Quantum Information and Cryptography
251 papers on this topic
Quantum Architecture Search: A Survey
Quantum computing has made significant progress in recent years, attracting immense interest not only in research laboratories but also in various industries. However, the application of quantum...
What Physical Layer Security Can Do for 6G Security
While existing security protocols were designed with a focus on the core network, the enhancement of the security of the B5G access network becomes of critical importance. Despite the...
Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
Quantum federated learning (QFL) has been recently introduced to enable a distributed privacy-preserving quantum machine learning (QML) model training across quantum processors (clients). Despite...
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning
The emergence of quantum reinforcement learning (QRL) is propelled by advancements in quantum computing (QC) and machine learning (ML), particularly through quantum neural networks (QNN) built on...
CNOT-Optimal Clifford Synthesis as SAT
Clifford circuit optimization is an important step in the quantum compilation pipeline. Major compilers employ heuristic approaches. While they are fast, their results are often suboptimal....
Artificial Intelligence and Machine Learning for Quantum Technologies
In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum...
A Hybrid Quantum Neural Network for Split Learning
Quantum Machine Learning (QML) is an emerging field of research with potential applications to distributed collaborative learning, such as Split Learning (SL). SL allows resource-constrained...
Learning to Program Quantum Measurements for Machine Learning
The rapid advancements in quantum computing (QC) and machine learning (ML) have sparked significant interest, driving extensive exploration of quantum machine learning (QML) algorithms to address...
Training-efficient density quantum machine learning
Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural networks, a model...
Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
A reinforcement learning (RL) framework is introduced for the efficient synthesis of quantum circuits that generate specified target quantum states from a fixed initial state, addressing a central...
Quantum Self-Attention Neural Networks for Text Classification
An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing (NLP). Although some...
Quantum automated learning with provable and explainable trainability
Machine learning is widely believed to be one of the most promising practical applications of quantum computing. Existing quantum machine learning schemes typically employ a quantum-classical...
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware
The Sachdev-Ye-Kitaev (SYK) model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally preparing thermal...
When Quantum Information Technologies Meet Blockchain in Web 3.0
With the drive to create a decentralized digital economy, Web 3.0 has become a cornerstone of digital transformation, developed on the basis of computing-force networking, distributed data...
Approximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of noises. However,...
Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion
Quantum Diffusion Models (QDMs) are an emerging paradigm in Generative AI that aims to use quantum properties to improve the performances of their classical counterparts. However, existing...
Towards a Quantum-classical Augmented Network
In the past decade, several small-scale quantum key distribution networks have been established. However, the deployment of large-scale quantum networks depends on the development of quantum...
A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI
This work focuses on developing efficient post-hoc explanations for quantum AI algorithms. In classical contexts, the cooperative game theory concept of the Shapley value adapts naturally to...