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C2020-003 - IBM Algo Fundamantals - Dump Information

Vendor : IBM
Exam Code : C2020-003
Exam Name : IBM Algo Fundamantals
Questions and Answers : 60 Q & A
Updated On : December 17, 2018
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C2020-003 Questions and Answers

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C2020-003 IBM Algo Fundamantals

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C2020-003 exam Dumps Source : IBM Algo Fundamantals

Test Code : C2020-003
Test Name : IBM Algo Fundamantals
Vendor Name : IBM
Q&A : 60 Real Questions

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Why and the way: Quantum Computing | killexams.com Real Questions and Pass4sure dumps

From the November 2018 concern

you are prone to have viewed all the information you need on the new iPhone, Google Pixel three, surface seasoned 6, and workplace 2019 as smartly as the effects of the CCH, Thomson and Intuit conferences, although if the reports aren't factual ample for me, you’ll see me pause this rising expertise collection to determine the information that depend for the practice of accounting. while Quantum Computing isn't equipped for major time, there's lots of excitement amongst builders and the most important expertise providers including, but now not limited to IBM, Google, Microsoft, Intel, and a few other huge gamers that you doubtless gained’t respect like D-Wave and Rigetti wooded area.

Let me be clear with out being hyperbolic, if the developers are a hit, Quantum Computing will trade the future and path of computing for many generations together with lengthy after I’m useless and gone. As a informed laptop scientist, my expectation and that of protagonists say that Quantum Computing could be in a position to damage all encryption in use these days, can tamper with most blockchain implementations, can outperform any latest computing hardware (typically known as Von Neumann computing in line with the design from the 1940’s used by all computers and smartphones nowadays), and has many operational attributes of interest that increase centralized, cloud computing. Antagonists claim that Quantum Computing capabilities are overstated, that the atmosphere is too difficult to make use of, and that the technical difficulties will not be overcome. Let me again up my hair-brained statements with some observations.

  • Quantum Computing is in the early tiers. Intel CES 2018 announcements:
  • Intel Labs has developed a neuromorphic analysis chip, code-named “Loihi”, which is a Quantum Computing enabled synthetic intelligence (AI) algorithm inspired via how the brain works. This may unencumber exponential beneficial properties in efficiency and vigor effectivity for the way forward for AI.
  • Intel’s Mike Mayberry, corporate vice chairman and managing director of Intel Labs said, “We predict it might be five to seven years before the trade receives to tackling engineering-scale complications, and it will doubtless require 1 million or more qubits to obtain industrial relevance.”
  • As Quantum Computing matures over the subsequent 10 years, it could threaten blockchain and most cryptography that secures statistics and digital signatures
  • Let’s accept as true with encryption. Quantum Computing procedures smash encryption.

  • Shor's algorithm can effortlessly locate public key integer components. This ability would permit a quantum computing device to decrypt many of the cryptographic methods in use nowadays.
  • accept as true with a problem that has these four homes:
  • The only approach to remedy it is to guess solutions time and again and verify them,
  • The variety of viable solutions to examine is the same because the variety of inputs,
  • every feasible answer takes the equal amount of time to investigate, and
  • There aren't any clues about which solutions may be more desirable: generating chances randomly is barely as good as checking them in some particular order
  • For problems with all four houses, the time for a quantum laptop to resolve this should be proportional to the rectangular root of the number of inputs. Triple DES and AES turn into effortless pursuits, which might be essentially the most normal encryption algorithms nowadays.
  • these things receives a little heady, and i definitely wouldn’t need you to believe I actually have mastered the entire concepts. Like all of the emerging technologies we have lined in these rising know-how columns, Quantum Computing has pros and cons.

