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 …
[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 …
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Parametrized quantum circuits serve as ansatze for solving variational problems and
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …
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 …
NISQ computing: where are we and where do we go?
In this short review article, we aim to provide physicists not working within the quantum
computing community a hopefully easy-to-read introduction to the state of the art in the field …
computing community a hopefully easy-to-read introduction to the state of the art in the field …
Higher order derivatives of quantum neural networks with barren plateaus
Quantum neural networks (QNNs) offer a powerful paradigm for programming near-term
quantum computers and have the potential to speed up applications ranging from data …
quantum computers and have the potential to speed up applications ranging from data …
Capacity and quantum geometry of parametrized quantum circuits
To harness the potential of noisy intermediate-scale quantum devices, it is paramount to find
the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are …
the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are …
Quantum Krylov subspace algorithms for ground-and excited-state energy estimation
Quantum Krylov subspace diagonalization (QKSD) algorithms provide a low-cost alternative
to the conventional quantum phase estimation algorithm for estimating the ground-and …
to the conventional quantum phase estimation algorithm for estimating the ground-and …
Trainability enhancement of parameterized quantum circuits via reduced-domain parameter initialization
Parameterized quantum circuits (PQCs) have been widely used as a machine learning
model to explore the potential of achieving quantum advantages for various tasks. However …
model to explore the potential of achieving quantum advantages for various tasks. However …