عنوان

Quantum machine learning with Python :

ويژگيها

(electronic bk.)

ويژگيها

(electronic bk.)

شابک

148426522X

شابک

9781484265222

شابک اشتباه

1484265211

شابک اشتباه

9781484265215

شماره ISMN

10.1007/978-1-4842-6522-2

شماره

15054

شماره کنترل سيستم

(OCoLC)1244120897

شماره کنترل سيستم غير معتبر يا حذف شده

(OCoLC)1249103902

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Quantum machine learning with Python :

ساير اطلاعات عنواني

using Cirq from Google Research and IBM Qiskit /

نام نخستين پديدآور

Santanu Pattanayak.

محل نشرو پخش و غیره

[Berkeley, CA] :

نام ناشر، پخش کننده و غيره

Apress,

تاریخ نشرو بخش و غیره

[2021]

نام خاص و کميت اثر

1 online resource (xix, 361 pages) :

ساير جزييات

illustrations.

متن يادداشت

Includes index.

متن يادداشت

Available to OhioLINK libraries.

متن يادداشت

Chapter 1: Introduction to Quantum Mechanics and Quantum Computing -- Chapter 2: Mathematical Foundations and Postulates of Quantum Computing -- Chapter 3: Introduction to Quantum Algorithms -- Chapter 4: Quantum Fourier Transform Related Algorithms -- PART 2 Chapter 5: Introduction to Quantum Machine Learning -- Chapter 6: Quantum Deep Learning and Quantum Optimization Based Algorithms -- Chapter 7: Quantum Adiabatic Processes and Quantum based Optimization.

بدون عنوان

0

متن يادداشت

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others. You'll start by reviewing the fundamental concepts of Quantum Computing, such as Dirac Notations, Qubits, and Bell state, followed by postulates and mathematical foundations of Quantum Computing. Once the foundation base is set, you'll delve deep into Quantum based algorithms including Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd) among others. You'll then be introduced to Quantum machine learning and Quantum deep learning-based algorithms, along with advanced topics of Quantum adiabatic processes and Quantum based optimization. Throughout the book, there are Python implementations of different Quantum machine learning and Quantum computing algorithms using the Qiskit toolkit from IBM and Cirq from Google Research. You will: Understand Quantum computing and Quantum machine learning Explore varied domains and the scenarios where Quantum machine learning solutions can be applied Develop expertise in algorithm development in varied Quantum computing frameworks Review the major challenges of building large scale Quantum computers and applying its various techniques.

منبع سفارش / آدرس اشتراک

Springer

شماره استاندارد بين المللي کتاب و موسيقي

9781484265215.

شماره استاندارد بين المللي کتاب و موسيقي

9781484265239.

عنصر شناسه ای

Machine learning.

عنصر شناسه ای

Python (Computer program language)

عنصر شناسه ای

Quantum computing.

کد مقوله موضوعی

COM004000

کد مقوله موضوعی

UYQ

کد مقوله موضوعی

UYQ

کد سيستم

bisacsh

کد سيستم

bicssc

کد سيستم

thema

شماره

ويراست

شماره رده

نشانه اثر

عنصر شناسه اي

Pattanayak, Santanu,

عنصر شناسه اي

Ohio Library and Information Network.

تاريخ عمليات

20230627123316.9

قواعد فهرست نويسي ( بخش توصيفي )

rda

مشخصه منبع قراردادي

https://rave.ohiolink.edu/ebooks/ebc2/9781484265222

مشخصه منبع قراردادي

http://proxy.ohiolink.edu:9099/login?url=http://link.springer.com/10.1007/978-1-4842-6522-2

مشخصه منبع قراردادي

http://link.springer.com/10.1007/978-1-4842-6522-2

مشخصه منبع قراردادي

https://learning.oreilly.com/library/view/~/9781484265222/?ar

يادداشت عمومي

Connect to resource

يادداشت عمومي

Connect to resource (off-campus)

يادداشت عمومي

Connect to resource

يادداشت عمومي

Connect to resource

نوع ماده

BL

کد کاربرگه

270410

تكميل شده

Y