    On the positive facet:

  • Computing the use of quantum-mechanical phenomena, equivalent to superposition and entanglement which allows representing an awful lot extra facts in a single place, known as a qubit
  • Google says 2018 and IBM says inside five years they're going to obtain quantum supremacy, it's working sooner than a classical desktop
  • simply 50 qubits can represent 10,000,000,000,000,000 numbers. A classical computer would require a petabyte-scale reminiscence to shop that quantity
  • On the down facet:

  • it's challenging currently to scale physically to boost the number of qubits
  • Qubits can also be initialized to arbitrary values and read quite simply
  • Quantum gates want faster decoherence time, currently optimized by using cryogenic temperatures, 20 Millikelvins = - 459 Fahrenheit
  • while the existing prototype hardware is working, the variety of qubits in use are quite small and the existing designs require complex computing. note from this photograph how IBM is cooling a 56-Qubit design:

    Why

    Limits are being approached in the current Von Neumann architectures produced via Intel, AMD and others world wide. The silicon architectures had been miniaturized to 7 nanometers (nm) by TSMC and Intel is currently trying to transition from 14nm to 10nm. it's becoming more complicated to construct processing chips based on these small distances or geometries.

    extra, the hints that engineers have used together with hardware virtualization, diverse cores and threads have made the chips quicker, too. youngsters, there isn't any big leap forward in the engineering thinking that allows you to improve performance peculiarly. whereas I’m hopeful that some sensible engineer will get a hold of a way to make chips quicker and smaller there is nothing this is being suggested within the engineering alternate journals that shows any promise. while I’m certain massive breakthroughs can be stored secret, lots of the methods over the final few years were evolutionary, no longer revolutionary trade. A ruin through is required, and Quantum Computing can be that breakthrough.

    What?

    For greater tips about building Quantum Computing, believe the following references:

  • A classical laptop has a memory made from bits, where every bit is represented by way of both a one or a zero. A quantum desktop continues a chain of qubits, examine ket 0 and ket 1
  • A single qubit can signify a one, a nil, or any quantum superposition of these two qubit states. A pair of qubits can also be in any quantum superposition of four states and three qubits in any superposition of 8 states
  • The representations of those states are graphically and mathematically represented through here:

    in addition, right here links will supply more introduction into the realm of Quantum Computing:

    How?

    So how does the Quantum Computing approach work? Physicists and laptop scientists appear on the properties of atoms and detect or predict the place of a variety of particles mathematically. The table below suggests the a considerable number of strategies being used together with: photons, coherent state of light, electrons, Nucleus, Optical lattices, Joseph junctions (tremendous conductors of cost, flux or part), Singly charged quantum dot pairs, or Quantum dots.

    What does this imply to the apply of accounting and to accountants? Our centralized functions will run on these platforms, currently in use by means of IBM, Google, NASA, and other large entities. As we outlined above, we've several working examples and construction fashions purchasable:

    here’s a abstract of what you need to know about Quantum Computing:

    Key counsel

    know-how: Quantum Computing

    Why is the new know-how more desirable?

    it's a new system of running programs, potentially at bigger speeds

    How can you try this nowadays?

    D-Wave ,IBM Q, Google, Intel, Microsoft,

    Rigetti wooded area

    dangers

    Computation might also now not be sooner

    where/when to use

    problems that ought to deal with gigantic quantities of information

    How a good deal?

    industrial contraptions are $15M+ for 2,000 qubits

    When anticipated in mainstream

    Ten+ years

    Displaced technology or carrier

    ordinary computer systems, mainframes

    different elements

    Waterloo Quantum one zero one, How QC Work

    enforcing Quantum Computing can take extremely colossal calculations and finished them greater rapidly. Quantum Computing is getting used in very advanced models today, such as climate.com by means of IBM. builders are working on compilers and algorithms to make these machines work effectively. From my view, Quantum computers are at concerning the equal stage that mainframe computers have been in 1963-1965 when mainframes (that are Von Neumann machines) had been simply getting the early compilers and functions were standard, but simply starting to work. The change between mainframes and Quantum computer systems is that as soon as building the hardware is useful and the compilers are working accurately, the additional speed attainable will revolutionize Cloud computing featuring the horsepower to run purposes radically quicker. Conversion to Quantum Computing may still predominantly imply recompiling functions to take abilities of the brand new platform.

    suggested subsequent Steps

    Quantum Computing is in its very early levels. This know-how could show to be a dead conclusion, but all signals are that we're past that stage. The largest problem is that hardware gates deserve to be designed that enable a quicker decoherence time that function at room temperature in place of in cryogenics. We’ve tried to provide you with satisfactory history so you might be aware probably the most fundamentals of Quantum Computing. whereas there is not any purposeful choice for many of you nowadays on account of the high can charge and particular atmosphere required through Quantum computer systems, you should wait for breakthroughs over the subsequent 5-10 years so as to permit your purposes to run sooner than is viable today.


