Search Results for author:
Found 23 papers, 11 papers with code
Neural Networks for Programming Quantum Annealers
We explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer.
Joint Embedding Self-Supervised Learning in the Kernel Regime
The fundamental goal of self-supervised learning (SSL) is to produce useful representations of data without access to any labels for classifying the data.
projUNN: efficient method for training deep networks with unitary matrices
In learning with recurrent or very deep feed-forward networks, employing unitary matrices in each layer can be very effective at maintaining long-range stability.
Quantum algorithms for group convolution, cross-correlation, and equivariant transformations
Group convolutions and cross-correlations, which are equivariant to the actions of group elements, are commonly used in mathematics to analyze or take advantage of symmetries inherent in a given problem setting.
A quantum algorithm for training wide and deep classical neural networks
Given the success of deep learning in classical machine learning, quantum algorithms for traditional neural network architectures may provide one of the most promising settings for quantum machine learning.
Learning quantum data with the quantum Earth Mover's distance
Quantifying how far the output of a learning algorithm is from its target is an essential task in machine learning.
Adversarial Robustness Guarantees for Random Deep Neural Networks
We explore the properties of adversarial examples for deep neural networks with random weights and biases, and prove that for any $p\ge1$, the $\ell^p$ distance of any given input from the classification boundary scales as one over the square root of the dimension of the input times the $\ell^p$ norm of the input.
Quantum Medical Imaging Algorithms
A central task in medical imaging is the reconstruction of an image or function from data collected by medical devices (e. g., CT, MRI, and PET scanners).
Image Reconstruction
Quantum Physics
Image and Video Processing
Medical Physics
Learning Unitaries by Gradient Descent
We study the hardness of learning unitary transformations in $U(d)$ via gradient descent on time parameters of alternating operator sequences.
Quantum embeddings for machine learning
Quantum classifiers are trainable quantum circuits used as machine learning models.
Quantum Physics
Quantum-inspired algorithms in practice
On the other hand, their performance degrades noticeably as the rank and condition number of the input matrix are increased.
Quantum Physics Data Structures and Algorithms
Random deep neural networks are biased towards simple functions
We prove that the binary classifiers of bit strings generated by random wide deep neural networks with ReLU activation function are biased towards simple functions.
Continuous-variable quantum neural networks
The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field.
Quantum generative adversarial learning
The learning process for generator and discriminator can be thought of as an adversarial game, and under reasonable assumptions, the game converges to the point where the generator generates the same statistics as the true data and the discriminator is unable to discriminate between the true and the generated data.
Quantum Physics
Quantum Machine Learning
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.
A Turing test for free will
Before Alan Turing made his crucial contributions to the theory of computation, he studied the question of whether quantum mechanics could throw light on the nature of free will.
Quantum algorithms for supervised and unsupervised machine learning
Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces.
Quantum Physics
Quantum principal component analysis
The usual way to reveal properties of an unknown quantum state, given many copies of a system in that state, is to perform measurements of different observables and to analyze the measurement results statistically.
Quantum Physics
Quantum support vector machine for big data classification
Supervised machine learning is the classification of new data based on already classified training examples.
Quantum algorithm for solving linear systems of equations
Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b, find a vector x such that Ax=b.
Quantum Physics
Architectures for a quantum random access memory
A quantum RAM, or qRAM, allows one to access superpositions of memory sites, which may contain either quantum or classical information.
Quantum Physics
Quantum random access memory
A random access memory (RAM) uses n bits to randomly address N=2^n distinct memory cells.
Quantum Physics
Computational capacity of the universe
The laws of physics determine the amount of information that a physical system can register (number of bits) and the number of elementary logic operations that a system can perform (number of ops).
Quantum Physics
