103,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
QUANTUM COMPUTING A helpful introduction to all aspects of quantum computing Quantum computing is a field combining quantum mechanics--the physical science of nature at the scale of atoms and subatomic particles--and information science. Where ordinary computing uses bits, logical values whose position can either be 0 or 1, quantum computing is built around qubits, a fundamental unit of quantum information which can exist in a superposition of both states. As quantum computers are able to complete certain kinds of functions more accurately and efficiently than computers built on classical binary logic, quantum computing is an emerging frontier which promises to revolutionize information science and its applications. This book provides a concise, accessible introduction to quantum computing. It begins by introducing the essentials of quantum mechanics that information and computer scientists require, before moving to detailed discussions of quantum computing in theory and practice. As quantum computing becomes an ever-greater part of the global information technology landscape, the knowledge in Quantum Computing will position readers to join a vital and highly marketable field of research and development. The book's readers will also find: * Detailed diagrams and illustrations throughout * A broadly applicable quantum algorithm that improves on the best-known classical algorithms for a wide range of problems * In-depth discussion of essential topics including key distribution, cluster state quantum computing, superconducting qubits, and more Quantum Computing is perfect for advanced undergraduate and graduate students in computer science, engineering, mathematics, or the physical sciences, as well as for researchers and academics at the intersection of these fields who want a concise reference.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 558
Veröffentlichungsjahr: 2023
Ähnliche
Table of Contents
Cover
Title Page
Copyright
Preface
Author Biography
1 Introduction of Quantum Computing
1.1 Introduction
1.2 What Is the Exact Meaning of Quantum Computing?
1.3 Origin of Quantum Computing
1.4 History of Quantum Computing
1.5 Quantum Communication
1.6 Build Quantum Computer Structure
1.7 Principle Working of Quantum Computers
1.8 Quantum Computing Use in Industry
1.9 Investors Invest Money in Quantum Technology
1.10 Applications of Quantum Computing
1.11 Quantum Computing as a Solution Technology
1.12 Conclusion
References
2 Pros and Cons of Quantum Computing
2.1 Introduction
2.2 Quantum as a Numerical Process
2.3 Quantum Complexity
2.4 The Pros and Cons of the Quantum Computational Framework
2.5 Further Benefits of Quantum Computing
2.6 Further Drawbacks to Quantum Computing
2.7 Integrating Quantum and Classical Techniques
2.8 Framework of QRAM
2.9 Computing Algorithms in the Quantum World
2.10 Modification of Quantum Building Blocks
References
3 Methods and Instrumentation for Quantum Computing
3.1 Basic Information of Quantum Computing
3.2 Signal Information in Quantum Computing
3.3 Quantum Data Entropy
3.4 Basics of Probability in Quantum Computing
3.5 Quantum Theorem of No‐Cloning
3.6 Measuring Distance
3.7 Fidelity in Quantum Theory
3.8 Quantum Entanglement
3.9 Information Content and Entropy
References
4 Foundations of Quantum Computing
4.1 Single‐Qubit
4.2 Multi‐qubit
4.3 Measuring of Multi‐Qubit
4.4 States of Quantum Metamorphosis
References
5 Computational Algorithm Design in Quantum Systems
5.1 Introduction
5.2 Quantum Algorithm
5.3 Rule 1 Superposition
5.4 Rule 2 Quantum Entanglement
5.5 Rule 3 Quantum Metrology
5.6 Rule 4 Quantum Gates
5.7 Rule 5 Fault‐Tolerant Quantum Gates
5.8 Quantum Concurrency
5.9 Rule 7 Quantum Interference
5.10 Rule 8 Quantum Parallelism
5.11 Summary
References
6 Optimization of an Amplification Algorithm
6.1 Introduction
6.2 The Effect of Availability Bias
6.3 Quantum Amplitude Estimation and Quantum Counting
6.4 An Algorithm for Quantitatively Determining Amplitude
6.5 Counting Quantum Particles: An Algorithm
References
7 Error‐Correction Code in Quantum Noise
7.1 Introduction
7.2 Basic Forms of Error‐Correcting Code in Quantum Technologies
7.3 Framework for Quantum Error‐Correcting Codes
7.4 Coding Standards for CSS
7.5 Codes for Stabilizers
7.6 A Stabilizer Role for CSS Codes
References
8 Tolerance for Inaccurate Information in Quantum Computing
8.1 Introduction
8.2 Initiating Stable Quantum Computing
8.3 Computational Error Tolerance Using Steane's Code
8.4 The Strength of Quantum Computation
References
9 Cryptography in Quantum Computing
9.1 Introduction of RSA Encryption
9.2 Concept of RSA Encryption
9.3 Quantum Cipher Fundamentals
9.4 The Controlled‐Not Invasion as an Illustration
9.5 Cryptography B92 Protocol
9.6 The E91 Protocol (Ekert)
References
10 Constructing Clusters for Quantum Computing
10.1 Introduction
10.2 The Preparation of Cluster States
10.3 Nearest Neighbor Matrix
10.4 Stabilizer States
10.5 Processing in Clusters
References
11 Advance Quantum Computing
11.1 Introduction
11.2 Computing with Superpositions
11.3 Notions of Complexity
11.4 A Simple Quantum Algorithm
11.5 Quantum Subroutines
11.6 A Few Simple Quantum Algorithms
11.7 Comments on Quantum Parallelism
11.8 Machine Models and Complexity Classes
11.9 Quantum Fourier Transformations
11.10 Shor's Algorithm
11.11 Omitting the Internal Measurement
11.12 Generalizations
11.13 The Application of Grover's Algorithm It's Time to Solve Some Difficulties
11.14 Effective State Operations
11.15 Grover's Algorithm and Its Optimality
11.16 Amplitude Amplification using Discrete Event Randomization of Grover's Algorithm
11.17 Implementing Grover's Algorithm with Gain Boosting
References
12 Applications of Quantum Computing
12.1 Introduction
12.2 Teleportation
12.3 The Peres Partial Transposition Condition
12.4 Expansion of Transportation
12.5 Entanglement Swapping
12.6 Superdense Coding
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Quantum computing getting more powerful.