    AI Nanodegree application Syllabus: time period 2 (Deep discovering), in depth | killexams.com Real Questions and Pass4sure dumps

    So let’s get begun! here is our first 3-month curriculum in depth, including all of the tasks and labs that you simply’ll be constructing.

    Core application

  • Deep Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Concentrations

  • Voice user Interfaces — with Amazon Alexa
  • natural Language Processing — with IBM Watson
  • computing device vision — with Affectiva
  • Core software Introduction to Deep Neural Networks

    Neural Networks are the simple constructing block of Deep learning. during this classification, you’ll learn their structure, and the way to build them and coach them. We’ll delivery the classification by using solving linear classification problems the usage of perceptrons and logistic regression. From there, we’ll extend to solving tremendously non-linear classification complications using deep neural networks. We’ll learn about their structure, a way to coach them the use of concepts akin to gradient descent and backpropagation, and how to optimize the practising system using diverse error features and regularization suggestions.

    Lab: constructing Neural Networks in Keras

    You’ll finish this lesson with a coding lab where you’ll have the chance to apply all these ideas in Keras, one of the most common Deep getting to know applications in Python.

    Convolutional Neural Networks

    Convolutional Neural Networks are known for graphic classification, amongst many different unique purposes. during this section you’ll learn the way they differ from common neural networks. After exploring some new forms of layers, we’ll discover a number of distinctive network architectures. You’ll discover the way to augment your records to enhance efficiency, earlier than researching strategies to visualize what your networks learn. You’ll be taught to faucet into the vigor of switch discovering, with the aid of benefitting from pre-expert networks reminiscent of VGGNet or ResNet.

    undertaking: construct a Dog consciousness App

    You’ll apply what you’ve realized to construct an conclusion-to-conclusion algorithm to system any user-presented picture. Given an image of a dog, your algorithm will establish an estimate of the dogs’s breed. If supplied a picture of a human, the code will identify the resembling dog breed. What kind of dog will your algorithm feel you appear to be? :)

    Recurrent Neural Networks

    Recurrent Neural Networks are used for some very entertaining functions, similar to time series predictions and sequence era. Their architecture is different from feedforward neural networks, as the assistance cycles throughout the community, allowing it to be aware outdated states.

    undertaking — half 1: Predicting Apple inventory

    You’ll follow what you’ve realized to construct an algorithm which will operate time series predictions, and may forecast the inventory rate of Apple 7 days in improve.

    task — half 2: producing Sherlock Holmes textual content

    You’ll additionally enforce a recurrent neural community to create an English language generator, that will build semi-coherent English sentences from scratch. You’ll be in a position to examine it through feeding it with the textual content of Sir Arthur Conan Doyle’s classic ebook, The Adventures of Sherlock Holmes, to be able to acquire text that resembles a Sherlock Holmes novel! fundamental, my pricey IBM Watson! :)

    Concentrations awareness 1: Voice person Interfaces with Amazon Alexa

    in this concentration, you’ll learn how computers can procedure speech, flip it into text, and vice versa. in the first half, you’ll get a top level view of Voice consumer Interfaces (VUI), center of attention on Conversational AI, and learn the way Alexa operates. Then, you’ll dive deeper into the entertaining box of Speech cognizance, discovering sign evaluation and Phonetics, single observe classification using Dynamic Time Warping, and sentence awareness the use of Hidden Markov fashions. ultimately, you’ll study about the leading edge in automatic Speech awareness, leveraging deep neural networks.

    Amazon Alexa Lab — constructing an Alexa ability

    in this lab, you’ll get your hands on the the Alexa advantage kit, where you’ll construct and deploy your first Alexa ability, so that you can stick with it a small dialog with the consumer and provide a fact from a given yr.

