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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…
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…