Table 1.2 Company funding in quantum technology.
Table 1.3 Year‐wise investing.
Table 1.4 Companies investing in quantum computing.
Chapter 3
Table 3.1 Decimal to binary representation (4 numbers).
Table 3.2 Decimal to binary representation (4 numbers).
Chapter 7
Table 7.1 Classical [3,1] repetition coding syndrome and associated error‐co...
Table 7.2 Error correction.
Table 7.3 Error code diagnosis.
Chapter 9
Table 9.1 Conversation between two objects.
Chapter 11
Table 11.1 The likelihood of a machine of a certain kind responding when x ...
List of Illustrations
Chapter 1
Figure 1.1 David Deutsch father of quantum computing.
Figure 1.2 Structure of bits and Qbits.
Figure 1.3 Structure of quantum annealing.
Figure 1.4 Structure of quantum simulation.
Figure 1.5 Quantum computing use in healthcare.
Figure 1.6 Quantum computing use in satellite for cyber security.
Figure 1.7 Ammonia use in agriculture.
Chapter 2
Figure 2.1 Structure of quantum computing complexity class.
Figure 2.2 Structure of QRAM architecture.
Chapter 4
Figure 4.1 Measurement of state.
Figure 4.2 Structure of Bell theorem.
Figure 4.3 Setup of Bell theorem.
Figure 4.4 An example of a graphical representation of a quantum gate array ...
Figure 4.5 Structure of dense coding.
Figure 4.6 Structure quantum teleportation.
Chapter 5
Figure 5.1 Structure of CNOT gate.
Figure 5.2 Reversible implementation of the binary addition on quantum state...
Chapter 7
Figure 7.1 Structure of CPF.
Figure 7.2 An U
P
circuit for syndrome extraction in the Steane programming l...
Figure 7.3 Measurement of the ancilla qubit.
Chapter 8
Figure 8.1 Computation of single bit.
Figure 8.2 Structure of quantum code.
Figure 8.3 Structure of environmental interactions shows each qubit.
Figure 8.4 Structure processing units.
Figure 8.5 Structure of fault‐tolerant error detection and correction.
Figure 8.6 Non‐fault‐tolerant construction of a cat state.
Figure 8.7 Fault‐tolerant cat state ancilla preparation.
Figure 8.8 Fault‐tolerant C
not
.
Figure 8.9 There is no transitive Toffoli gate T that can be constructed usi...
Figure 8.10 Circuit operation.
Figure 8.11 Measurement of fault‐tolerant measurement.
Chapter 10
Figure 10.1 Cluster structure.
Figure 10.2 A graph represents cluster state quantum computation.
Chapter 11
Figure 11.1 Circuit of qubits.
Figure 11.2 Structure of ancilla qubit.
Figure 11.3 Circuit for controlled rotation.
Figure 11.4 Structure of Bernstein–Vazirani problem.
Figure 11.5 Algorithm circuitry based on the work of Bernstein and Vazirani....
Figure 11.6 Structure of Hadamard.
Figure 11.7 Structure of Hadamard modifications.
Figure 11.8 Circuit of Hadamard.
Figure 11.9 Relationships of containment between several machines.
Figure 11.10 Containment relation involving classical and quantum complexity...
Figure 11.11 The R shuffle transform shown.
Figure 11.12 A quantum circuit that iteratively performs the Fourier transfo...
Figure 11.13 Grover's approach iterates by (a) flipping the good elements' s...
Figure 11.14 The initial state U|0 in the basis.
Figure 11.15 Structure of transformation variables.
Figure 11.16 The transformation causes |2 to exist by reflecting |1 down a l...
Figure 11.17 Number of iterations in Grover's approach.
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
Author Biography
Begin Reading
Index
End User License Agreement
Pages
ii
iii
iv
xiii
xiv
xv
xvi
xvii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
295
296
297
298
299
300
301
302
303
304
305
306
307
309
310
311
312
313
IEEE Press
445 Hoes Lane Piscataway, NJ 08854
IEEE Press Editorial Board
Sarah Spurgeon,
Editor in Chief
Jón Atli Benediktsson
Behzad Razavi
Jeffrey Reed
Anjan Bose
Jim Lyke
Diomidis Spinellis
James Duncan
Hai Li
Adam Drobot
Amin Moeness
Brian Johnson
Tom Robertazzi
Desineni Subbaram Naidu
Ahmet Murat Tekalp
Quantum Computing
A New Era of Computing
Kuldeep Singh KaswanGalgotias UniversityUttar Pradesh, India
Jagjit Singh DhatterwalKL Deemed to be UniversityAndhra Pradesh, India
Anupam BaliyanChandigarh UniversityPunjab, India
Shalli RaniChitkara University Institute of Engineering and Technology, Chitkara UniversityPunjab, India
Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc.Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750‐8400, fax (978) 750‐4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748‐6011, fax (201) 748‐6008, or online at http://www.wiley.com/go/permission.
Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762‐2974, outside the United States at (317) 572‐3993 or fax (317) 572‐4002.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com.
Library of Congress Cataloging-in-Publication Data:
Names: Kaswan, Kuldeep Singh, author. | Dhatterwal, Jagjit Singh, author. | Baliyan, Anupam, 1976- author. | Rani, Shalli, author.
Title: Quantum computing : a new era of computing / Kuldeep Singh Kaswan, Galgotias University, Uttar Pradesh, India, Jagjit Singh Dhatterwal, PDM University, Haryana, India, Anupam Baliyan, Chandigarh University, Punjab, India, Shalli Rani, Chitkara University Institute of Engineering, India.
Description: First edition. | Hoboken, New Jersey : John Wiley & Sons, Inc, [2023] | Includes index.
Identifiers: LCCN 2023002835 (print) | LCCN 2023002836 (ebook) | ISBN 9781394157815 (hardback) | ISBN 9781394157822 (adobe pdf) | ISBN 9781394157839 (epub)
Subjects: LCSH: Quantum computing.