    Capstone assignment — Speech attention with Neural Networks

    in this challenge, you're going to construct a deep neural network that capabilities as a part of an end-to-conclusion automatic speech awareness (ASR) pipeline! Your accomplished pipeline will settle for uncooked audio as enter and return a predicted transcription of the spoken language. the entire pipeline is summarized in the determine under.

    First, you’ll preprocess the uncooked audio to transform it to certainly one of two feature representations that are time-honored for ASR. Then, you'll study concerning the basic kinds of neural networks that are often used for acoustic modeling, and be able to construct your personal acoustic mannequin! This model may be used as enter into the decoder, a good way to return the expected transcription.

    awareness 2: natural Language Processing with IBM Watson

    natural Language Processing (NLP) is a desirable box through which we train computer systems to be mindful and analyze text. in this concentration, you’ll learn to decompose a problem that includes examining herbal language text into projects, function basic NLP operations, akin to constructing an N-gram language model from a given corpus, and labelling words in a sentence with half-of-Speech (POS) tags and as named entities. you'll accomplish end-to-conclusion NLP tasks corresponding to doc classification, desktop translation, and so forth., using a combination of custom processing and cloud-based mostly APIs.

    IBM Watson Lab — question-Answering Agent with Watson

    in this lab, you're going to build a simple question-answering agent that's capable of be trained from any text statistics you deliver, and answer queries posed in natural language. you will use IBM Watson’s cloud-primarily based functions to manner the input textual content facts and find crucial responses.

    Capstone venture — computer Translation

    during this mission, you are going to build a deep neural network that capabilities as a part of an conclusion-to-conclusion computer translation pipeline. Your completed pipeline will settle for English text as enter and return the French translation.

    First, you’ll preprocess the information to transform text to a chain of integers. Then, you’ll create models which accept a sequence of integers as input, and return the probability distribution over possible translations. After researching about the fundamental kinds of neural networks that are sometimes used for laptop translations, you could be able to interact in your personal investigations, to design your own computer translation mannequin for you to run on English textual content!

    awareness three: computing device imaginative and prescient with Affectiva

    impressed by using human vision, computing device vision aims to give machines the means to look and interpret the world with the aid of extracting assistance from photos. in this attention, you’ll be trained the fundamentals of computer imaginative and prescient and the function it plays in artificial intelligence methods. computer vision is used in lots of functions, from detecting dermis melanoma to emotion attention (and even self-using car navigation!). all the way through this time period, you’ll develop useful knowledge and get hands-on coding experience with lots of these precise-world purposes. You’ll be trained to ruin down any issue that includes visible notion into laptop imaginative and prescient projects, akin to: enhancing pictures, making use of colour and geometric transformations to trade the look of an image, detecting object boundaries, computing gradients and filtering pictures, and extracting points like object edges and pleasing visible patterns. We’ll go over these foundational desktop vision ideas in aspect. Then, you’ll make the most of what you’ve learned to create an entire AI equipment that makes use of computing device vision to operate sensible object detection and endeavor attention.

    Affectiva Emotion API Lab — Mimic Me!

    in this lab, you’ll be taught to music faces in a video and establish facial expressions (pleasure, sadness, surprise, and so forth.) the usage of AffedexMe from Affectiva. As a fun visualization, it can be up to you to tag every identified face with its acceptable emoji! Then, you’ll flip this into a game where a participant needs to mimic a random emoji displayed via the computing device.

    Capstone mission — Facial Keypoint Detection

    during this challenge, you’ll combine your skills of desktop imaginative and prescient innovations and deep researching to construct an conclusion-to-conclusion facial keypoint awareness system! Facial keypoints encompass facets around the eyes, nose, and mouth on a face and are used in many purposes, from facial monitoring to emotion attention.

    This undertaking is broken into just a few leading ingredients. First, you’ll use your capabilities of desktop imaginative and prescient and OpenCV to observe faces. You’ll also use graphic filtering techniques to de-noise images and transform the look of a face in an image. second, you’ll define and instruct a Convolutional Neural network (CNN) to realize facial keypoints. ultimately, you’ll put these two materials collectively so that you should reliably establish facial keypoints on any picture!

