Quantum forking for fast weighted power summation

  title={Quantum forking for fast weighted power summation},
  author={Daniel K Park and I. Sinayskiy and M. Fingerhuth and Francesco Petruccione and J. Rhee},
  journal={arXiv: Quantum Physics},
The computational cost of preparing an input quantum state can be significant depending on the structure of data to be encoded. Many quantum algorithms require repeated sampling to find the answer, mandating reconstruction of the same input state for every execution of an algorithm. Thus, the advantage of quantum information processing can diminish due to redundant state initialization. Here we present a framework based on quantum forking that bypasses such a fundamental issue and expedites a… Expand
Circuit-Based Quantum Random Access Memory for Classical Data
This work presents a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way and presents a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Expand
A Variational Algorithm for Quantum Neural Networks
This work introduces a novel variational algorithm for quantum Single Layer Perceptron and designs a quantum circuit to perform linear combinations in superposition, and discusses adaptations to classification and regression tasks. Expand


Quantum Operating Systems
This paper considers systems-level issues that quantum computers would raise, and demonstrates that these machines would offer surprising speed-ups for a number of everyday systems tasks, such as unit testing and CPU scheduling. Expand
Quantum Networks for Generating Arbitrary Quantum States
Quantum protocols often require the generation of specific quantum states. We describe a quantum algorithm for generating any prescribed quantum state. For an important subclass of states, includingExpand
  • Rev. A 97, 052329
  • 2018
Proceedings of the 16th Workshop on Hot Topics in Operating Systems
  • Computer Science
  • HotOS
  • 2017
  • Rev. Lett. 117, 260501
  • 2016
New Journal of Physics 17
  • 123010
  • 2015
New Journal of Physics 17
  • 113020
  • 2015
Journal of Physics A: Mathematical and Theoretical 47
  • 483001
  • 2014
  • Xiang, Z.-Y. Zhu, L.-z. Jiang, and L.-n. Wu, Phys. Rev. A 86, 010306
  • 2012
  • Rev. A 83, 032302
  • 2011