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We study fracton topological codes as a framework for quantum error correction, showing that their sub-extensive ground state degeneracy provides natural protection against local errors.

We study the performance of the Quantum Approximate Optimization Algorithm (QAOA), proving concentration of parameters and providing implementation strategies for near-term quantum hardware.

We present PennyLane, a Python library for differentiable programming of quantum computers that seamlessly integrates classical machine learning libraries with quantum hardware and simulators.