3 citations · 4 across the 17 of their papers we have counts for
18 papers
When do quantum views help? Complementary classical-quantum representations in multi-view fusion
Azadeh Alavi, Fatemeh Kouchmeshki, Hossein Akhoundi +3
Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity. Research includes comparisons with…
DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
Jiayang Niu, Yan Wang, Jie Li +4
Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly invokes a variational quantum eigensolver (VQE) after each gate addition even though circuit transition…
Gram-Certified Resource Continuation for Structured Quantum Representation Audits
Azadeh Alavi, Fatemeh Kouchmeshki, Hossein Akhoundi +1
Dense representation of an -qubit pure state requires complex amplitudes, precluding dense classical materialization at large . We develop Gram-certified resource conti…
Invariance Audits for Quantum Kernels and Variational Rewinding: A Real-to-Hermitian Taxonomy of Projector, Flag, Anchor, and Density Geometry
Azadeh Alavi, Fatemeh Kouchmeshki, Hossein Akhoundi
Machine-learning models often replace vectors by normalized directions, projectors, covariances, subspaces, ordered flags, quantum states, or density operators before any classifie…
Energy Accuracy Is Not Enough: A Structure-Aware Benchmark and Evaluation Protocol for Quantum Architecture Search
Jiayang Niu, Akib Karim, Yan Wang +5
Quantum architecture search for molecular ground-state estimation is commonly evaluated through energy accuracy, which does not describe circuit cost or the physical properties of…
A Givens-exchange ansatz for molecular variational eigensolvers
Azadeh Alavi, Fatemeh Kouchmeshki, Muhammad Usman +4
Molecular ground-state energies help determine conformer rankings, reaction energetics, and electronic effects in computational drug discovery, but accurate calculations become dif…
QBioFusion-QSAR: Morgan-Anchored Quantum Multiple Kernel Learning for Small-Data Ligand Classification
Azadeh Alavi, Fatemeh Kouchmeshki, Muhammad Usman +1
Small quantitative structure-activity relationship (QSAR) studies are difficult when close molecular analogues have different activity labels. This paper asks whether a quantum ker…
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
Joy Dhar, Song Xia, Manish Kumar Pandey +5
We introduce Hybrid Convolutions with Attention Stochasticity (HyCAS), an adversarial defense that narrows the long-standing gap between provable robustness under L2 certificates a…
Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease
Azadeh Alavi, Hamidreza Khalili, Stanley H. Chan +3
Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people worldwide, and skeletal-muscle dysfunction is clinically important. Quantum machine learning is…
Practical Quantum-Classical Feature Fusion for complex data Classification
Azadeh Alavi, Fatemeh Kouchmeshki, Abdolrahman Alavi
Hybrid quantum and classical learning aims to couple quantum feature maps with the robustness of classical neural networks, yet most architectures treat the quantum circuit as an i…
Hybrid Action Reinforcement Learning for Quantum Architecture Search
Jiayang Niu, Yan Wang, Jie Li +4
Reinforcement learning-based Quantum Architecture Search (QAS) offers a promising avenue for automating the design of variational quantum circuits, but existing methods typically d…
A Unified Contrastive-Generative Framework for Time Series Classification
Ziyu Liu, Azadeh Alavi, Minyi Li +1
Self-supervised learning (SSL) for multivariate time series mainly includes two paradigms: contrastive methods that excel at instance discrimination and generative approaches that…
Quantum Semi-Random Forests for Qubit-Efficient Recommender Systems
Azadeh Alavi, Fatemeh Kouchmeshki, Abdolrahman Alavi +2
Modern recommenders describe each item with hundreds of sparse semantic tags, yet most quantum pipelines still map one qubit per tag, demanding well beyond one hundred qubits, far…
A Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods
Azadeh Alavia, Hossein Akhoundib, Fatemeh Kouchmeshkib +4
Geometric Machine Learning (GML) has shown that respecting non-Euclidean geometry in data spaces can significantly improve performance over naive Euclidean assumptions. In parallel…
Analyzing Advanced AI Systems Against Definitions of Life and Consciousness
Azadeh Alavi, Hossein Akhoundi, Fatemeh Kouchmeshki
Could artificial intelligence ever become truly conscious in a functional sense; this paper explores that open-ended question through the lens of Life, a concept unifying classical…
Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification
Ziyu Liu, Azadeh Alavi, Minyi Li +1
Self-supervised contrastive learning has become a key technique in deep learning, particularly in time series analysis, due to its ability to learn meaningful representations witho…
Leveraging SPD Matrices on Riemannian Manifolds in Quantum Classical Hybrid Models for Structural Health Monitoring
Azadeh Alavi, Sanduni Jayasinghe
Realtime finite element modeling of bridges assists modern structural health monitoring systems by providing comprehensive insights into structural integrity. This capability is es…
Self-Supervised Learning for Time Series: Contrastive or Generative?
Ziyu Liu, Azadeh Alavi, Minyi Li +1
Self-supervised learning (SSL) has recently emerged as a powerful approach to learning representations from large-scale unlabeled data, showing promising results in time series ana…