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[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 …
Does provable absence of barren plateaus imply classical simulability? or, why we need to rethink variational quantum computing
A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …
Quantum variational algorithms are swamped with traps
One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …
trainable they are, though their training algorithms typically rely on optimizing complicated …
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 …
Is quantum advantage the right goal for quantum machine learning?
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 …
Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution
It has been reported that quantum generative adversarial networks have a potential
exponential advantage over classical generative adversarial networks. However, quantum …
exponential advantage over classical generative adversarial networks. However, quantum …
Quantum convolutional neural network for classical data classification
With the rapid advance of quantum machine learning, several proposals for the quantum-
analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark …
analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark …
Training variational quantum algorithms is NP-hard
Variational quantum algorithms are proposed to solve relevant computational problems on
near term quantum devices. Popular versions are variational quantum eigensolvers and …
near term quantum devices. Popular versions are variational quantum eigensolvers and …
Synergistic pretraining of parametrized quantum circuits via tensor networks
Parametrized quantum circuits (PQCs) represent a promising framework for using present-
day quantum hardware to solve diverse problems in materials science, quantum chemistry …
day quantum hardware to solve diverse problems in materials science, quantum chemistry …