1 citations · 1 across the 9 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…