5 citations · 13 across the 27 of their papers we have counts for
31 papers
Fourier-Geometric Circuit Design for Gate and Entanglement Placement in Quantum Neural Networks
Seungcheol Oh, Chaemoon Im, Daeyeun Kim +4
The output of a parameterized quantum circuit (PQC) can be expressed as a finite Fourier series whose accessible frequencies are fixed by the data-encoding gates. While the encoder…
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 Pauli…
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…
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…
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…
Quantum Data Sketches
Qin Zhang, Mohsen Heidari
Recent advancements in quantum technologies, particularly in quantum sensing and simulation, have facilitated the generation and analysis of inherently quantum data. This progress…
New Bounds on Quantum Sample Complexity of Measurement Classes
Mohsen Heidari, Wojciech Szpankowski
This paper studies quantum supervised learning for classical inference from quantum states. In this model, a learner has access to a set of labeled quantum samples as the training…
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…
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…
Quantum Shadow Gradient Descent for Variational Quantum Algorithms
Mohsen Heidari, Mobasshir A Naved, Zahra Honjani +3
Gradient-based optimizers have been proposed for training variational quantum circuits in settings such as quantum neural networks (QNNs). The task of gradient estimation, however,…
On The Reliability Function of Discrete Memoryless Multiple-Access Channel with Feedback
Mohsen Heidari, Achilleas Anastasopoulos, S. Sandeep Pradhan
The reliability function of a channel is the maximum achievable exponential rate of decay of the error probability as a function of the transmission rate. In this work, we derive b…
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression
Mohsen Heidari, Wojciech Szpankowski
Many conventional learning algorithms rely on loss functions other than the natural 0-1 loss for computational efficiency and theoretical tractability. Among them are approaches ba…
On Non-Interactive Source Simulation via Fourier Transform
Farhad Shirani, Mohsen Heidari
The non-interactive source simulation (NISS) scenario is considered. In this scenario, a pair of distributed agents, Alice and Bob, observe a distributed binary memoryless source $…
Expected Worst Case Regret via Stochastic Sequential Covering
Changlong Wu, Mohsen Heidari, Ananth Grama +1
We study the problem of sequential prediction and online minimax regret with stochastically generated features under a general loss function. We introduce a notion of expected wors…
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
Changlong Wu, Mohsen Heidari, Ananth Grama +1
We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We f…
Toward Physically Realizable Quantum Neural Networks
Mohsen Heidari, Ananth Grama, Wojciech Szpankowski
There has been significant recent interest in quantum neural networks (QNNs), along with their applications in diverse domains. Current solutions for QNNs pose significant challeng…
Upper Bounds on the Feedback Error Exponent of Channels With States and Memory
Mohsen Heidari, Achilleas Anastasopoulos, S. Sandeep Pradhan
As a class of state-dependent channels, Markov channels have been long studied in information theory for characterizing the feedback capacity and error exponent. This paper studies…
A Theoretical Framework for Learning from Quantum Data
Mohsen Heidari, Arun Padakandla, Wojciech Szpankowski
Over decades traditional information theory of source and channel coding advances toward learning and effective extraction of information from data. We propose to go one step furth…
On Agnostic PAC Learning using -polynomial Regression and Fourier-based Algorithms
Mohsen Heidari, Wojciech Szpankowski
We develop a framework using Hilbert spaces as a proxy to analyze PAC learning problems with structural properties. We consider a joint Hilbert space incorporating the relation bet…
Learning k-qubit Quantum Operators via Pauli Decomposition
Mohsen Heidari, Wojciech Szpankowski
Motivated by the limited qubit capacity of current quantum systems, we study the quantum sample complexity of -qubit quantum operators, i.e., operations applicable on only o…
Structured Mappings and Conferencing Common Information for Multiple-access Channels
Mohsen Heidari, S. Sandeep Pradhan
In this work, we study two problems: three-user Multiple-Access Channel (MAC) with correlated sources, and MAC with Feedback (MAC-FB) with independent messages. For the first probl…
Boolean Functions with Biased Inputs: Approximation and Noise Sensitivity
Mohsen Heidari, S. Sandeep Pradhan, Ramji Venkataramanan
This paper considers the problem of approximating a Boolean function using another Boolean function from a specified class. Two classes of approximating functions are considere…
Faithful Simulation of Distributed Quantum Measurements with Applications in Distributed Rate-Distortion Theory
Touheed Anwar Atif, Mohsen Heidari, S. Sandeep Pradhan
We consider the task of faithfully simulating a distributed quantum measurement, wherein we provide a protocol for the three parties, Alice, Bob and Eve, to simulate a repeated act…
Bounds on the Effective-length of Optimal Codes for Interference Channel with Feedback
Mohsen Heidari, Farhad Shirani, S. Sandeep Pradhan
In this paper, we investigate the necessity of finite blocklength codes in distributed transmission of independent message sets over channels with feedback. Previously, it was show…