with time, via adopting a sequential application of an scrambling-measure-reset process on a small number of ancillae. Via tuning the ancilla size and number of time steps, holographic deep thermalization allows a continuous trade-off between the total quantum circuit size and the ancilla size. In the case of finite-size systems, we further enhance the performance of holographic deep thermalization via generative quantum machine learning, which leads to constant-factor advantages in the convergence towards Haar random. The theoretical predictions are verified with IBM Quantum noisy simulations., Comment: 6+10 pages, 12 figures
- Published
- 2024