    You’ll also be given optional exercises that e book you in extending this undertaking to video (or a computer digicam feed); this gives you the potential to put into effect enjoyable face filters and keypoint detection in real time!

    college students in this program will master an in-depth curriculum that covers both the foundations and the slicing-edge advancements of Deep discovering. This variety of palms-on exposure to probably the most transformational applied sciences of our time makes this program a special learning probability for anyone excited through the percentages of AI. We invite you to join us as we prepare the subsequent era of AI experts who will energy the unbelievable innovations ahead!

    think free to reach out to us if you have any questions, and notice you in the application!

    observe now to be part of the AI Nanodegree software!

    Driverless automobiles and the look for true AI | killexams.com Real Questions and Pass4sure dumps

    Introduction

    The look for actual artificial Intelligence is the foundation of the continual construction of vehicles devoid of drivers. here's what you should find out about it.

    What is true artificial Intelligence? anything it's, consultants rush to make clear the proven fact that what we now have in the meanwhile is not.

    “To get to the element the place we now have a true synthetic ordinary intelligence, we're an extended method off. We are not even near that, and researchers in the AI enviornment understand,” says Raveen Nao Intel.

    Why are we to this point? in short, constructing a real artificial ordinary intelligence is terribly elaborate on account of one fundamental fact: intelligence is greater than the recognition and the reaction of patterns. If there is only one photo that represents intelligence, it's the Sistine Chapel of Michelangelo.

    The Paradox of Intelligence 

    The noted fresco The introduction of Adam indicates God giving lifestyles to Adam, however the finger of God is not touching Adam (or Adam’s finger isn't touching God, counting on how you examine it).

    The level of logic and secret and abstraction and actualization establishes the creation of Adam within the divine realms as profane each on the equal time. Intelligence is the space between the finger of Adam and the finger of God. To ask, “How do you're making poor area mean anything?” And the laptop will certainly emit absurd; at least not produce an such as the creation of response Adam. Go forward, ask Siri.

    after we are trying to create a real AI, we are trying to animate an artificial Adam, however for this to be viable, we need to create a paradox: a being able to logical and illogical thinking on the equal time. For abstraction and real intelligence to be feasible, a mind need to be in a position to withdraw from itself.

    self sustaining vehicles are probably the most crucial and basic piece of the actual AI jigsaw puzzle that advances; right here will inform you why.

    Why automobiles with no Driver be counted

    besides being crucial to AI, vehicles without drivers are tremendously essential for the future of using and transportation:

  • It could reduce the variety of accidents with the aid of ninety%, save lives, and reduce the can charge of clinical care and auto restoration.
  • They might reduce emissions with the aid of 60%, which would assist in the battle against climate alternate.
  • They might free drivers to do other issues, reduce stress, and catalyze productiveness.
  • These are only one of the most capabilities advantages of automobiles without a driver. There are others related equivalent to enhancing the information superhighway of things and releasing up house in cities that have massive implications for a way to construct and manage our cities and infrastructure merits.

    The issue for AI

    there is a huge difficulty. This issue is not the best consultant of the hunt for proper AI; it is the crux of the hunt.

    should still the AI be programmed with no driver to all the time comply with traffic legal guidelines? How does a car with out a driver make a decision what to do when there is a decision between saving the adult within the vehicle or saving someone out of the vehicle?

    right here’s a situation: A car and not using a driver transports a passenger across a bridge tons of of toes above the depth of the water.

    unexpectedly, a driver on the approaching song falls asleep and his vehicle enters the incorrect lane, heading straight for the car with no driver at excessive speed. It’s late at nighttime; the passenger within the automobile with out a driver is additionally sound asleep. If the computer woke the passenger and warned him, giving him the option to take control and make a call, it will be too late and there can be a head-on collision.

