Challenges and opportunities in quantum machine learning
At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …
has the potential of accelerating data analysis, especially for quantum data, with …
Quantum computing for finance
Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …
computers and have a transformative impact on numerous industry sectors. We present a …
A review on quantum approximate optimization algorithm and its variants
Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …
variational quantum algorithm that aims to solve combinatorial optimization problems that …
Quantum circuits with many photons on a programmable nanophotonic chip
JM Arrazola, V Bergholm, K Brádler, TR Bromley… - Nature, 2021 - nature.com
Growing interest in quantum computing for practical applications has led to a surge in the
availability of programmable machines for executing quantum algorithms,. Present-day …
availability of programmable machines for executing quantum algorithms,. Present-day …
The power of quantum neural networks
It is unknown whether near-term quantum computers are advantageous for machine
learning tasks. In this work we address this question by trying to understand how powerful …
learning tasks. In this work we address this question by trying to understand how powerful …
Is quantum advantage the right goal for quantum machine learning?
M Schuld, N Killoran - Prx Quantum, 2022 - APS
Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …
computing. This is in fact a curious choice: the machine-learning algorithms of today are …
Effect of data encoding on the expressive power of variational quantum-machine-learning models
Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …
circuits as models that map data inputs to predictions. While a lot of work has been done to …
Absence of barren plateaus in quantum convolutional neural networks
Quantum neural networks (QNNs) have generated excitement around the possibility of
efficiently analyzing quantum data. But this excitement has been tempered by the existence …
efficiently analyzing quantum data. But this excitement has been tempered by the existence …
Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …
for a vast array of data processing tasks, which has entered most scientific disciplines in …
Pennylane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a Python 3 software framework for differentiable programming of quantum
computers. The library provides a unified architecture for near-term quantum computing …
computers. The library provides a unified architecture for near-term quantum computing …