Quantum Computing for Software Engineering: Prospects
arXiv:2203.03575 · doi:10.1145/3549036.3562060
Abstract
Quantum computers (QCs) are maturing. When QCs are powerful enough, they may be able to handle problems in chemistry, physics, and finance that are not classically solvable. However, the applicability of quantum algorithms to speed up Software Engineering (SE) tasks has not been explored. We examine eight groups of quantum algorithms that may accelerate SE tasks across the different phases of SE and sketch potential opportunities and challenges.
References in corpus (15)
- Quantum Computing in the NISQ era and beyond
- Tight bounds on quantum searching
- Quantum computing for finance: overview and prospects
- Quantum algorithm for systems of linear equations with exponentially improved dependence on precision
- Data re-uploading for a universal quantum classifier
- A rigorous and robust quantum speed-up in supervised machine learning
- Quantum reinforcement learning
- High-order quantum algorithm for solving linear differential equations
- Efficient quantum algorithm for dissipative nonlinear differential equations
- Nested quantum search and NP-complete problems
- A Quantum Algorithm for Solving Linear Differential Equations: Theory and Experiment
- Quantum Algorithms for Mixed Binary Optimization applied to Transaction Settlement
- On Testing Quantum Programs
- Multi-block ADMM Heuristics for Mixed-Binary Optimization on Classical and Quantum Computers
- EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates