A hybrid machine-learning algorithm for designing quantum experiments
We introduce a hybrid machine-learning algorithm for designing quantum optics experiments that produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schr\"odinger cat states and cubic phase states, all to a fidelity of over $96\%$. Here we specifically focus on designing realistic experiments, and hence all of the algorithm's designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon-number distributions.
Code (1)
Tasks
AllBIG-bench Machine LearningHybrid Machine LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm
Recent studies in quantum machine learning advocated the use of hybrid models to assist with the limitations of the currently existing Noisy Intermediate Scale Quantum (NISQ) devices, but what was missing from most of th…
Neural Architecture SearchQuantum Machine LearningRepresentation Learning via Quantum Neural Tangent Kernels
Variational quantum circuits are used in quantum machine learning and variational quantum simulation tasks. Designing good variational circuits or predicting how well they perform for given learning or optimization tasks…
BIG-bench Machine LearningQuantum Machine LearningRepresentation LearningParameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach
Quantum machine learning (QML) is the use of quantum computing for the computation of machine learning algorithms. With the prevalence and importance of classical data, a hybrid quantum-classical approach to QML is calle…
Dimensionality ReductionQuantum Machine LearningQ-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints. Existing approaches …
Neural Architecture SearchQuantum Machine LearningImage ClassificationNeural Architecture Search for Quantum Autoencoders
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data.…
Neural Architecture SearchQuantum Machine LearningImage ClassificationSpeech Recognition