# Quantum forking for fast weighted power summation

@article{Park2019QuantumFF, 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}, year={2019} }

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

#### 2 Citations

Circuit-Based Quantum Random Access Memory for Classical Data

- Computer Science, Physics
- Scientific Reports
- 2019

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

- Computer Science
- ICCS
- 2020

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

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