Ising machines as hardware solvers of combinatorial optimization problems
Ising machines are hardware solvers that aim to find the absolute or approximate ground
states of the Ising model. The Ising model is of fundamental computational interest because …
states of the Ising model. The Ising model is of fundamental computational interest because …
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 chemistry in the age of quantum computing
Practical challenges in simulating quantum systems on classical computers have been
widely recognized in the quantum physics and quantum chemistry communities over the …
widely recognized in the quantum physics and quantum chemistry communities over the …
Evaluating analytic gradients on quantum hardware
An important application for near-term quantum computing lies in optimization tasks, with
applications ranging from quantum chemistry and drug discovery to machine learning. In …
applications ranging from quantum chemistry and drug discovery to machine learning. In …
Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices
The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical
variational algorithm designed to tackle combinatorial optimization problems. Despite its …
variational algorithm designed to tackle combinatorial optimization problems. Despite its …
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 …
[HTML][HTML] Quantum natural gradient
A quantum generalization of Natural Gradient Descent is presented as part of a general-
purpose optimization framework for variational quantum circuits. The optimization dynamics …
purpose optimization framework for variational quantum circuits. The optimization dynamics …
Barren plateaus in quantum neural network training landscapes
Many experimental proposals for noisy intermediate scale quantum devices involve training
a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum …
a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum …
A survey on quantum computing technology
L Gyongyosi, S Imre - Computer Science Review, 2019 - Elsevier
The power of quantum computing technologies is based on the fundamentals of quantum
mechanics, such as quantum superposition, quantum entanglement, or the no-cloning …
mechanics, such as quantum superposition, quantum entanglement, or the no-cloning …
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 …