Search Results for author: Isaac L. Chuang

Found 14 papers, 4 papers with code

Biological error correction codes generate fault-tolerant neural networks

no code implementations25 Feb 2022 Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Ila R. Fiete, Max Tegmark, Isaac L. Chuang

It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons?

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

Quantum Hypothesis Testing with Group Structure

no code implementations3 Feb 2021 Zane M. Rossi, Isaac L. Chuang

The problem of discriminating between many quantum channels with certainty is analyzed under the assumption of prior knowledge of algebraic relations among possible channels.

Quantum Physics

Confident Learning: Estimating Uncertainty in Dataset Labels

2 code implementations31 Oct 2019 Curtis G. Northcutt, Lu Jiang, Isaac L. Chuang

Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence.

Learning with noisy labels Sentiment Analysis

Learnability for the Information Bottleneck

no code implementations ICLR Workshop LLD 2019 Tailin Wu, Ian Fischer, Isaac L. Chuang, Max Tegmark

However, in practice, not only is $\beta$ chosen empirically without theoretical guidance, there is also a lack of theoretical understanding between $\beta$, learnability, the intrinsic nature of the dataset and model capacity.

Representation Learning

Meta-learning autoencoders for few-shot prediction

1 code implementation26 Jul 2018 Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark

Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges.

Meta-Learning

Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

3 code implementations4 May 2017 Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang

To highlight, RP with a CNN classifier can predict if an MNIST digit is a "one"or "not" with only 0. 25% error, and 0. 46 error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.

General Classification Noise Estimation +1

Realization of a scalable Shor algorithm

no code implementations31 Jul 2015 Thomas Monz, Daniel Nigg, Esteban A. Martinez, Matthias F. Brandl, Philipp Schindler, Richard Rines, Shannon X. Wang, Isaac L. Chuang, Rainer Blatt

It was only in 1994 that Peter Shor came up with an algorithm that is able to calculate the prime factors of a large number vastly more efficiently than known possible with a classical computer.

Quantum Physics

Experimental realization of Shor's quantum factoring algorithm using nuclear magnetic resonance

no code implementations30 Dec 2001 Lieven M. K. Vandersypen, Matthias Steffen, Gregory Breyta, Costantino S. Yannoni, Mark H. Sherwood, Isaac L. Chuang

The number of steps any classical computer requires in order to find the prime factors of an $l$-digit integer $N$ increases exponentially with $l$, at least using algorithms known at present.

Quantum Physics

Universal simulation of Markovian quantum dynamics

no code implementations15 Aug 2000 Dave Bacon, Andrew M. Childs, Isaac L. Chuang, Julia Kempe, Debbie W. Leung, Xinlan Zhou

Although the conditions for performing arbitrary unitary operations to simulate the dynamics of a closed quantum system are well understood, the same is not true of the more general class of quantum operations (also known as superoperators) corresponding to the dynamics of open quantum systems.

Quantum Physics

Experimental Realization of an Order-Finding Algorithm with an NMR Quantum Computer

no code implementations6 Jul 2000 Lieven M. K. Vandersypen, Matthias Steffen, Gregory Breyta, Costantino S. Yannoni, Richard Cleve, Isaac L. Chuang

We report the realization of a nuclear magnetic resonance (NMR) quantum computer which combines the quantum Fourier transform (QFT) with exponentiated permutations, demonstrating a quantum algorithm for order-finding.

Quantum Physics

Quantum Teleportation is a Universal Computational Primitive

no code implementations2 Aug 1999 Daniel Gottesman, Isaac L. Chuang

We present a method to create a variety of interesting gates by teleporting quantum bits through special entangled states.

Quantum Physics

Prescription for experimental determination of the dynamics of a quantum black box

no code implementations1 Oct 1996 Isaac L. Chuang, M. A. Nielsen

We give an explicit prescription for experimentally determining the evolution operators which completely describe the dynamics of a quantum mechanical black box -- an arbitrary open quantum system.

Quantum Physics

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