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Concentration Bounds for Quantum States and QAOA Limitations

Squid: Schools for Quantum Information Development via YouTube

Overview

Explore a conference talk from TQC 2023 that delves into concentration bounds for quantum states and their implications for quantum approximate optimization algorithm (QAOA). Learn about groundbreaking research demonstrating concentration bounds for output states of shallow quantum circuits, injective matrix product states, and dense Hamiltonian evolution states. Discover how polynomial approximations prove these states' proximity to local operators, leading to concentrated distributions in computational basis measurements. Understand the significant implications for QAOA, including new limitations on dense instances at super-constant level, showing that for random spin models, QAOA can only succeed with negligible probability at level p = o(log log n). Follow along as the presentation covers introduction to concentration bounds, their applications, local approximations, shallow quantum circuits, dense Hamiltonian evolution, and concludes with insights into QCOA limitations.

Syllabus

Introduction
Concentration bounds
Why use concentration bounds
Local approximations
Local operators and concentration
Shallow quantum circuits
Dense Hamiltonian Evolution
QCOA
Questions

Taught by

Squid: Schools for Quantum Information Development

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