[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices
The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …
received significant attention from the research community in recent years. It uses the …
Noisy intermediate-scale quantum algorithms
A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …
integer factorization and unstructured database search requires millions of qubits with low …
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 …
Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm
for solving optimization problems on quantum computers. However, the potential of QAOA to …
for solving optimization problems on quantum computers. However, the potential of QAOA to …
Quantum computing for finance: State-of-the-art and future prospects
This article outlines our point of view regarding the applicability, state-of-the-art, and
potential of quantum computing for problems in finance. We provide an introduction to …
potential of quantum computing for problems in finance. We provide an introduction to …
Tensorflow quantum: A software framework for quantum machine learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of
hybrid quantum-classical models for classical or quantum data. This framework offers high …
hybrid quantum-classical models for classical or quantum data. This framework offers high …
Filtering variational quantum algorithms for combinatorial optimization
D Amaro, C Modica, M Rosenkranz… - Quantum Science …, 2022 - iopscience.iop.org
Current gate-based quantum computers have the potential to provide a computational
advantage if algorithms use quantum hardware efficiently. To make combinatorial …
advantage if algorithms use quantum hardware efficiently. To make combinatorial …
Optimization applications as quantum performance benchmarks
Combinatorial optimization is anticipated to be one of the primary use cases for quantum
computation in the coming years. The Quantum Approximate Optimization Algorithm and …
computation in the coming years. The Quantum Approximate Optimization Algorithm and …
Formulating and solving routing problems on quantum computers
The determination of vehicle routes fulfilling connectivity, time, and operational constraints is
a well-studied combinatorial optimization problem. The NP-hard complexity of vehicle …
a well-studied combinatorial optimization problem. The NP-hard complexity of vehicle …
Differentiable quantum architecture search
Quantum architecture search (QAS) is the process of automating architecture engineering of
quantum circuits. It has been desired to construct a powerful and general QAS platform …
quantum circuits. It has been desired to construct a powerful and general QAS platform …