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In recent years, some big tech companies like IBM, Microsoft, Intel, or Google have been working in relative silence on something that sounds great: quantum computing. The main problem with this is that it is difficult to know what exactly it is and what it can be useful for.

There are some questions that can be easily solved. For example, quantum computing is not going to help you have more FPS on your graphics card at the moment. Nor will it be as easy as changing the CPU of your computer for a quantum to make it hyperfast. Quantum computing is fundamentally different from the computing we are used to, but how?

At the beginning of the 20th century, Planck and Einstein proposed that light is not a continuous wave (like the waves in a pond) but that it is divided into small packages or quanta. This apparently simple idea served to solve a problem called the ultraviolet catastrophe. But over the years other physicists developed it and came to surprising conclusions about the matter, of which we will be interested in two: the superposition of states and entanglement.

To understand why we are interested, lets take a short break and think about how a classic computer works. The basic unit of information is the bit, which can have two possible states (1 or 0) and with which we can perform various logical operations (AND, NOT, OR). Putting together n bits we can represent numbers and operate on those numbers, but with limitations: we can only represent up to 2 different states, and if we want to change x bits we have to perform at least x operations on them: there is no way to magically change them without touching them.

Well, superposition and entanglement allow us to reduce these limitations: with superposition, we can store many more than just 2 ^ n states with n quantum bits (qubits), and entanglement maintains certain relations between qubits in such a way that the operations in one qubit they forcefully affect the rest.

Overlapping, while looking like a blessing at first glance, is also a problem. As Alexander Holevo showed in 1973, even though we have many more states than we can save in n qubits, in practice we can only read 2 ^ n different ones. As we saw in an article in Genbeta about the foundations of quantum computing: a qubit is not only worth 1 or 0 as a normal bit, but it can be 1 in 80% and 0 in 20%. The problem is that when we read it we can only obtain either 1 or 0, and the probabilities that each value had of leaving are lost because when we measured it we modified it.

This discrepancy between the information kept by the qubits and what we can read led Benioff and Feynman to demonstrate that a classical computer would not be able to simulate a quantum system without a disproportionate amount of resources, and to propose models for a quantum computer that did. was able to do that simulation.

Those quantum computers would probably be nothing more than a scientific curiosity without the second concept, entanglement, which allows two quite relevant algorithms to be developed: quantum tempering in 1989 and Shors algorithm in 1994. The first allows finding minimum values of functions, which So said, it does not sound very interesting but it has applications in artificial intelligence and machine learning, as we discussed in another article. For example, if we manage to code the error rate of a neural network as a function to which we can apply quantum quenching, that minimum value will tell us how to configure the neural network to be as efficient as possible.

The second algorithm, the Shor algorithm, helps us to decompose a number into its prime factors much more efficiently than we can achieve on a normal computer. So said, again, it doesnt sound at all interesting. But if I tell you that RSA, one of the most used algorithms to protect and encrypt data on the Internet, is based on the fact that factoring numbers are exponentially slow (adding a bit to the key implies doubling the time it takes to do an attack by force) then the thing changes. A quantum computer with enough qubits would render many encryption systems completely obsolete.

Until now, quantum computing is a field that hasnt been applied much in the real world. To give us an idea, with the twenty qubits of the commercial quantum computer announced by IBM, we could apply Shors factorization algorithm only to numbers less than 1048576, which as you can imagine is not very impressive.

Still, the field has a promising evolution. In 1998 the first ord quantum drive (only two qubits, and needed a nuclear magnetic resonance machine to solve a toy problem (the so-called Deutsch-Jozsa problem). In 2001 Shors algorithm was run for the first time. Only 6 years later, in 2007, D-Wave presented its first computer capable of executing quantum quenching with 16 qubits. This year, the same company announced a 2000 qubit quantum quenching computer. On the other hand, the new IBM computers, although with fewer qubits, they are able to implement generic algorithms and not only that of quantum quenching. In short, it seems that the push is strong and that quantum computing will be increasingly applicable to real problems.

