Search Results for author: John Smolin

Found 3 papers, 1 papers with code

Active Learning of Quantum System Hamiltonians yields Query Advantage

no code implementations29 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.

Active Learning

Private learning implies quantum stability

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.

Learning Theory PAC learning

Denoising quantum states with Quantum Autoencoders -- Theory and Applications

1 code implementation29 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.

Denoising

Cannot find the paper you are looking for? You can Submit a new open access paper.