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Found 13 papers, 2 papers with code
Tensor networks in machine learning
In this survey we recover the basics of tensor networks and explain the ongoing effort to develop the theory of tensor networks in machine learning.
Quantum-machine-learning channel discrimination
Quantum channel discrimination with a variational quantum classifier (ii) allows one to operate even with random and mixed input states and simple variational circuits.
Experimental quantum adversarial learning with programmable superconducting qubits
Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.
On barren plateaus and cost function locality in variational quantum algorithms
Variational quantum algorithms rely on gradient based optimization to iteratively minimize a cost function evaluated by measuring output(s) of a quantum processor.
Certified variational quantum algorithms for eigenstate preparation
Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form.
Variational Quantum Eigensolver for Frustrated Quantum Systems
Hybrid quantum-classical algorithms have been proposed as a potentially viable application of quantum computers.
Lectures on Quantum Tensor Networks
Situated as a language between computer science, quantum physics and mathematics, tensor network theory has steadily grown in popularity and can now be found in applications ranging across the entire field of quantum information processing.
Quantum Physics Strongly Correlated Electrons Mathematical Physics Category Theory Mathematical Physics
Machine Learning Phase Transitions with a Quantum Processor
To overcome this slowdown while still leveraging machine learning, we propose a variational quantum algorithm which merges quantum simulation and quantum machine learning to classify phases of matter.
Experimental neural network enhanced quantum tomography
We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to a SPAM-agnostic protocol with idealized measurements.
Quantum Machine Learning Tensor Network States
Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states.
A quantum algorithm to train neural networks using low-depth circuits
The question has remained open if near-term gate model quantum computers will offer a quantum advantage for practical applications in the pre-fault tolerance noise regime.
Quantum Physics Disordered Systems and Neural Networks
Tensor Networks in a Nutshell
Tensor network methods are taking a central role in modern quantum physics and beyond.
Quantum Physics Disordered Systems and Neural Networks General Relativity and Quantum Cosmology High Energy Physics - Theory Mathematical Physics Mathematical Physics
Quantum Machine Learning
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.
