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Found 16 papers, 5 papers with code
Toward Mixed Analog-Digital Quantum Signal Processing: Quantum AD/DA Conversion and the Fourier Transform
We demonstrate the utility of this paradigm by showcasing how it naturally enables analog-digital conversion of quantum signals -- specifically, the transfer of states between DV and CV quantum systems.
Noisy dynamical systems evolve error correcting codes and modularity
Noise is a ubiquitous feature of the physical world.
Pareto-optimal clustering with the primal deterministic information bottleneck
Our goal is to map out and study the Pareto frontier that quantifies this trade-off.
Fault-Tolerant Neural Networks from Biological Error Correction Codes
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
Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers.
Quantum Hypothesis Testing with Group Structure
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
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.
Learnability for the Information Bottleneck
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.
Meta-learning autoencoders for few-shot prediction
Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges.
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
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.
Realization of a scalable Shor algorithm
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
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
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
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
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
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
