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Quantum Algorithm Implementations for Beginners

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arxiv 1804.03719 v3 pith:BSB3436A submitted 2018-04-10 cs.ET quant-ph

Quantum Algorithm Implementations for Beginners

classification cs.ET quant-ph
keywords quantumalgorithmscomputerhardwareavailableclassicalcomputerscomputing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers. While currently available quantum computers have less than 100 qubits, quantum computing hardware is widely expected to grow in terms of qubit count, quality, and connectivity. This review aims to explain the principles of quantum programming, which are quite different from classical programming, with straightforward algebra that makes understanding of the underlying fascinating quantum mechanical principles optional. We give an introduction to quantum computing algorithms and their implementation on real quantum hardware. We survey 20 different quantum algorithms, attempting to describe each in a succinct and self-contained fashion. We show how these algorithms can be implemented on IBM's quantum computer, and in each case, we discuss the results of the implementation with respect to differences between the simulator and the actual hardware runs. This article introduces computer scientists, physicists, and engineers to quantum algorithms and provides a blueprint for their implementations.

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Cited by 2 Pith papers

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  1. Advanced Scheduling Strategies for Distributed Quantum Computing Jobs

    quant-ph 2026-02 conditional novelty 5.0

    Link-aware scheduling rules for distributed quantum computing jobs cut simulated batch makespan by about half versus FIFO/LIST, but the best-performing rule is a simple heuristic, not the trained RL agent.

  2. Machine learning methods in quantum computing theory

    quant-ph 2019-06 unverdicted novelty 5.0

    Authors present a multiclass tree tensor network algorithm demonstrated on IBM quantum processor and a neural network approach for noise-robust quantum state tomography.