Classification: LCC QA76.889 .K37 2023 (print) | LCC QA76.889 (ebook) | DDC 006.3/843–dc23/eng/20230201
LC record available at https://lccn.loc.gov/2023002835
LC ebook record available at https://lccn.loc.gov/2023002836
Cover Design: WileyCover Image: © AniGraphics/Getty Images
Preface
In the twenty‐first century, it is reasonable to expect that some of the most important developments in science and engineering will come about through interdisciplinary research. Already in the making is surely one of the most interesting and exciting development we are sure to see for a long time, quantum computation. A merger of computer science and physics, quantum computation came into being from two lines of thought. The first was the recognition that information is physical, which is an observation that simply states the obvious fact that information can't exist or be processed without a physical medium. At the present time, quantum computers are mostly theoretical constructs. However, it has been proved that in at least some cases, quantum computation is much faster in principle than any done by classical computer. The most famous algorithm developed is Shor's factoring algorithm, which shows that a quantum computer, if one could be constructed, could quickly crack the codes currently used to secure the world's data. Quantum information processing systems can also do remarkable things not possible otherwise, such as teleporting the state of a particle from one place to another and providing unbreakable cryptography systems.
Our treatment is not rigorous nor is it complete for the following reason: this book is aimed primarily at two audiences, the first group being undergraduate physics, math, and computer science majors. In most cases these undergraduate students will find the standard presentations on quantum computation and information science a little hard to digest. This book aims to fill in the gap by providing undergraduate students with an easy‐to‐follow format that will help them grasp many of the fundamental concepts of quantum information science. This book is also aimed at readers who are technically trained in other fields. This includes students and professionals who may be engineers, chemists, or biologists. These readers may not have the background in quantum physics or math that most people in the field of quantum computation have. This book aims to fill the gap here as well by offering a more “hand‐holding” approach to the topic so that readers can learn the basics and a little bit on how to do calculations in quantum computation.
Finally, the book will be useful for graduate students in physics and computer science taking a quantum computation course who are looking for a calculational oriented supplement to their main textbook and lecture notes.
The goal of this book is to open up and introduce quantum computation to these nonstandard audiences. As a result, the level of the book is a bit lower than that found in the standard quantum computation books currently available. The presentation is informal, with the goal of introducing the concepts used in the field and then showing through explicit examples how to work with them. Some topics are left out entirely and many are not covered at the deep level that would be expected in a graduate level quantum computation textbook. An in‐depth treatment of adiabatic quantum computation or cluster state computation is beyond this scope of this book. However, it will give readers who are new to the field a substantial foundation that can be built upon to master quantum computation. While an attempt was made to provide a broad overview of the field, the presentation is weighted more in the physics direction.
Dr. Kuldeep Singh Kaswan
Author Biography
Dr. Kuldeep Singh Kaswan is presently working in School of Computing Science & Engineering, Galgotias University, Uttar Pradesh, India. His contributions focus on BCI, Cyborg and Data Sciences. His Academic degrees and thirteen years of experience working with global Universities like, Amity University, Noida, Gautam Buddha University, Greater Noida and PDM University, Bahadurgarh, has made him more receptive and prominent in his domain. He received Doctorate in Computer Science from Banasthali Vidyapith, Rajasthan. He Received Doctor of Engineering (D. Eng.) from Dana Brain Health Institute, Iran. He has obtained Master Degree in Computer Science and Engineering from Choudhary Devi Lal University, Sirsa (Haryana). He has supervised many UG and PG projects of engineering students. He has supervised 2 PhD graduates and presently is supervising 4 PhD. He is also Member of Computer Science Teacher Association (CSTA), New York, USA, International Association of Engineers (IAENG), Hong Kong, IACSIT (International Association of Computer Science and Information Technology, USA, professional member Association of Computing Machinery, USA, and IEEE. He has number of publications in International/National Journal and Conferences. He is an editor/author, and review editor of Journals and Books with IEEE, Wiley, Springer, IGI, River etc.
Dr. Jagjit Singh Dhatterwal is presently working as an Associate Professor, Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India. He completed Doctorate in Computer Science from Mewar University, Rajasthan, India. He received Master of Computer Application from Maharshi Dayanand University, Rohtak (Haryana). He has also worked with Maharishi Dayanand University, Rohtak, Haryana. He is also Member of Computer Science Teacher Association (CSTA), New York, USA, International Association of Engineers (IAENG), Hong Kong, IACSIT, USA, professional member Association of Computing Machinery, USA, IEEE. His area of interests includes Artificial Intelligence, BCI and Multi‐Agents Technology. He has numbers of publications in International/National Journals and Conferences.
Dr. Anupam Baliyan is working as an Additional Director (Computer Science & Engineering) in Department of Computer Science and Engineering, Chandigarh University, Ghraun, Mohali, Punjab(India). He has more than 22 Years of Experience in Academic. He is MCA from Gurukul kangari University, MTech(CSE) and Phd(CSE) from Banasthali University. He published more than 30 Research papers in various International Journal indexed at Scopus and ESI. He is Life time member of CSI and ISTE. He has been chaired many sessions in International Conferences across the India. He also published some edited books and chapters. He is also the Asst. Editor of some Journals those are Scopus indexed. His Research Area is Algorithms, Machine learning, Wireless networks and AI.
Dr. Shalli Rani is pursuing postdoctoral from Manchester Metropolitan University from July, 2021. She is Associate Professor in CSE with Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. She has 18+ years teaching experience. She is pursuing postdoctoral fellowship from Manchester Metropolitan University, UK. She received MCA degree from Maharishi Dyanand University, Rohtak in 2004 and the MTech degree in Computer Science from Janardan Rai Nagar Vidyapeeth University, Udaipur in 2007 and PhD degree in Computer Applications from Punjab Technical University, Jalandhar in 2017. Her main area of interest and research are Wireless Sensor Networks, Underwater Sensor networks and Internet of Things. She has published/accepted/presented more than 70+ papers in international journals /conferences (SCI+Scopus) and edited/authored five books with international publishers. She is serving as the associate editor of IEEE Future Directions Letters. She is serving as a guest editor in IEEE Transaction on Industrial Informatics and Elsevier IoT Journals. She has also served as reviewer in many repudiated journals of IEEE, Springer, Elsevier, IET, Hindawi and Wiley. She has worked on Big Data, Underwater Acoustic Sensors and IoT to show the importance of WSN in IoT applications. She received a young scientist award in February 2014 from Punjab Science Congress, Lifetime Achievement Award and Supervisor of the year award from Global Innovation and Excellence, 2021.
