-
University of Southern California
- Los Angeles, CA
- https://qserver.usc.edu/blog/2021/08/amy-brown/
- https://orcid.org/0000-0001-6664-6494
- in/amyfbrown
Starred repositories
A JIT compiled chess engine which traverses the search tree in batches in a best-first manner, allowing for neural network batching, asynchronous GPU use, and vectorized CPU computations.
Efficient numerical calculation of generalized filter functions
SCQC and BARQ implementation based on JAX.
metriq-gym is a framework for implementing and running standard quantum benchmarks on different quantum devices by different providers
Research presentations, lectures, tutorials, and technical notes by Dr. Zlatko K. Minev
Tools for constructing and analyzing quantum low density parity check (qLDPC) codes. Also stabilizer and subsystem codes more broadly.
The Classiq Library is the largest collection of quantum algorithms and applications. It is the best way to explore quantum computing software. We welcome community contributions to our Library 🙌
Rigetti Resource Estimation (RRE) tool
Qᴜᴀʟᴛʀᴀɴ is a Python library for expressing and analyzing Fault Tolerant Quantum algorithms.
Resource estimation for fault-tolerant quantum computation.
Binder repository of interactive tutorial notebooks for pyQuil and Forest.
Lincoln Laboratory Quantum Algorithm Test and Research
Solve a Sudoku puzzle with a quantum computer
Code-agnostic tensor-network (MPS-MPO) decoder for quantum error-correcting codes.
This is a simple script to plot energy profile diagrams using Python and matplotlib.
Python implementation of Krotov's method for quantum optimal control
MQT QECC - Tools for Quantum Error Correcting Codes
A playbook for systematically maximizing the performance of deep learning models.
Simple python implementation of Scott Aaronson et al's CHP simulator.
Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper "General tensor network decoding of 2D Pauli codes" (arXiv:2101.04125).
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.