1 citations · 2 across the 11 of their papers we have counts for
12 papers
An analysis of iterative refinement for quantum linear system solvers
Adrian Harkness, Mohammadhossein Mohammadisiahroudi, Brandon Augustino +2
We present and analyze an iterative refinement (IR) framework for improving the precision dependence of algorithms that combine a quantum linear system algorithm (QLSA) with quantu…
FTCircuitBench: A Benchmark Suite for Fault-Tolerant Quantum Compilation and Architecture
Adrian Harkness, Shuwen Kan, Chenxu Liu +10
Realizing large-scale quantum advantage is expected to require quantum error correction (QEC), making the compilation and optimization of logical operations a critical area of rese…
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…
Characterizing QUBO Reformulations of the Max-k-Cut Problem for Quantum Computing
Adrian Harkness, Hamidreza Validi, Ramin Fakhimi +4
Quantum computing offers significant potential for solving NP-hard combinatorial (optimization) problems that are beyond the reach of classical computers. One way to tap into this…
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…
A preconditioned inexact infeasible quantum interior point method for linear optimization
Zeguan Wu, Xiu Yang, Tamás Terlaky
Quantum Interior Point Methods (QIPMs) have been attracting significant interests recently due to their potential of solving optimization problems substantially faster than state-o…
An Efficient Quantum Algorithm for Linear System Problem in Tensor Format
Zeguan Wu, Sidhant Misra, Tamás Terlaky +2
Solving linear systems is at the foundation of many algorithms. Recently, quantum linear system algorithms (QLSAs) have attracted great attention since they converge to a solution…
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…
A quantum central path algorithm for linear optimization
Brandon Augustino, Jiaqi Leng, Giacomo Nannicini +3
We propose a novel quantum algorithm for solving linear optimization problems by quantum-mechanical simulation of the central path. While interior point methods follow the central…
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…
Characterization of QUBO reformulations for the maximum -colorable subgraph problem
Rodolfo Quintero, David Bernal, Tamás Terlaky +1
Quantum devices can be used to solve constrained combinatorial optimization (COPT) problems thanks to the use of penalization methods to embed the COPT problem's constraints in its…