Quantum circuit synthesis and compilation optimization: Overview and prospects
Quantum computing is regarded as a promising paradigm that may overcome the current
computational power bottlenecks in the post-Moore era. The increasing maturity of quantum …
computational power bottlenecks in the post-Moore era. The increasing maturity of quantum …
SoK: quantum computing methods for machine learning optimization
H Baniata - Quantum Machine Intelligence, 2024 - Springer
Hyperparameter optimization (HPO) and neural architecture search (NAS) of machine
learning (ML) models are in the core implementation steps of AI-enabled systems. With multi …
learning (ML) models are in the core implementation steps of AI-enabled systems. With multi …
Differentiable quantum architecture search in asynchronous quantum reinforcement learning
SYC Chen - arXiv preprint arXiv:2407.18202, 2024 - arxiv.org
The emergence of quantum reinforcement learning (QRL) is propelled by advancements in
quantum computing (QC) and machine learning (ML), particularly through quantum neural …
quantum computing (QC) and machine learning (ML), particularly through quantum neural …
Training-free quantum architecture search
Variational quantum algorithm (VQA) derives advantages from its error resilience and high
flexibility in quantum resource requirements, rendering it broadly applicable in the noisy …
flexibility in quantum resource requirements, rendering it broadly applicable in the noisy …
Quantum Machine Learning Architecture Search via Deep Reinforcement Learning
The rapid advancement of quantum computing (QC) and machine learning (ML) has given
rise to the burgeoning field of quantum machine learning (QML), aiming to capitalize on the …
rise to the burgeoning field of quantum machine learning (QML), aiming to capitalize on the …
Continuous evolution for efficient quantum architecture search
QG Ma, CL Hao, XK Yang, LL Qian, H Zhang… - EPJ Quantum …, 2024 - epjqt.epj.org
Variational quantum algorithms (VQAs) have been successfully applied to quantum
approximate optimization algorithms, variational quantum compiling, and quantum machine …
approximate optimization algorithms, variational quantum compiling, and quantum machine …
Benchmarking of quantum fidelity kernels for Gaussian process regression
Quantum computing algorithms have been shown to produce performant quantum kernels
for machine-learning classification problems. Here, we examine the performance of …
for machine-learning classification problems. Here, we examine the performance of …
MG-Net: Learn to Customize QAOA with Circuit Depth Awareness
Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense
potential in tackling combinatorial optimization challenges. However, their practical …
potential in tackling combinatorial optimization challenges. However, their practical …
Neural auto-designer for enhanced quantum kernels
Quantum kernels hold great promise for offering computational advantages over classical
learners, with the effectiveness of these kernels closely tied to the design of the quantum …
learners, with the effectiveness of these kernels closely tied to the design of the quantum …
Architectural Patterns for Designing Quantum Artificial Intelligence Systems
Utilising quantum computing technology to enhance artificial intelligence systems is
expected to improve training and inference times, increase robustness against noise and …
expected to improve training and inference times, increase robustness against noise and …