    What should still the AI do? if you deviate to the wrong lane, you're breaking the law, and the different driver is likely to die as a result of your impulse will send you to the railing. in case you go ahead, kill both people. if you stray from the road to the handrail, a collision could take place and will lead directly during the handrail and kill the passenger.

    logic does not all the time observe to AI automobiles

    in this situation, logical and algorithmic protocols don't seem to observe. Programming an AI to destroy the legislation goes towards an extra programming, which is in keeping with the legislations. if you encode an algorithm to break the legislation and unlock it with algorithms that recognize the legislation, there's a opportunity that the algorithms have interaction abruptly and unintentionally.

    even more: think about that there's a human within the driver’s seat, no longer knowing what to do when the motor vehicle methods. The human instinctively steps on the brakes. The coming near driver all at once wakes up and has time to get lower back on track because the uncertainty of the different driver gave him time to make the correction.

    You cannot software synthetic Intelligence to be uncertain, because the algorithms are if-this-then-that; It appears illogical to step on the brakes with a motor vehicle coming toward you, so during this case, the one, doubtful mind of a human is an competencies.

    The IoT factor

    AI cars are naturally related cars, they are part of IoT, which could be the most vital development within the twenty first century. it's estimated that there will be about 50 to 70 billion IoT gadgets in operation via 2020.

    here’s the query: If automobiles without a driver might communicate with all the different vehicles on the highway, if all vehicles had AI (even though they had been not without a driver), it will seem to be that there could be no issue. One automobile could inform a different to cancel a dozing driver and prevent.

    besides the fact that children, IoT is at risk of attack. cars can also be mass-armed. but what if the cars had real generalized artificial Intelligence? A car might have whatever like an immune equipment. He would recognize when he is attacking and initiate protection protocols.

    AI Will Win once It Solves the Paradox

    using gifts the AI with an issue, that if it might probably discover the solution, it should be smarter than the humans. Let’s say that for the car, it's at all times logical to make the correct determination. So the right choice is logical. when you are AI, how do you are making an illogical determination that's appropriate, and therefore, logical?

    outstanding paintings like the introduction of Adam has nothing to do with good judgment; it isn't logical to consider that a being who defies the legal guidelines of physics has grew to become nothing into something and created individuals who are sure via the laws of physics. however, the abstract and metaphorical pondering fundamental for faith and artwork has created a language system that has allowed people to thrive, so this form of thinking has its own logic.

    We may additionally on no account be capable of make AI feel that means, but at the least we're discovering a great deal about ourselves throughout the effort.

    topics:

    cars with out drivers ,ai ,what is true artificial intelligence ,what are driverless cars ,ai vehicles ,iot cars ,iot


    C2020-003 IBM Algo Fundamantals

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    Why and How: Quantum Computing | killexams.com real questions and Pass4sure dumps

    From the November 2018 Issue

    You are likely to have seen all the information you need on the new iPhone, Google Pixel 3, Surface Pro 6, and Office 2019 as well as the outcomes of the CCH, Thomson and Intuit conferences, although if the reviews are not factual enough for me, you’ll see me pause this Emerging Technology series to identify the facts that matter for the practice of accounting. While Quantum Computing is not ready for prime time, there is plenty of excitement among developers and the major technology providers including, but not limited to IBM, Google, Microsoft, Intel, and some other significant players that you probably won’t recognize like D-Wave and Rigetti Forest.

    Let me be clear without being hyperbolic, if the developers are successful, Quantum Computing will change the future and direction of computing for many generations including long after I’m dead and gone. As a trained computer scientist, my expectation and that of protagonists say that Quantum Computing will be able to break all encryption in use today, can tamper with most blockchain implementations, can outperform any current computing hardware (typically known as Von Neumann computing based on the design from the 1940’s used by all computers and smartphones today), and has many operational attributes of interest that improve centralized, cloud computing. Antagonists claim that Quantum Computing capabilities are overstated, that the environment is too difficult to use, and that the technical difficulties will not be overcome. Let me back up my hair-brained statements with some observations.

