7 papers
Learning DNF through Generalized Fourier Representations
Mohsen Heidari, Roni Khardon
The Boolean Fourier representation has been widely used in learning theory, particularly for learning Disjunctive Normal Form (DNF) under uniform and product distributions. Extendi…
Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries
Mohsen Heidari, Masih Mozakka, Wojciech Szpankowski
Gradient estimation is a central challenge in training parameterized quantum circuits (PQCs) for hybrid quantum-classical optimization and learning problems. This difficulty arises…
Query Learning Nearly Pauli Sparse Unitaries in Diamond Distance
Zahra Honjani, Mohsen Heidari
We study the problem of learning nearly -sparse unitaries, meaning that the Pauli spectrum is concentrated on at most components with at most residual mass in Paul…
Accelerating Feedback-based Algorithms for Quantum Optimization Using Gradient Descent
Masih Mozakka, Mohsen Heidari
Feedback-based methods have gained significant attention as an alternative training paradigm for the Quantum Approximate Optimization Algorithm (QAOA) in solving combinatorial opti…
Quantum Natural Stochastic Pairwise Coordinate Descent
Mohammad Aamir Sohail, Mohsen Heidari, S. Sandeep Pradhan
Variational quantum algorithms, optimized using gradient-based methods, often exhibit sub-optimal convergence performance due to their dependence on Euclidean geometry. Quantum nat…
Improved Classical Shadow Tomography Using Quantum Computation
Zahra Honjani, Mohsen Heidari
Classical shadow tomography (CST) involves obtaining enough classical descriptions of an unknown state via quantum measurements to predict the outcome of a set of quantum observabl…