What can those applications be? As we mentioned before, the quantum tempering algorithm is very appropriate for machine learning problems, which makes the computers that implement it extremely useful, although the only thing they can do is run that single algorithm. If systems can be developed that, for example, are capable of transcribing conversations or identifying objects in images and can be translated to train them in quantum computers, the results could be orders of magnitude better than those that already exist. The same algorithm could also be used to find solutions to problems in medicine or chemistry, such as finding the optimal treatment methods for a patient or studying the possible structures of complex molecules.

Generic quantum computers, which have fewer qubits right now, could run more algorithms. For example, they could be used to break much of the crypto used right now as we discussed earlier (which explains why the NSA wanted to have a quantum computer). They would also serve as super-fast search engines if Grovers search algorithm can be implemented, and for physics and chemistry, they can be very useful as efficient simulators of quantum systems.

Unfortunately, algorithms and codes for classic computers couldnt be used on quantum computers and magically get an improvement in speed: you need to develop a quantum algorithm (not a trivial thing) and implement it in order to get that improvement. That, at first, greatly restricts the applications of quantum computers and will be a problem to overcome when those systems are more developed.

However, the main problem facing quantum computing is building computers. Compared to a normal computer, a quantum computer is an extremely complex machine: they operate at a temperature close to absolute zero (-273 C), the qubits support are superconducting and the components to be able to read and manipulate the qubits are not simple either.

What can a non-quantum quantum computer be like? As we have explained before, the two relevant concepts of a quantum computer are superposition and entanglement, and without them, there cannot be the speed improvements that quantum algorithms promise. If computer disturbances modify overlapping qubits and bring them to classical states quickly, or if they break the interweaving between several qubits, what we have is not a quantum computer but only an extremely expensive computer that only serves to run a handful of algorithms. equivalent to a normal computer (and will probably give erroneous results).

Of the two properties, entanglement is the most difficult to maintain and prove to exist. The more qubits there are, the easier it is for one of them to deinterlace (which explains why increasing the number of qubits is not a trivial task). And it is not enough to build the computer and see that correct results come out to say that there are intertwined qubits: looking for evidence of entanglement is a task in itself and in fact, the lack of evidence was one of the main criticisms of D-systems. Wave in its beginnings.

A priori and with the materials that quantum computers are being built with, it does not seem that miniaturization is too feasible. But there is already research on new materials that could be used to create more accessible quantum computers. Who knows if fifty years from now we will be able to buy quantum CPUs to improve the speed of our computers.

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Will Quantum Computing Really Change The World? Facts And Myths - Analytics India Magazine

We will know when someone opens his office door

John Martinis, who had established Googles quantum hardware group in 2014, has cleaned out his office, put the cats out and left the building.

Martinis says a few months after he got Googles now legendary quantum computing experiment to go he was reassigned from a leadership position to an advisory one.

Martinis told Wired that the change led to disagreements with Hartmut Neven, the longtime leader of Googles quantum project.

Martinis said he had to go because his professional goal is for someone to build a quantum computer.

Google has not disputed this account, and says that the company is grateful for Martinis contributions and that Neven continues to head the companys quantum project.

Martinis retains his position as a professor at the UC Santa Barbara, which he held throughout his tenure at Google, and says he will continue to work on quantum computing.

To be fair, Googles quantum computing project was founded by Neven, who pioneered Googles image search technology, and got enough cats together.

The project took on greater scale and ambition when Martinis joined in 2014 to establish Googles quantum hardware lab in Santa Barbara, bringing along several members of his university research group. His nearby lab at UC Santa Barbara had produced some of the most prominent work in the field over the past 20 years, helping to demonstrate the potential of using superconducting circuits to build qubits, the building blocks of quantum computers.

Googles ground-breaking supremacy experiment used 53 qubits working together. They took minutes to crunch through a carefully chosen math problem the company calculated would take a supercomputer 10,000 years to work out. It still does not have a practical use, and the cats were said to be bored with the whole thing.

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Google's top quantum computing brain may or may not have quit - Fudzilla

BOULDER, Colo., April 22, 2020 The Rocky Mountain Advanced Computing Consortium (RMACC) will hold its 10thannual High Performance Computing Symposium as a multi-track on-line version on May 20-21.Registration for the event will be free to all who would like to attend.

