Variational quantum algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …
algebra problems are very challenging for classical computers, owing to the extremely high …
Quantum computing: progress and prospects
M Horowitz, E Grumbling - 2019 - books.google.com
Quantum mechanics, the subfield of physics that describes the behavior of very small
(quantum) particles, provides the basis for a new paradigm of computing. First proposed in …
(quantum) particles, provides the basis for a new paradigm of computing. First proposed 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 …
From the quantum approximate optimization algorithm to a quantum alternating operator ansatz
The next few years will be exciting as prototype universal quantum processors emerge,
enabling the implementation of a wider variety of algorithms. Of particular interest are …
enabling the implementation of a wider variety of algorithms. Of particular interest are …
Circuit-centric quantum classifiers
Variational quantum circuits are becoming tools of choice in quantum optimization and
machine learning. In this paper we investigate a class of variational circuits for the purposes …
machine learning. In this paper we investigate a class of variational circuits for the purposes …
Robust data encodings for quantum classifiers
Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …
quantum machine learning with near-term quantum computers, equally important …
Continuous-variable quantum neural networks
We introduce a general method for building neural networks on quantum computers. The
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …
Potential of quantum computing for drug discovery
Y Cao, J Romero… - IBM Journal of Research …, 2018 - ieeexplore.ieee.org
Quantum computing has rapidly advanced in recent years due to substantial development in
both hardware and algorithms. These advances are carrying quantum computers closer to …
both hardware and algorithms. These advances are carrying quantum computers closer to …
Solving nonlinear differential equations with differentiable quantum circuits
We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …
a quantum feature map encoding, we define functions as expectation values of parametrized …
Performance of the quantum approximate optimization algorithm on the maximum cut problem
GE Crooks - arxiv preprint arxiv:1811.08419, 2018 - arxiv.org
The Quantum Approximate Optimization Algorithm (QAOA) is a promising approach for
programming a near-term gate-based hybrid quantum computer to find good approximate …
programming a near-term gate-based hybrid quantum computer to find good approximate …