1Introduction of Quantum Computing
1.1 Introduction
A significant advancement in computer science may take the form of a new algorithm that significantly outperforms the state of the art, or it may provide theoretical evidence that the state of the art cannot be significantly improved. The latter condition imposes a fundamental limit on the complexity of problems that any given computer can solve in a given amount of time. Increasing the computer's processing speed is the only way to increase the number of problems that can be solved. According to Moore's Law, the size of semiconductors (and, by extension, computing capability) has approximately doubled every two years since the 1960s. It is clear that, despite the fact that this development has been going on for decades, it cannot go on forever because of a number of basic physical constraints. As a result, quantum weirdness will dominate the behavior of the circuitry by 2020, and by 2050, the circuits will have achieved the lowest size at which knowledge can be permanently contained [1].
The results of this study have piqued the public's interest in how quantum theory may affect the future of computing over the next several decades. Is it possible, for instance, to make circuits immune to the influence of quantum effects? As an alternative, may quantum phenomena be exploited to do arithmetic? In order to do calculations, quantum computers take advantage of quantum phenomena. However, a quantum computer is not only a device with enhanced performance because of the faster speed of quantum‐scale circuits. It is of more interest to the software programmer than to the theoretical physicist. After all, the computational complexity of algorithms executed on a certain CPU remains the same regardless of the CPU's clock speed. Different algorithms may provide better complexity in terms of the new variable P if the computer's architecture is altered to include some number P of processors. We may able to reduce the greatest feasible complexity for solving a specific problem from O(N) to O(N/P), if we have a good parallel extraction of processors. However, not all algorithms can be broken down into O(P)‐independent portions that can be incorporated and enforced during the algorithm's operating time, therefore obtaining an O(P) complexity reduction is not always possible. To store and manipulate data, for instance, analog hardware and programmable real numbers may replace a discrete set of symbols, which would need a more radical redesign. It is possible that this design will prove to be far more powerful than the classic Turing machine. Because of the limitless precision with which a single physical value may be measured, it is possible to analyze massive amounts of data in parallel by treating them as a single unit cost. This is, of course, completely hypothetical since it assumes infinite precision can be maintained throughout those operations, and there is no reason to believe that such an infrastructure is physically conceivable. The potential of a quantum computer, which relies on the preservation of real, complex values, is underutilized [2].
1.2 What Is the Exact Meaning of Quantum Computing?
Large, complex datasets are no match for the speed with which quantum computers can process them. They use the foundations of quantum physics to speed up the process of doing complex computations. Quantum computers' ability to break cryptography and encrypted electronic communications is already changing portions of cybersecurity, and their usage in simulators with a practically endless quantity of variables has implications across fields, from biology to economics. The next large electronics race has already started [3], with some of the biggest names in industry, including Google, Microsoft, Intel, IBM, and Alibaba, exploring quantum computing to improve rates and other applications. Although Google has been studying quantum computing to speed up internet searches since at least 2009, the market for commercialized quantum entanglement is still in its infancy, and it is not yet obvious who will emerge as the market leader.
1.2.1 What Is Quantum Computing in Simple Terms?
Figure 1.1 depicts the interactions of matter in the universe at the level of fundamental particles, which provide the basis for special relativity, upon which quantum computing is founded. Bits can only be encrypted in classical computers if they have a value of 1 or 0.
Figure 1.1 David Deutsch father of quantum computing.
Source: Lulie Tanett (https://images.app.goo.gl/CQBoMf7JqWzXfr6r9).
1.3 Origin of Quantum Computing
Some types of computations now baffle today's computers and will continue to do so even if Moore's Law is extended indefinitely, although quantum computers may give a stronger correlation boost. Just imagine you have a phone book and need to find a certain number. A conventional computer would have to go through each listing in the phone book to find and provide the appropriate contact information. In theory, a computer system might scan an entire phone book in a fraction of a second, evaluating each line simultaneously and returning the result far faster than a modern computer [4]. The term “complex mathematical optimizing” is often used to describe the process of finding the best possible combination of elements and answers to a problem. Consider the costs of building the tallest building in the world, including machinery, food, labor, and permits. The challenge is in figuring out how to optimally allocate resources like money, time, and manpower. As a result, we may be able to plan for major projects with more efficiency with the aid of quantum computing if these factors are taken into account. Software development, supply chain management, finance, internet‐based research, genomics, and other fields all face optimization challenges. The most challenging optimization problems in these fields are inherently well‐suited for solution on a quantum machine [4] but stump conventional computers. In contrast to classical computers, which rely almost entirely on technological advances in transistors and microchips, quantum computers may evolve in ways that classical computers cannot. In quantum computers, transistors are not utilized (or classical bits). Substituting qubits for bits. In a quantum algorithm, qubits serve as the basic building blocks for pattern recognition. The example is shown in Figure 1.2.
Figure 1.2 Structure of bits and Qbits.
Source: Adapted from https://images.app.goo.gl/DeYCU9A7TeJvV5c16 Last accessed 25 Oct 2022.
Qubits may take on the characteristics of either a 0 or a 1, or they can have both at the same time. More choices exist to get accurate results quickly while doing computations. In addition, quantum entanglement and superposition are two important states of matter on which quantum computers depend. When applied to computing, these physical properties have the potential to greatly increase our ability to do very large computations [5].