The on-line Symposium will include presentations by two keynote speakers and a full slate of tutorial sessions.Another longtime Symposium tradition a poster competition for students to showcase their own research also will be continued. Competition winners will receive an all-expenses paid trip to SC20 in Atlanta.

Major sponsor support is being provided by Intel, Dell and HPE with additional support from ARM, IBM, Lenovo and Silicon Mechanics.

Links to the Symposium registration, its schedule, and how to enter the poster competition can be found atwww.rmacc.org/hpcsymposium.

The Keynote speakers areDr.Nick Bronn, a Research Staff Member in IBMs Experimental Quantum Computing group, andDr. Jason Dexter, a working group coordinator for the groundbreaking black hole imaging studies published by Event Horizon Telescope.

Dr. Bronn serves at IBMs TJ Watson Research Center in Yorktown Heights, NY.He has been responsible for qubit (quantum bits) device design, packaging, and cryogenic measurement, working towards scaling up larger numbers of qubits on a device and integration with novel implementations of microwave and cryogenic hardware.He will speak on the topic,Benchmarking and Enabling Noisy Near-term Quantum Hardware.

Dr.Dexter is a member of the astrophysical and planetary sciences faculty at the University of Colorado Boulder.He will speak on the role of high performance computing in understanding what we see in the first image of a black hole.Dr. Dexter is a member of both the Event Horizon Telescope and VLTI/GRAVITY collaborations, which can now image black holes.

Their appearances along with the many tutorial sessions continue the RMACCs annual tradition of showcasing cutting-edge HPC achievements in both education and industry.

The largest consortium of its kind, the RMACC is a collaboration among 30 academic and government research institutions in Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Washington and Wyoming. The consortiums mission is to facilitate widespread effective use of high performance computing throughout the 9-state intermountain region.

More about the RMACC and its mission can be found at the website:www.rmacc.org.

About RMACC

Primarily a volunteer organization, the RMACC is collaboration among 30 academic and research institutions located in Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Washington and Wyoming.The RMACCs mission is to facilitate widespread effective use of high performance computing throughout this 9-state intermountain region.

Source: RMACC

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RMACC's 10th High Performance Computing Symposium to Be Held Free Online - HPCwire

Understanding advanced encryption standard on basic level doesnt require a higher degree in computer science or Matrix-level consciousness lets break AES encryption down into laymans terms

Hey, all. We know of security of information to be a hot topic since, well, forever. We entrust our personal and sensitive information to lots of major entities and still have problems with data breaches, data leaks, etc. Some of this happens because of security protocols in networking, or bad practices of authentication management but, really, there are many ways that data breaches can occur. However, the actual process of decrypting a ciphertext without a key is far more difficult. For that, we can thank the encrypting algorithms like the popular advanced encryption standard and the secure keys that scramble our data into indecipherable gibberish.

Lets look into how AES works and different applications for it. Well be getting a little into some Matrix-based math so, grab your red pills and see how far this rabbit hole goes.

Lets hash it out.

You may have heard of advanced encryption standard, or AES for short but may not know the answer to the question what is AES? Here are four things you need to know about AES:

The National Institute of Standards and Technology (NIST) established AES as an encryption standard nearly 20 years ago to replace the aging data encryption standard (DES). After all, AES encryption keys can go up to 256 bits, whereas DES stopped at just 56 bits. NIST could have chosen a cipher that offered greater security, but the tradeoff would have required greater overhead that wouldnt be practical. So, they went with one that had great all-around performance and security.

AESs results are so successful that many entities and agencies have approved it and utilize it for encrypting sensitive information. The National Security Agency (NSA), as well as other governmental bodies, utilize AES encryption and keys to protect classified or other sensitive information. Furthermore, AES is often included in commercial based products, including but limited to:

Although it wouldnt literally take forever, it would take far longer than any of our lifetimes to crack an AES 256-bit encryption key using modern computing technology. This is from a brute force standpoint, as in trying every combination until we hear the click/unlocking sound. Certain protections are put in place to prevent stuff from like this happening quickly, such as a limit on password attempts before a lockdown, which may or may not include a time lapse, to occur before trying again. When we are dealing with computation in milliseconds, waiting 20 minutes to try another five times would seriously add to the time taken to crack a key.

