19 citations · 24 across the 11 of their papers we have counts for
12 papers
Quantum Interior Point Methods: A Review of Developments and An Optimally Scaling Framework
Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Pouya Sampourmahani +2
The growing demand for solving large-scale, data-intensive linear and conic optimization problems, particularly in applications such as artificial intelligence and machine learning…
Optimal Scaling Quantum Interior Point Method for Linear Optimization
Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Pouya Sampourmahani +2
The emergence of huge-scale, data-intensive linear optimization (LO) problems in applications such as machine learning has driven the need for more computationally efficient interi…
A Gateway to Quantum Computing for Industrial Engineering
Emily L. Tucker, Mohammadhossein Mohammadisiahroudi
Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engi…
Towards identifying possible fault-tolerant advantage of quantum linear system algorithms in terms of space, time and energy
Yue Tu, Mark Dubynskyi, Mohammadhossein Mohammadisiahroudi +5
Quantum computing, a prominent non-Von Neumann paradigm beyond Moore's law, can offer superpolynomial speedups for certain problems. Yet its advantages in efficiency for tasks like…
A quantum dual logarithmic barrier method for linear optimization
Zeguan Wu, Pouya Sampourmahani, Mohammadhossein Mohammadisiahroudi +1
Quantum computing has the potential to speed up some optimization methods. One can use quantum computers to solve linear systems via Quantum Linear System Algorithms (QLSAs). QLSAs…
Quantum Computing Inspired Iterative Refinement for Semidefinite Optimization
Mohammadhossein Mohammadisiahroudi, Brandon Augustino, Pouya Sampourmahani +1
Iterative Refinement (IR) is a classical computing technique for obtaining highly precise solutions to linear systems of equations, as well as linear optimization problems. In this…
Improvements to Quantum Interior Point Method for Linear Optimization
Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Brandon Augustino +2
Quantum linear system algorithms (QLSA) have the potential to speed up Interior Point Methods (IPM). However, a major challenge is that QLSAs are inexact and sensitive to the condi…
An Inexact Feasible Interior Point Method for Linear Optimization with High Adaptability to Quantum Computers
Mohammadhossein Mohammadisiahroudi, Ramin Fakhimi, Zeguan Wu +1
The use of quantum computing to accelerate complex optimization problems is a burgeoning research field. This paper applies Quantum Linear System Algorithms (QLSAs) to Newton syste…
Generating Linear, Semidefinite, and Second-order Cone Optimization Problems for Numerical Experiments
Mohammadhossein Mohammadisiahroudi, Ramin Fakhimi, Brandon Augustino +1
The numerical performance of algorithms can be studied using test sets or procedures that generate such problems. This paper proposes various methods for generating linear, semidef…
On Semidefinite Representations of Second-order Conic Optimization Problems
Pouya Sampourmahani, Mohammadhossein Mohammadisiahroudi, Tamás Terlaky
Second-order conic optimization (SOCO) can be considered as a special case of semidefinite optimization (SDO). In the literature it has been advised that a SOCO problem can be embe…
An Inexact Feasible Quantum Interior Point Method for Linearly Constrained Quadratic Optimization
Zeguan Wu, Mohammadhossein Mohammadisiahroudi, Brandon Augustino +2
Quantum linear system algorithms (QLSAs) have the potential to speed up algorithms that rely on solving linear systems. Interior Point Methods (IPMs) yield a fundamental family of…
Efficient Use of Quantum Linear System Algorithms in Interior Point Methods for Linear Optimization
Mohammadhossein Mohammadisiahroudi, Ramin Fakhimi, Tamás Terlaky
Quantum computing has attracted significant interest in the optimization community because it potentially can solve classes of optimization problems faster than conventional superc…