Although Rigetti Computing's 19‐qubit devices are the most powerful in the field of quantum computing, but after 2019, the business is moving on 128‐qubit circuit. But as can be seen in Table 1.1, the race to build the most advanced quantum computer with the most qubits has been going on since at least the late 1990s.
Table 1.1 Quantum computing getting more powerful.
Year
Labs
Q‐bits
1998
IBM, Oxford, Berkeley, Stanford, MIT
2
2000
Technical University of Munich
7
2006
Institute for Quantum Computing
12
2008
D‐Wave System
28
2016
IBM
50
2018
72
2020
Rigetti
128
1.4 History of Quantum Computing
Conjugate coding was first developed in the 1960s by Stephen Wiesner. In the 1970s, James Park established the no‐cloning theorem using his formulation. Alexander Holevo proved what is now known as Holevo's theorem, or Holevo's bound, in a paper that was published in 1973. This theorem states that even though n qubits may store more relevant data than n classical bits, only n conventional bits are obtainable. This is despite the fact that n qubits may store more information than n classical bits.
Research conducted by Charles H. Bennett demonstrates that it is feasible to carry out computing in a backward‐compatible manner.
In 1975, R. P. Poplavskii published (in Russian) thermodynamical models of information processing. This work highlights the computational difficulties of reproducing quantum systems on classical computers owing to the fact that the superposition principle is at play.
In 1976, the Polish mathematician and physicist Roman Stanislaw Ingarden published Quantum Information Theory in the journal
Reports on Mathematical Physics
. Ingarden's paper “1976 Quantum Information Theory.” This study, which was one of the early efforts to build quantum synchronization theory, demonstrates that the traditional Shannon communication theory cannot simply be translated into the quantum situation. This was one of the earliest attempts to establish quantum entanglement theory. However, a quantum entanglement theory, which is a wide expansion of Shannon's theory, is possible to construct within the representation of an expanded subatomic particles of open systems and a generalized idea of explanatory variables that is both broad and imprecise (the so‐called semi‐observables).
Paul Benioff is credited with developing the very first computer model based on quantum physics in the 1980s. In this paper, Benioff paved the way for further research in quantum computing by laying the groundwork for future work in the field by proposing a Schrodinger equation description of Turing machines. This demonstration showed that a computer could operate in accordance with the rules of quantum physics. The work was first shown to the public in June 1979, and four months later, in April 1980, it was published. Yuri Manin presents a synopsis of the field of quantum computing in this article.
The reversible Toffoli gate, with the NOT and XOR gates, forms the foundation of a universal set that is used for bidirectional classical computing.
In May 1980, the Massachusetts Institute of Technology (MIT) played host to the First Conference on the Physics of Computation. At this conference, prominent figures in the field of computing, such as Paul Benioff and Richard Feynman, explored quantum computing. Benioff's current investigation is an expansion of his earlier work from 1980, which demonstrated the possibility that a computer may function in line with the principles of quantum physics. Quantum mechanical Hamiltonian models of discrete processes that delete their own histories: application to Turing machines,” the talk's title said. During his presentation, Feynman said that it seemed to be difficult to properly mimic the evolution of a quantum particle on a regular computer. In addition to that, he laid the foundation for the contemporary quantum algorithm.
Paul Benioff continued to develop his concept of a Turing machine that was based on quantum modeling in 1982. William Wootters, Wojciech Zurek, and Dennis Dieks all independently rediscovered the no‐cloning theorem at around the same time.
In 1984, Charles Bennett and Gilles Brassard resort to Wiesner's conjugate coding in order to distribute cryptographic keys in an uncompromised manner.
In 1985, while working at Oxford University, David Deutsch was the first person to conceptualize a universal quantum computer. A universal quantum computer, much like a multiclass support vector machine, has the potential to successfully imitate any other quantum computer with just a polynomial amount of latency (Church–Turing thesis).
Yoshihisa Yamamoto and K. Igeta, two physicists, developed the first practical implementation of a quantum algorithm in 1988. Their algorithm utilized Feynman's CNOT gate as one of its components. Their system utilizes both atoms and photons, which positions it as a forerunner of present quantum computing and networking protocols. These protocols employ photons to transport qubits, while atoms are utilized to carry out two‐qubit operations. Gerard J. Milburn demonstrates a quantum‐optical variant of the Fredkin gate in his presentation.
In 1989, researchers at the Saha Institute of Nuclear Physics in Kolkata, led by Bikas K. Chakrabarti, proposed that particle physics activity could be used to learn to navigate rough energy environments by tunneling (rather than trying to climb over using thermal vibrational modes) to escape from local minima of crystalline form systems with tall but thin barriers. This was done in an effort to break free from the local optimal solution of crystallized systems with large but small barriers.
In 1991, Artur Ekert of the University of Oxford expanded upon the idea of entanglement‐based encrypted communication proposed by David Deutsch.
David Deutsch and Richard Jozsa proposed a number of problems in 1992 that could be quickly solved on a quantum system with the assistance of the predetermined Deutsch–Jozsa automated system, but for which there are no feasible categorical imperatives using classical methodology. This problem was referred to as the “Deutsch–Jozsa algorithm problem.” It was the possible first discovery of its kind in the realm of quantum computing, and it demonstrated that qubits are capable of doing a particular computing job more rapidly and precisely than any conventional computer.
In 1993, Dan Simon, a professor at the University of Montréal, thought of the concept of an “oracle scenario,” in which a computer program would be able to do calculations at a rate that is geometrically faster than a typical computer. The enhancements that were made to Peter were based, in large part, on the core principles that are provided in this method.
Peter Shor of AT&T's Bell Labs in New Jersey discovered a crucial method via his factorization algorithm. A quantum computer can now quickly factor very large numbers using this method. As a bonus, it also solves the discrete log issue and the factoring problem. Many modern cryptosystems may be vulnerable to Shor's algorithm. Following its discovery, enthusiasm for quantum computers skyrocketed.
In the fall, the first federal government workshop on quantum computing will be held in Gaithersburg, Maryland, hosted by the National Institute of Standards and Technology (NIST).
