14 papers
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 optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construct…
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…
HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search
Jiayang Niu, Akib Karim, Yan Wang +5
Quantum Architecture Search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms, yet existing benchmarks organize instances by molecular…
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…