Just how long would it take? We are venturing into a thousand monkeys working on a thousand typewriters to write A Tale of Two Cities territory. The possible combinations for AES 256-bit encryption is 2256. Even if a computer can do multiple quadrillions of instructions per second, then we are still in that eagles-wings-eroding-Mount-Everest time frame.

Needless to say, its waaaaaaaaaaaaaaaaaaay (theres not enough memory on our computers to support the number of a letters that I want to convey) longer than our current universe has been in existence. And thats just for a 16-byte block of data. So, as you can see, brute forcing AES even if it is 128 bits AES is futile.

That would likely change, though, once quantum computing becomes a little more mainstream, available, and effective. Quantum computing is expected to break AES encryption and require other methods to protect our data but thats still a ways down the road.

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To better understand what AES is, you need to understand how it works. But in order to see how the advanced encryption standard actually works, however, we first need to look at how this is set up and the rules concerning the process based on the users selection of encryption strength. Typically, when we discuss using higher bit levels of security, were looking at things that are more secure and more difficult to break or hack. While the data blocks are broken up into 128 bits, the key size have a few varying lengths: 128 bits, 196 bits, and 256 bits. What does this mean? Lets back it up for a second here.

We know that encryption typically deals in the scrambling of information into something unreadable and an associated key to decrypt the scramble. AES scramble procedures use four scrambling operations in rounds, meaning that it will perform the operations, and then repeat the process based off of the previous rounds results X number of times. Simplistically, if we put in X and get out Y, that would be one round. We would then put Y through the paces and get out Z for round 2. Rinse and repeat until we have completed the specified number of rounds.

The AES key size, specified above, will determine the number of rounds that the procedure will execute. For example:

As mentioned, each round has four operations.

So, youve arrived this far. Now, you may be asking: why, oh why, didnt I take the blue pill?

Before we get to the operational parts of advanced encryption standard, lets look at how the data is structured. What we mean is that the data that the operations are performed upon is not left-to-right sequential as we normally think of it. Its stacked in a 44 matrix of 128 bits (16 bytes) per block in an array thats known as a state. A state looks something like this:

So, if your message was blue pill or red, it would look something like this:

So, just to be clear, this is just a 16-byte block so, this means that every group of 16 bytes in a file are arranged in such a fashion. At this point, the systematic scramble begins through the application of each AES encryption operation.

As mentioned earlier, once we have our data arrangement, there are certain linked operations that will perform the scramble on each state. The purpose here is to convert the plaintext data into ciphertext through the use of a secret key.

The four types of AES operations as follows (note: well get into the order of the operations in the next section):

As mentioned earlier, the key size determines the number of rounds of scrambling that will be performed. AES encryption uses the Rjindael Key Schedule, which derives the subkeys from the main key to perform the Key Expansion.

The AddRoundKey operation takes the current state of the data and executes the XOR Boolean operation against the current round subkey. XOR means Exclusively Or, which will yield a result of true if the inputs differ (e.g. one input must be 1 and the other input must be 0 to be true). There will be a unique subkey per round, plus one more (which will run at the end).

The SubBytes operation, which stands for substitute bytes, will take the 16-byte block and run it through an S-Box (substitution box) to produce an alternate value. Simply put, the operation will take a value and then replace it by spitting out another value.

The actual S-Box operation is a complicated process, but just know that its nearly impossible to decipher with conventional computing. Coupled with the rest of AES operations, it will do its job to effectively scramble and obfuscate the source data. The S in the white box in the image above represents the complex lookup table for the S-Box.

The ShiftRows operation is a little more straightforward and is easier to understand. Based off the arrangement of the data, the idea of ShiftRows is to move the positions of the data in their respective rows with wrapping. Remember, the data is arranged in a stacked arrangement and not left to right like most of us are used to reading. The image provided helps to visualize this operation.

The first row goes unchanged. The second row shifts the bytes to the left by one position with row wrap around. The third row shifts the bytes one position beyond that, moving the byte to the left by a total of two positions with row wrap around. Likewise, this means that the fourth row shifts the bytes to the left by a total of three positions with row wrap around.

