no code implementations • 29 Dec 2021 • Arkopal Dutt, Edwin Pednault, Chai Wah Wu, Sarah Sheldon, John Smolin, Lev Bishop, Isaac L. Chuang
Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers.
no code implementations • NeurIPS 2021 • Srinivasan Arunachalam, Yihui Quek, John Smolin
We then show information-theoretic implications between DP learning quantum states in the PAC model, learnability of quantum states in the one-way communication model, online learning of quantum states, quantum stability (which is our conceptual contribution), various combinatorial parameters and give further applications to gentle shadow tomography and noisy quantum state learning.
1 code implementation • 29 Dec 2020 • Tom Achache, Lior Horesh, John Smolin
We implement a Quantum Autoencoder (QAE) as a quantum circuit capable of correcting Greenberger-Horne-Zeilinger (GHZ) states subject to various noisy quantum channels : the bit-flip channel and the more general quantum depolarizing channel.