19 citations · 24 across the 11 of their papers we have counts for
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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…
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…