The MixColumns operation, in a nutshell, is a linear transformation of the columns of the dataset. It uses matrix multiplication and bitwise XOR addition to output the results. The column data, which can be represented as a 41 matrix, will be multiplied against a 44 matrix in a format called the Gallois field, and set as an inverse of input and output. That will look something like the following:

As you can see, there are four bytes in that are ran against a 44 matrix. In this case, matrix multiplication has each input byte affecting each output byte and, obviously, yields the same size.

Now that we have a decent understanding of the different operations utilized to scramble our data via AES encryption, we can look at the order in which these operations execute. It will be as such:

Note: The MixColumns operation is not in the final round. Without getting into the actual math of this, theres no additional benefit to performing this operation. In fact, doing so would simply make the decryption process a bit more taxing in terms of overhead.

If we consider the number of rounds and the operations per round that are involved, by the end of it, you should have a nice scrambled block. And that is only a 16-byte block. Consider how much information that equates to in the big picture. Its miniscule when compared to todays file/packet sizes! So, if each 16-byte block has seemingly no discernable pattern at least, any pattern that can be deciphered in a timely manner Id say AES has done its job.

We know the advanced encryption standard algorithm itself is quite effective, but its level of effectiveness depends on how its implemented. Unlike the brute force attacks mentioned above, effective attacks are typically launched on the implementation and not on the algorithm itself. This can be equated to attacking users as in phishing attacks versus attacking the technology behind the service/function that may be hard to breach. These can be considered side-channel attacks where the attacks are being carried out on other aspects of the entire process and not the focal point of the security implementation.

While I always advocate going with a reasonable/effective security option, a lot of AES encryption is happening without you even knowing it. Its locking down spots of the computing world that would otherwise be wide open. In other words, there would be many more opportunities for hackers to capture data if advanced encryption standard wasnt implemented at all. We just need to know how to identify the open holes and figure out how to plug them. Some may be able to use AES and others may need another protocol or process.

Appreciate the encryption implementations we have, use the best ones when needed, and happy scrutinizing!

Link:
Advanced Encryption Standard (AES): What It Is and How It Works - Hashed Out by The SSL Store - Hashed Out by The SSL Store

the Russian developer of electronics and robotics Khamster robotics, introduced the first domestic mini PC processor Baikal and operating system Alt Linux. On the one hand, this event aroused considerable interest from technical experts, and other government agencies to whom it is daunting task to switch to Russian software. However, even back in 2010, Steve jobs made the revolutionary statement that the personal computer is dead. Then it was supported by many authoritative publications and experts in the field of IT, in a voice announced the death of the PC. Indeed, according to Gartner, this market segment is rapidly declining for the seventh consecutive year. Compared to 2011 sales volume of desktop computers fell by 30%, reaching in 2018 only 259 million units a figure that is comparable to the distant 2006. Users increasingly demand the combination of functionality, design and performance. So, according to experts, against the background of General decline in sales of devices only 2-in-1 ultra-thin laptops will show a positive growth in the near future.

However, you cannot deny that over the last decade the personal computer has evolved greatly. Laptops, tablets and smartphones have superseded it as the base unit for work, casual gaming and web surfing, but a desktop computer is indispensable to perform specialized functions. Thanks to more powerful processor and options for customization of the PC is better suited to working with graphics, run heavy games out there, carrying out complex calculations and other functions workstation.

Breakthrough in quantum technologies has proved once again that the computer isnt going to give up and completely give in to their mobile counterparts. Google quantum processor, presented in September of last year, just three and a half minutes to cope with the task, which have the most advanced supercomputer would have left about ten thousand years. About what this means for the average consumer and in what direction will develop the technology, said the experts.

