Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ)
computers. Previous work for mitigating noise has primarily focused on gate-level or pulse …

Quantum computing is among the most promising emerging techniques to solve problems
that are computationally intractable on classical hardware. A large body of existing works …

Among different quantum algorithms, PQC for QML show promises on near-term devices. To
facilitate the QML and PQC research, a recent python library called TorchQuantum has been …

Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to
its potential to achieve quantum advantages on near-term Noisy Intermediate Scale …

Quantum hardware suffers from high error rates and noise, which makes directly running
applications on them ineffective. Quantum Error Correction (QEC) is a critical technique …

Neural Networks (NNs) are being extensively used in critical applications such as
aerospace, healthcare, autonomous driving, and military, to name a few. Limited precision of …

Model compression, such as pruning and quantization, has been widely applied to optimize
neural networks on resource-limited classical devices. Recently, there are growing interest …

Variational Quantum Algorithms (VQA) are promising to demonstrate quantum advantages
on near-term devices. Designing ansatz, a variational circuit with parameterized gates, is of …

Current quantum programs are mostly synthesized and compiled on the gate-level, where
quantum circuits are composed of quantum gates. The gate-level workflow, however …

As restricted quantum computers become available, research focuses on finding meaningful
applications. For example, in quantum machine learning, a special type of quantum circuit …