  • Quantum Computing is in the early stages. Intel CES 2018 announcements:
  • Intel Labs has developed a neuromorphic research chip, code-named “Loihi”, which is a Quantum Computing enabled artificial intelligence (AI) algorithm inspired by how the brain works. This could unlock exponential gains in performance and power efficiency for the future of AI.
  • Intel’s Mike Mayberry, corporate vice president and managing director of Intel Labs said, “We expect it will be five to seven years before the industry gets to tackling engineering-scale problems, and it will likely require 1 million or more qubits to achieve commercial relevance.”
  • As Quantum Computing matures over the next 10 years, it could threaten blockchain and most cryptography that secures data and digital signatures
  • Let’s consider encryption. Quantum Computing approaches break encryption.

  • Shor's algorithm can easily find public key integer factors. This ability would allow a quantum computer to decrypt many of the cryptographic systems in use today.
  • Consider a problem that has these four properties:
  • The only way to solve it is to guess answers repeatedly and check them,
  • The number of possible answers to check is the same as the number of inputs,
  • Every possible answer takes the same amount of time to check, and
  • There are no clues about which answers might be better: generating possibilities randomly is just as good as checking them in some special order
  • For problems with all four properties, the time for a quantum computer to solve this will be proportional to the square root of the number of inputs. Triple DES and AES become easy targets, which are the most popular encryption algorithms today.
  • This stuff gets a little heady, and I certainly wouldn’t want you to think I have mastered all the principles. Like all the emerging technologies we have covered in these emerging technology columns, Quantum Computing has pros and cons.

    On the positive side:

  • Computing using quantum-mechanical phenomena, such as superposition and entanglement which permits representing much more data in a single position, called a qubit
  • Google says 2018 and IBM says within five years they will achieve quantum supremacy, that is running faster than a classical computer
  • Just 50 qubits can represent 10,000,000,000,000,000 numbers. A classical computer would require a petabyte-scale memory to store that number
  • On the down side:

  • It is hard currently to scale physically to increase the number of qubits
  • Qubits can be initialized to arbitrary values and read easily
  • Quantum gates need faster decoherence time, currently optimized by cryogenic temperatures, 20 Millikelvins = - 459 Fahrenheit
  • While the current prototype hardware is working, the number of qubits in use are quite small and the current designs require elaborate computing. Note from this picture how IBM is cooling a 56-Qubit design:

    Why

    Limits are being approached in the current Von Neumann architectures produced by Intel, AMD and others around the world. The silicon architectures have been miniaturized to 7 nanometers (nm) by TSMC and Intel is currently attempting to transition from 14nm to 10nm. It is becoming more difficult to build processing chips based on these small distances or geometries.

    Further, the tricks that engineers have used including hardware virtualization, multiple cores and threads have made the chips faster, too. However, there is no significant breakthrough in the engineering thinking that will improve performance notably. While I’m hopeful that some smart engineer will come up with a way to make chips faster and smaller there is nothing that is being reported in the engineering trade journals that shows any promise. While I’m sure significant breakthroughs would be kept secret, most of the methods over the last few years have been evolutionary, not revolutionary change. A break through is needed, and Quantum Computing may be that breakthrough.

    What?

    For more information about building Quantum Computing, consider the following references:

  • A classical computer has a memory made up of bits, where each bit is represented by either a one or a zero. A quantum computer maintains a sequence of qubits, read ket 0 and ket 1
  • A single qubit can represent a one, a zero, or any quantum superposition of those two qubit states. A pair of qubits can be in any quantum superposition of 4 states and three qubits in any superposition of 8 states
  • The representations of these states are graphically and mathematically represented by the following:

    In addition, the following links will provide more introduction into the world of Quantum Computing:

    How?

    So how does the Quantum Computing approach work? Physicists and computer scientists look at the properties of atoms and detect or predict the position of various particles mathematically. The table below shows the various approaches being used including: photons, coherent state of light, electrons, Nucleus, Optical lattices, Joseph junctions (super conductors of charge, flux or phase), Singly charged quantum dot pairs, or Quantum dots.