Quantum computer, no matter what skeptics already exists. Albeit this is not the most productive computing device, but the quantum superiority was demonstrated. Google created a superconducting quantum simulator for the selected algorithm showed much better performance than the most powerful classical computer. Experimental research on the creation of a quantum computer is conducted in many research centers around the world. Huge financial resources invested in this area, and basic prohibitions on creating twhat kind of cars not only in achieving a certain technological level, said General Director of concern Automatics of the state Corporation rostec Vladimir Kabanov.

the Term quantum advantage is the ability of quantum computing to solve problems inaccessible to classical computers, regardless of use and practical applicability of these results. To perform its calculations, a quantum computer uses complicated phenomena of quantum mechanics quantum entanglement and superposition, explained the head of the laboratory of cryptography JSC SPC Kryptonite (included in X holding) Vasily Shishkin.

the Potential superiority of quantum computer over classical is that the quantum computer operates not ordinary bats, and quantum qubits. Unlike bits, which in each moment can only be in one of two States 0 or 1, qubits take both these values with a certain probability. This phenomenon is called quantum superposition, said Shishkin. Due to their characteristics, the qubits can carry much more information, which drastically increases the computational power of a quantum computer.

However, the expert said, the development of such devices raises a number of practical difficulties: as soon As the user reads the value of a qubit, it loses its quantum properties and turns into an ordinary bit with one constant value. Therefore, the input data is written in the form of a system of qubits, and the calculation without measuring their values. Once the values of the qubits are read, calculations stop.

in addition, when you create a quantum computer it is necessary to consider the phenomenon of quantum entanglement. This means that the qubits should be in a dependent state. For example, if the measurement of one qubit we get the value 1, then the result of the measurement of all associated qubit will give 0. The main current technological problem is that systems of coupled qubits is very unstable and quickly lead to errors. And the more qubits, the shorter the period of stable operation, said the expert.

This feature explains why in the existing quantum computers are so few qubits. However, each year this number is increasing: for example, in the first quantum computer, tested IBM in 2001, there were only seven qubits, and submitted to Google in 2019 quantum processor Sycamore 53.

Indeed, the last three years there is double the annual increase in the number of qubits in quantum computers, so the technology has great promise, says Sergey Chirkin, Dean of the faculties of artificial intelligence and Big Data Analytics GeekUniversity, educational portal GeekBrains: In quantum computing is expected to at least annual small breakthroughs, which ultimately should lead to the fact that a significant part of the computation for artificial intelligence will be run on quantum computers. This can happen in the next ten years, provided that investment in research will grow and will increase the number of developers of quantum computers. Technology of this level will usually be available everywhere: access to a quantum computer, you can get as part of the cloud service.

Technology, a quantum computer will require improvement, says the co-founder of the company Crown, doctor of physical Sciences Ivan Atkin. Separate scientific breakthroughs happen regularly, but this is the beginning of a journey the scientists just realized that this is possible. In the next five years this quantum breakthrough is definitely not happening, the first success will be only in ten years. Thus, the technology of artificial intelligence was established more than 50 years ago, but the real use began recently. While the AI is much more simple technology than a quantum computer, predicts scientist.

Evolution of computer demonstration: breakthrough for its time, a development first introduced in specialized areas and then you get a wide domestic distribution. So, the first prototypes of the computer, which appeared in 1940-ies, were used exclusively for military and scientific purposes. They occupied entire rooms, weighed tens of tons and could carry up to several thousand operations per second. The era of the personal computer began only in 1980-ies due to the Mac Steve jobs. The device was worth two and a half thousand dollars, weighed a little less than ten pounds and could be operated even by a child. By the time jobs announced the death of the PC in 2010, it became an integral part of the lives of most people. Probably eventually the same thing will happen with a quantum computer. However, according to experts, the ordinary consumer should not expect personal quantum devices at least the next ten years.

in the meantime, all work in this area are of exploratory character and the results needed in the first place scientific and technological community, says the rector of Innopolis University, Advisor of the Russian quantum center Alexander Tormasov. The industry is just beginning to be interested in quantum technologies. The Russian quantum center, we are discussing the creation of operating systems for work with quantum computers. Also in the next ten years the market can reach the quantum devices to measure time with high accuracy. This will be the impetus for further technological development, the as we will be able to receive a satellite positioning system, which will increase the scale of recognition with 100 meters to 30 centimeters. Then we from space to see the steps of the person, concluded the expert.

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The qubit schrdinger will replace if a quantum computer traditional PCs - The KXAN 36 News