Isaac Chuang and Yoshihisa Yamamoto, both of whom are physicists, believe that the most effective implementation of Deutsch's method would be to use a quantum computer that was instantiated via quantum optics. A new method of dual‐rail encoding for photonic qubits was developed as a consequence of their research.
During the month of December, Ignacio Cirac of the University of Castilla‐La Mancha in Ciudad Real and Peter Zoller of the University of Granada got together.
Researchers from Innsbruck University have suggested the controlled‐NOT gate as a potential use in the real world. In order to make the gate function properly, the researchers recommend using cold trapped ions.
Three US Army scientists, Charles M. Bowden, Jonathan P. Dowling, and Henry O. Everitt, planned the first US Department of Defense training course in electromagnetism and cryptography in February 1995 at the University of Arizona in Tucson.
Peter Shor is credited with having suggested some of the early quantum error‐correcting algorithms.
At the NIST in Boulder, Colorado, Christopher Monroe and David Wineland used trapped ions and the Cirac–Zoller principle to construct the first quantum logic gate. This gate was called the controlled‐NOT gate. In 1996, at Bell Labs, Lov Grover developed the first approach that might be considered practically useful for exploring quantum databases. Compared to a quadratic speedup, a factorization, discrete log, or linear speedup comes up more prominently.
Simulations of the physical world are one example. Having said that, the approach may be used to address a far larger range of issues. This speedup by a factor of four is available for use in any activity that can gain an advantage by doing a random brute‐force search (in the number of search queries).
The federal government of the United States has just released its first call for research ideas on quantum computing. This request is the result of a collaborative effort between the National Security Agency and the Army Research Office, which is now a branch of the Army Research Laboratory. Steane codes are a kind of error‐correcting code that was developed by Andrew Steane. IBM's David P. DiVincenzo sets out the fundamentals that have to be in place before one can build a quantum computer. These fundamentals are required in order to build a computational model.
In 1997 David Cory, Amr Fahmy, Timothy Havel, Neil Gershenfeld, and Isaac L. Chuang were all working at MIT at the same time when they published the first publications establishing gates for subatomic particles based on bulk nuclear spin‐responsive or thermal ensembles. These works were published simultaneously. This method makes use of a device known as a nuclear magnetic resonance (NMR) machine, which is relatively similar to magnetic resonance scanners used in the medical field.
Alexei Kitaev has presented topological quantum computing as one technique for lowering the risk of decoherence occurring in a quantum system.
The electrons contained within quantum dots serve as qubits in the Loss–DiVincenzo quantum computer, which was suggested by Daniel Loss and David P. DiVincenzo. Each electron has its own spin‐1/2 degree of freedom.
In 1998, a quantum algorithm was successfully realized for the first time in an experimental setting. Jonathan A. Jones and Michele Mosca of Oxford University and Isaac L. Chuang of IBM's Watson Research Center employed a two‐qubit NMR quantum computer to answer the issue that was presented by Deutsch. The problem was solved by the computer. Researchers from the Almaden Research Center, directed by Mark Kubinec, collaborated with colleagues from Stanford University and MIT. An NMR computer that has, for the first time, a data storage capacity equal to three qubits. Bruce Kane has developed a computational model for nuclear spins in silicon. In this model, the nuclear spins of certain phosphorus atoms in silicon serve as qubits, while donor electrons are responsible for mediating qubit coupling. The first time Grover's method was ever put into action, it was on an NMR computer. Researchers at the Tokyo Institute of Technology, headed by Hidetoshi Nishimori, have shown that quantum annealing is better than more traditional types of simulated annealing. Daniel Gottesman and Emanuel Knill, two different researchers, separately demonstrate that classical resources may successfully simulate a subset of quantum processes (the Gottesman–Knill theorem).
Isaac Chuang and Yoshihisa Yamamoto, who are both physicists, think that the use of a quantum algorithm that was created by the application of quantum optics would be the way that would result in the most successful application of Deutsch's method. As a direct result of their investigation, a novel approach to dual‐rail encoding for photonic qubits was conceived and created.
Ignacio Cirac and Peter Zoller, both from the University of Castilla‐La Mancha in Ciudad Real, and Peter Zoller, from the University of Granada, joined together during the month of December.
The controlled‐NOT gate has been proposed as a possible use in the real world by a group of researchers from Innsbruck University. The researchers suggest using cold ions that are confined in a vacuum in order to ensure that the gate operates correctly.
The first United States Department of Defense training course on electromagnetism and cryptography is scheduled to take place at the University of Arizona in Tucson in February 1995. The course is being planned by three scientists who are employed by the United States Army: Charles M. Bowden, Jonathan P. Dowling, and Henry O. Everitt. Henry O. Everitt, Charles M. Bowden, and Jonathan P. Dowling are the authors of this work.
Peter Shor is widely acknowledged as having proposed some of the first quantum error‐correcting algorithms.
Christopher Monroe and David Wineland built the first quantum logic gate at the NIST in Boulder, Colorado, by using trapped ions and the Cirac–Zoller principle. This gate was known as the controlled‐NOT gate at one point in time. In 1996, Lov Grover created the first method that might be regarded as realistically viable for browsing quantum datasets at Bell Labs. This method was the first of its kind. A factorization, discrete log, or linear speedup shows a more significant increase when compared to a quadratic speedup.
One example of this would be simulations of the physical world. Having said that, the strategy may be used to solve a far wider variety of problems than I've mentioned here. This increase in speed by a factor of four is accessible for use in any endeavor that can benefit from carrying out a random brute‐force search (in the number of search queries).
The United States federal government has officially issued its first request for ideas pertaining to research on quantum computing. The National Security Agency and the Army Research Office, which is now a part of the Army Research Laboratory, are working together to put out this call for proposals. This request is the result of their joint effort. Steane codes are a kind of error‐correcting code that was invented by Andrew Steane. Steane codes were developed in the 1960s. David P. DiVincenzo of IBM lays out the principles that have to be in place before one can develop a quantum computer. These fundamentals are necessary in order to build a quantum computer. To construct a computational model, you will need to have a firm grasp of these foundations.
