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See the “Quantum Algorithm Zoo” for a comprehensive list of quantum algorithms. https://quantumalgorithmzoo.org/ https://quantumalgorithmzoo.org/ Massive amou

See the “Quantum Algorithm Zoo” for a comprehensive list of quantum algorithms. https://quantumalgorithmzoo.org/ https://quantumalgorithmzoo.org/ Massive amounts of work is needed to make these comprehensible.

westurner 4y ago
> Tequila is an Extensible Quantum Information and Learning Architecture where the main goal is to simplify and accelerate implementation of new ideas for quantum algorithms. It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state of the art simulators as well as on real quantum devices. https://github.com/tequilahub/tequila#quantum-backends 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/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.
- westurner 4y ago
  A problem that could possibly be quantum-optimized and an existing set of solutions: Scheduling 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 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 https://news.ycombinator.com/item?id=22589911 > [Hilbert Curve Scheduling] https://en.wikipedia.org/wiki/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]

westurner 4y ago

> Tequila is an Extensible Quantum Information and Learning Architecture where the main goal is to simplify and accelerate implementation of new ideas for quantum algorithms. It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state of the art simulators as well as on real quantum devices. https://github.com/tequilahub/tequila#quantum-backends 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/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.

westurner 4y ago
A problem that could possibly be quantum-optimized and an existing set of solutions: Scheduling 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 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 https://news.ycombinator.com/item?id=22589911 > [Hilbert Curve Scheduling] https://en.wikipedia.org/wiki/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]