Obstacles to State Preparation and Variational Optimization from Symmetry Protection

APA

(2020). Obstacles to State Preparation and Variational Optimization from Symmetry Protection. The Simons Institute for the Theory of Computing. https://simons.berkeley.edu/talks/obstacles-state-preparation-and-variational-optimization-symmetry-protection

MLA

Obstacles to State Preparation and Variational Optimization from Symmetry Protection. The Simons Institute for the Theory of Computing, May. 07, 2020, https://simons.berkeley.edu/talks/obstacles-state-preparation-and-variational-optimization-symmetry-protection

BibTex

          @misc{ scivideos_15632,
            doi = {},
            url = {https://simons.berkeley.edu/talks/obstacles-state-preparation-and-variational-optimization-symmetry-protection},
            author = {},
            keywords = {},
            language = {en},
            title = {Obstacles to State Preparation and Variational Optimization from Symmetry Protection},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2020},
            month = {may},
            note = {15632 see, \url{https://scivideos.org/Simons-Institute/15632}}
          }
          
Robert König (Technical University of Munich)
Source Repository Simons Institute

Abstract

Local Hamiltonians with topological quantum order exhibit highly entangled ground states that cannot be prepared by shallow quantum circuits. Here, we show that this property may extend to all low-energy states in the presence of an on-site Z2 symmetry. This proves a version of the No Low-Energy Trivial States (NLTS) conjecture for a family of local Hamiltonians with symmetry protected topological order. A surprising consequence of this result is that the Goemans-Williamson algorithm outperforms the Quantum Approximate Optimization Algorithm (QAOA) for certain instances of MaxCut, at any constant level. We argue that the locality and symmetry of QAOA severely limits its performance. To overcome these limitations, we propose a non-local version of QAOA, and give numerical evidence that it significantly outperforms standard QAOA for frustrated Ising models on random 3-regular graphs. This is joint work with Sergey Bravyi, Alexander Kliesch and Eugene Tang.