Qasmbench: A low-level quantum benchmark suite for nisq evaluation and simulation
The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-
level benchmark suite and insightful evaluation metrics for characterizing the properties of …
level benchmark suite and insightful evaluation metrics for characterizing the properties of …
Quantum architecture search via deep reinforcement learning
Recent advances in quantum computing have drawn considerable attention to building
realistic application for and using quantum computers. However, designing a suitable …
realistic application for and using quantum computers. However, designing a suitable …
Variational quantum reinforcement learning via evolutionary optimization
Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …
a promising direction for performing RL on a quantum computer. However, potential …
Multiclass seismic damage detection of buildings using quantum convolutional neural network
The traditional visual inspection technique for damage assessment of buildings immediately
after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies …
after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies …
Qasmbench: A low-level qasm benchmark suite for nisq evaluation and simulation
The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-
level benchmark suite and insightful evaluation metrics for characterizing the properties of …
level benchmark suite and insightful evaluation metrics for characterizing the properties of …
EQC: ensembled quantum computing for variational quantum algorithms
Variational quantum algorithm (VQA), which is comprised of a classical optimizer and a
parameterized quantum circuit, emerges as one of the most promising approaches for …
parameterized quantum circuit, emerges as one of the most promising approaches for …
Qugan: A quantum state fidelity based generative adversarial network
Tremendous progress has been witnessed in artificial intelligence where neural network
backed deep learning systems have been used, with applications in almost every domain …
backed deep learning systems have been used, with applications in almost every domain …
Elastic resource management for deep learning applications in a container cluster
The increasing demand for learning from massive datasets is restructuring our economy.
Effective learning, however, involves nontrivial computing resources. Most businesses utilize …
Effective learning, however, involves nontrivial computing resources. Most businesses utilize …
A hybrid system for learning classical data in quantum states
Deep neural network powered artificial intelligence has rapidly changed our daily life with
various applications. However, as one of the essential steps of deep neural networks …
various applications. However, as one of the essential steps of deep neural networks …
Reservoir computing via quantum recurrent neural networks
SYC Chen, D Fry, A Deshmukh, V Rastunkov… - arXiv preprint arXiv …, 2022 - arxiv.org
Recent developments in quantum computing and machine learning have propelled the
interdisciplinary study of quantum machine learning. Sequential modeling is an important …
interdisciplinary study of quantum machine learning. Sequential modeling is an important …