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Quantum Algorithm Implementations for Beginners

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TLDR
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.
Abstract
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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Journal ArticleDOI

Mitigation of readout noise in near-term quantum devices by classical post-processing based on detector tomography

TL;DR: A simple scheme to reduce readout errors in experiments on quantum systems with finite number of measurement outcomes, which relies on performing classical post-processing which is preceded by Quantum Detector Tomography, and results showing improvement for the implementation of certain probability distributions in the case of five qubits are presented.
Journal ArticleDOI

Open source software in quantum computing

TL;DR: A wide range of open source software for quantum computing is reviewed, covering all stages of the quantum toolchain from quantum hardware interfaces through quantum compilers to implementations of quantum algorithms, as well as all quantum computing paradigms, including quantum annealing, and discrete and continuous-variable gate-model quantum computing.
Journal ArticleDOI

Overview and Comparison of Gate Level Quantum Software Platforms

TL;DR: In this article, the authors provide a current picture of the rapidly evolving quantum computing landscape by comparing four software platforms (Forest, Qiskit, ProjectQ, and the Quantum Developer Kit (Q\#)) that enable researchers to use real and simulated quantum devices.
Posted Content

Error mitigation with Clifford quantum-circuit data

TL;DR: A novel, scalable error-mitigation method that applies to gate-based quantum computers and obtains an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.
References
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Book

Quantum Computation and Quantum Information

TL;DR: In this article, the quantum Fourier transform and its application in quantum information theory is discussed, and distance measures for quantum information are defined. And quantum error-correction and entropy and information are discussed.

Quantum Computation and Quantum Information

TL;DR: This chapter discusses quantum information theory, public-key cryptography and the RSA cryptosystem, and the proof of Lieb's theorem.
Journal ArticleDOI

A method for obtaining digital signatures and public-key cryptosystems

TL;DR: An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key.
Journal ArticleDOI

LIII. On lines and planes of closest fit to systems of points in space

TL;DR: This paper is concerned with the construction of planes of closest fit to systems of points in space and the relationships between these planes and the planes themselves.
Journal ArticleDOI

Least Squares Support Vector Machine Classifiers

TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
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