activity
20192021
most citedQuantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty

13 citations · 15 across the 3 of their papers we have counts for

collaborators

7 papers

quant-ph2021

EQUAL: Improving the Fidelity of Quantum Annealers by Injecting Controlled Perturbations

Ramin Ayanzadeh, Poulami Das, Swamit S. Tannu +1

Quantum computing is an information processing paradigm that uses quantum-mechanical properties to speedup computationally hard problems. Although promising, existing gate-based qu…

quant-ph2020

An Ensemble Approach for Compressive Sensing with Quantum

Ramin Ayanzadeh, Milton Halem, Tim Finin

We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing. Since executing quantum machine instructions on a quant…

quant-ph2020

Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach

Ramin Ayanzadeh, Milton Halem, Tim Finin

We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning…

quant-ph2019

Quantum-Assisted Greedy Algorithms

Ramin Ayanzadeh, Milton Halem, John Dorband +1

We show how to leverage quantum annealers to better select candidates in greedy algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics for making…

math.OC2019

A Survey on Compressive Sensing: Classical Results and Recent Advancements

Ahmad Mousavi, Mehdi Rezaee, Ramin Ayanzadeh

Recovering sparse signals from linear measurements has demonstrated outstanding utility in a vast variety of real-world applications. Compressive sensing is the topic that studies…

cs.IT20192 cited

SAT-based Compressive Sensing

Ramin Ayanzadeh, Milton Halem, Tim Finin

We propose to reduce the original well-posed problem of compressive sensing to weighted-MAX-SAT. Compressive sensing is a novel randomized data acquisition approach that linearly s…