13 papers
Noise Resilience of Quantum Support Vector Machine with Selected Feature Maps
Muhammad Ahsan Shakeel, Saad Muzammil, Danyal Tayyub +1
Gate-level noise degrades the classification accuracy of Quantum Support Vector Machines (QSVMs) on Noisy Intermediate-Scale Quantum (NISQ) hardware, and the degree of degradation…
The Input Problem: A Permanent Bottleneck for Quantum Machine Learning
Muhammad Faryad
Quantum algorithms are conventionally presented with their input state supplied for free. When the input is classical data, this convention conceals a cost that is frequently large…
DSpinGNN: A Physics-Informed Equivariant Graph Neural Network for Dynamic Magnetic Exchange Prediction in Strain-Deformed Monolayer CrI
Isam A. Balghari, M. Faryad, M. Sabieh Anwar
Resolving the instantaneous, position-dependent isotropic magnetic exchange coupling across a dynamically deforming crystal lattice requires a computational approach that…
A Systematic Study of Noise Effects in Hybrid Quantum-Classical Machine Learning
Bhavna Bose, Muhammad Faryad
Near-term quantum machine learning (QML) models operate in environments wherein noise is unavoidable, arising from both imperfect classical data acquisition and the limitations of…
Hybrid Quantum--Classical k-Means Clustering via Quantum Feature Maps
Syed M. Abdullah, Alisha Baba, Muhammad Siddique +1
Clustering is one of the most fundamental tasks in machine learning, and the k-means clustering algorithm is perhaps one of the most widely used clustering algorithms. However, it…
Hardware-Aware Quantum Support Vector Machines
Adil Mubashir Chaudhry, Ali Raza Haider, Hanzla Khan +1
Deploying quantum machine learning algorithms on near-term quantum hardware requires circuits that respect device-specific gate sets, connectivity constraints, and noise characteri…