See the “Quantum Algorithm Zoo” for a comprehensive list of quantum algorithms.
https://quantumalgorithmzoo.org/
Massive amounts of work is needed to make these comprehensible.
https://quantumalgorithmzoo.org/
Massive amounts of work is needed to make these comprehensible.
https://github.com/tequilahub/tequila#quantum-backends
Quantum Backends currently supported by tequilahub/tequila: Qulacs, Qibo, Qiskit, Cirq (SymPy), PyQuil, QLM / myQLM
tequila-tutorials/Quantum_Calculator.ipynb https://github.com/tequilahub/tequila-tutorials/blob/main/Qu... :
> Welcome to the Tequila Calculator Tutorial. In this tutorial, you will learn how to create a quantum circuit that simulates addition using Tequila. We also compare the performance of various backends that Tequila uses.
Optimize the scheduling problem better than e.g. SLURM and then generate a sufficient classical solution that executes in P-space on classical computers.
SLURM https://en.wikipedia.org/wiki/Slurm_Workload_Manager :
> Slurm uses a best fit algorithm based on Hilbert curve scheduling or fat tree network topology in order to optimize locality of task assignments on parallel computers.[2]
Additional applications and use cases: "Employee Scheduling" > "Ask HN: What algorithms should I research to code a conference scheduling app" https://news.ycombinator.com/item?id=22589911
> [Hilbert Curve Scheduling] https://en.wikipedia.org/wiki/Hilbert_curve_scheduling :
> [...] the Hilbert curve scheduling method turns a multidimensional task allocation problem into a one-dimensional space filling problem using Hilbert curves, assigning related tasks to locations with higher levels of proximity.[1] Other space filling curves may also be used in various computing applications for similar purposes.[2]