In 1997, when David Cory, Amr Fahmy, Timothy Havel, Neil Gershenfeld, and Isaac L. Chuang published the first works defining gates for subatomic particles based on bulk nuclear spin‐sensitive or thermal ensembles, they were all working at MIT at the same time. These pieces appeared in publications at the same time. This technique makes use of a machine that is referred to as an NMR machine, which is somewhat comparable to magnetic resonance scanners that are used in the area of medicine.
Alexei Kitaev has proposed topological computational methods as a method that may reduce the likelihood of decoherence taking place in a quantum system.
The Loss–DiVincenzo quantum computer, which was proposed by Daniel Loss and David P. DiVincenzo, uses the electrons that are housed inside quantum dots as qubits. Every electron has a degree and a half of spin‐dependent freedom individually.
It was not until 1998 that a quantum algorithm was effectively implemented in an experimental environment for the very first time. Jonathan A. Jones and Michele Mosca of Oxford University and Isaac L. Chuang of IBM's Watson Research Center used a two‐qubit NMR quantum computer to answer the question that was posed by Deutsch. Deutsch was the one who posed the question. The issue was resolved as a result of the computer's efforts. Researchers from Stanford University and MIT worked with their counterparts at the Almaden Research Center, which is managed by Mark Kubinec. A first‐of‐its kind NMR computer with a data storage capacity equivalent to three qubits. An innovative computational model for nuclear spins in silicon was created by Bruce Kane. Donor electrons are responsible for mediating qubit coupling in this paradigm. The qubits themselves are the nuclear spins of certain phosphorus atoms in silicon. The NMR computer was the platform on which Grover's approach was first implemented for the first time in history. Researchers at the Tokyo Institute of Technology led by Hidetoshi Nishimori have found quantum annealing to be superior to more conventional kinds of simulated annealing. Both Daniel Gottesman and Emanuel Knill, who are scholars in their own right, show that classical resources may effectively imitate a subset of quantum processes in their own respective studies (the Gottesman–Knill theorem).
In 2007, the development of a waveguide with a sub‐wavelength light signal. Creation of an optical fiber‐based single‐photon emitter. We construct a six‐photon, single‐direction multicore computer. There is a new suggested material for use in quantum computers. There is now a server for spontaneous emission from a single atom. This is the first instance of Deutsch's algorithm being implemented on a quantum computer with a cluster state. An electron quantum pump has been developed at Cambridge University. Better qubit coupling methods have been developed. Demonstration of qubits with a connection that can be controlled. An important step forward in incorporating spin‐based electronics with silicon‐based devices. The quantum states of light and matter are shown to exchange with one another by scientists. Making a quantum register out of a diamond. In this scenario, we use a controlled NOT to activate quantum gates implementation of two superconducting quantum bits in a three‐dimensional array, scientists may hold and analyse hundreds of individual atoms. The buckyball molecule, which contains nitrogen, is used in quantum computing. Several hundreds of electrons have established quantum connections. The spin‐orbit coupling of electrons was quantified. Laser‐light‐based atomic manipulation at the quantum level. Electronic spins are controlled by pulsing light. Over a range of tens of nanometers, quantum effects have been shown. The evolution of quantum computers is being hastened by the use of light pulses. Plans for quantum random‐access memory are now public knowledge. Development of a prototype quantum transistor. Proof of long‐range entanglement has been shown. Photonic quantum computing was used to factor numbers in two separate labs. The quantum bus was developed in a joint effort between two separate laboratories. Construction of a quantum cable using superconducting technology. An example of qubit transfer is shown. The development of high‐quality qubit material is a major achievement. Electronic memory have single qubit space in the disk. Quantum memory via Bose–Einstein condensation has been realized. D‐Wave Systems showcases a 28‐qubit processor in action. By decreasing decoherence and increasing interaction distance, a novel cryonic technique improves the efficiency of quantum computers. A proof‐of‐concept for a photonic quantum computer has been shown. The use of graphene quantum dots as spin qubits has been suggested [6].
In 2008, researchers were able to store a quantum bit in graphene quantum dots, demonstrate three‐dimensional qubit–qutrit entanglement, and establish analog quantum computing. Controlling quantum tunneling led to the creation of entangled memory, the invention of a superior NOT gate, the discovery of an optical fiber quantum logic gate, the development of qutrits, and the creation of a better Hall as a result, we may infer that the spin states of quantum dots are stable for a considerable amount of time. A quantum memory based on molecular magnets has been proposed.
The possibility of a reliable quantum computer is improved by the existence of quasiparticles.
It is possible that qubit storage is preferable than image storage.
Quantum entangled pictures.
Modified the quantum state of a molecule on purpose.
Microwave photons are pumped into a silicon circuit with the help of a superconducting electronic circuit.
D‐Wave Systems claim that it has designed a computer chip with 128 qubits; however, this has not been independently validated.
In 2009, the purity of carbon‐12 was improved, which should result in increased coherence over longer periods of time.
Entanglement of the six‐photon graph state is used in order to simulate the fractional statistics of anyons that are situated in synthetic spin‐lattice models.
Quantum computing: create a photon grenade launcher the development of a quantum algorithm for use with differential equation systems – Presentation of the world's first quantum system, which has completely digital control hardware as well as software. Scientists are able to change the atoms and molecules of electrons using electromagnetic energy. Google, in collaboration with D‐Wave Systems, presented a technique for synchronizing the characteristic features of several linked CJJ rf‐SQUID flux qubits with a low distribution of electronic resistivity due to manufacturing differences. The spectrum response of hydrogen and helium was correctly calculated by an optical quantum computer with three qubits in 2010; the first semiconductor materials laser brings us closer to electro‐optical computing systems. Ions were captured via an optical trap in 2010.
The transmission of subatomic particles across a quantum communications channel may be sped up by architectures that use multiplexing. Quantum state in macroscopic object. Innovative strategy for the cooling of quantum computers. Quantum contact between a single photon and a single atom has been shown to exist using microfabricated planar ion traps in the research. Quantum bits (or “qubits”) are handled using electrical current rather than magnets.
