7 papers
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…
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…
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…