    What does this mean to the practice of accounting and to accountants? Our centralized applications will run on these platforms, currently in use by IBM, Google, NASA, and other large entities. As we mentioned above, we have several working examples and production models available:

    Here’s a summary of what you need to know about Quantum Computing:

    Key Information

    TECHNOLOGY: Quantum Computing

    Why is the new technology better?

    It is a new method of running programs, potentially at higher speeds

    How can you do this today?

    D-Wave ,IBM Q, Google, Intel, Microsoft,

    Rigetti Forest

    Risks

    Computation may not be faster

    Where/when to use

    Problems that have to handle vast amounts of data

    How much?

    Commercial units are $15M+ for 2,000 qubits

    When expected in mainstream

    Ten+ years

    Displaced technology or service

    Traditional computers, mainframes

    Other resources

    Waterloo Quantum 101, How QC Work

    Implementing Quantum Computing can take extremely large calculations and complete them more rapidly. Quantum Computing is being used in very complex models today, such as Weather.com by IBM. Developers are working on compilers and algorithms to make these machines work properly. From my view, Quantum Computers are at about the same stage that mainframe computers were in 1963-1965 when mainframes (which are Von Neumann machines) were just getting the early compilers and applications were simple, but just starting to work. The difference between mainframes and Quantum Computers is that once building the hardware is practical and the compilers are working properly, the additional speed available will revolutionize Cloud computing providing the horsepower to run applications radically faster. Conversion to Quantum Computing should predominantly mean recompiling applications to take advantage of the new platform.

    Recommended Next Steps

    Quantum Computing is in its very early stages. This technology could prove to be a dead end, but all indications are that we are beyond that stage. The biggest issue is that hardware gates need to be designed that allow a faster decoherence time that operate at room temperature rather than in cryogenics. We’ve tried to give you enough background that you can understand some of the fundamentals of Quantum Computing. While there is no practical choice for most of you today because of the high cost and special environment required by Quantum Computers, you should watch for breakthroughs over the next 5-10 years which will enable your applications to run faster than is possible today.


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    No result found, try new keyword!Watson, IBM's most famous creation for Artificial Intelligence and machine learning, is now at the forefront of cognitive cybersecurity. The company's unique of approach to creating a self-learning en...

    Microsoft debuts free quantum computer programming katas | killexams.com real questions and Pass4sure dumps

    Microsoft yesterday announced the release of Quantum Katas, a self-paced programming project that teaches developers how to write code for quantum computers.

    Quantum Katas, as the name implies, are coding katas from Microsoft that teach beginning developers the fundamentals of the company’s quantum computer programming language called Q#. The best part about the exercises: they’re free and open source.

    According to a Microsoft blog post:

    Each kata offers a sequence of tasks on a certain quantum computing topic, progressing from simple to challenging. Each task requires you to fill in some code; the first task might require just one line, and the last one might require a sizable fragment of code. A testing framework validates your solutions, providing real-time feedback.

    There are four different katas, each addressing a different facet of quantum computing and Q# code.

  • Basic gates: an exercise in how gates control quantum logic
  • Deutsch-Jozsa algorithm: run an actual quantum algorithm
  • Measurements: learn how to interpret quantum measuresments
  • Superposition: take your basic understanding of qubits to the next level
  • The quantum computing industry is expected to be worth $15 billion in a decade, according to analysts. That’s more than Hollywood, and it means quantum computer programmers will likely be in high demand.

    Even if you aren’t planning on starting, or switching to, a career in the field, there’s plenty of opportunities for hobbyists and code geeks to dabble in quantum computing.

    IBM’s Q platform allows anyone to interact with quantum computers by providing cloud-based access to one. And Google recently announced Cirq, an open-source project that will allow quantum developers to work together solving some of the field’s biggest challenges.

    If you’re interested in running Microsoft’s Quantum Katas, you’ll need to install the quantum development kit. Once you’ve muddled through that, you can get to the katas on GitHub.

    Read next: IBM’s Hello Quantum is a devilishly difficult puzzle game about qubits

    Read next: Not even shirtless Jesus gets a pass on Facebook's anti-nudity policy



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    IBM C2020-003 Exam (IBM Algo Fundamantals) Detailed Information



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