Electron quantum states are electrically controlled by scientists.
A technique for synchronizing the characteristics of several connected CJJ rf‐SQUID flux qubits with a minimal spread of electrical characteristics owing to manufacturing variances was shown by Google in collaboration with D‐Wave Systems.
Realization of Universal Ion Trap Quantum Computation with Decoherence Free Qubits 2010
Ion trapped in optical trap
Optical quantum computer with three qubits calculated the spectral response of hydrogen and helium with high precision
First semiconductor materials laser brings us closer to electro ‐ optic computer systems
Single electron qubit established
Quantum state in macroscopic object
New quantum computer cooling method developed
Quantum interface between a single photon and a single atom proven
LED quantum entanglement established
Multiplexed design speeds up quantum information transfer across a quantum communications channel
Planar ion traps that have been microfabricated Qubits are controlled electricity
In a solid‐state spin ensemble, what exactly is meant by the term “entanglement”? In a quantum semiconductor technology that makes use of superconductivity, light from the near‐outer radiation (NOON) is used. Quantum antennas are due to multimode quantum interference.
Atomic racing dual
Quantum pen
D‐Wave one product claims that it discovered quantum annealing. The company claims its product is the world's first quantum computer accessible for purchase. It has been demonstrated that a quantum processor can perform repetitive error correction, that a diamond can be used as a storage medium for a quantum computer, that Modes can be established, that DE coherence can be suppressed, that controlled operations can be streamlined, and that ions can be entangled using microwaves.
Repetitive error correction performed in a quantum processor
Diamond quantum computer storage exhibited
Qmodes established
DE coherence suppressed
Simplification of controlled operations
Ions entangled using microwaves
Practical error rates attained Quantum Entanglement might aid in the development of photonic processors.
It was reported that a quantum simulator with 300 qubits or particles had been successfully built.
A topologically protected qubit that is entangled with eight photons provides a safe and secure approach to the implementation of real quantum computing [7].
In the beginning, there was 1QB Information Technologies (1QBit). The first software firm in the world to exclusively concentrate on quantum computing. Developed the first system for repeating quantum operations that does not depend on quantum memory.
At room temperature, the use of a laser to manipulate carbon‐13 atoms briefly and for a period of two seconds reduced decoherence.
The development of a revolutionary, low‐overhead approach for building fault‐tolerant quantum logic, which is referred to as lattice surgery. This method is developed on the concept of Bell‐based unpredictable expansion and makes a more moderate assumption of measurement being independent.
In 2013, 39‐minute coherence times have been measured for ensembles of impurity‐spin qubits in isotopically linked systems while the temperature was maintained at room temperature (and three hours at cryogenic temperatures). The amount of time that a qubit remains in a superimposed state has been multiplied by 10 in order to account for the change.
In 2014, the first ever evaluation of this kind was constructed for factoring in order to determine whether or not it would be possible to implement a large‐scale quantum algorithm with explicit fault‐tolerant and error‐correcting protocols.
The NSA's Penetrating Hard Targets program, which is constructing a computer program for the purposes of cryptanalysis, has received backing as a result of the disclosures that were made public by Edward Snowden.
In a first‐of‐its‐kind development anywhere in the world, researchers from Japan and Austria have made public the designs for a huge quantum computer based on diamonds. Researchers at the University of Innsbruck accomplish quantum numerical computations on a qubit that is topologically encapsulated and password protected in linked states that are scattered among seven trapped‐ion qubits.
Using neutrino oscillation, scientists have succeeded in sending data across a distance of 10 feet (3.048 m) with no discernible delay. Percent of inaccuracies, a significant achievement on the way to the construction of a quantum network. Nike Dattani and Nathan Bryans have set a new record for the number 56 153 that can be factored using a quantum device.
In 2015, nuclear spins in a solid, which can have their coherence examined optically, may have coherence periods of up to six hours. A quantum process known as transcription makes use of straightforward electrical pulses, and a quantum error detection code is based on a square lattice of four superconducting qubits as its fundamental building block. On June 22, D‐Wave Systems Inc. made the announcement that the company has reached a breakthrough of 1,000 qubits. A silicon logic gate with two qubits has been designed and tested satisfactorily. By simulating its behavior after that of a classical computer and replicating quantum states such as quantum superposition and entanglement using a traditional analog web browser, it is possible to develop a completely classical framework that behaves like a real quantum computer. This is made possible by designing its behavior after that of a quantum computer.
Researchers headed by Rainer Blatt and Isaac Asimov used an ion‐trap‐based quantum computer to solve the problem. In 2016, Chuang at MIT was successful in running the algorithm developed by Shor. The online interface for IBM's superconducting systems, known as the Quantum Experience, has been made available. After that, the system is put to use in the propagation of cutting‐edge techniques in digital signal processing. In order to replicate a hydrogen molecule, Google makes use of an array consisting of nine superconducting qubits. This array was built by the Martinis group at UCSB. In 2017, researchers from Japan and Australia created a quantum version of the communications system known as Sneakernet. D‐Wave Systems Inc., claims that the D‐Wave 2000Q quantum annealed is now readily accessible for widespread usage in business settings. This apparatus is capable of storing 2000 qubits of information. The blueprint for a quantum computer that operates by entrapping ions in microwaves has been made available to the public. A novel approach to evaluating IBM's 17‐qubit quantum computer has been made public by the company. Scientists have devised a device that can produce two entangled qubits, each of which may exist in one of ten distinct states consisting of a hundred different dimensions Visual Studio now comes equipped with Q Sharp, the newest quantum software platform developed by Microsoft. For the purpose of program execution, there is accessibility to both a local 32‐qubit simulator and a cloud‐based 40‐qubit simulator. Intel recently claimed in a news release that it had produced a superconducting test circuit with 17 qubits. The device was used for testing purposes. IBM demonstrates for the very first time a functional model of a quantum computer that has 50 qubits and has the ability to maintain its physical phenomenon for 90 microseconds [8].
