Category Archives: Quantum Computing

Quantum Computing Market Overview

The Quantum Computing market report presents a detailed evaluation of the market. The report focuses on providing a holistic overview with a forecast period of the report extending from 2018 to 2026. The Quantum Computing market report includes analysis in terms of both quantitative and qualitative data, taking into factors such as Product pricing, Product penetration, Country GDP, movement of parent market & child markets, End application industries, etc. The report is defined by bifurcating various parts of the market into segments which provide an understanding of different aspects of the market.

The overall report is divided into the following primary sections: segments, market outlook, competitive landscape and company profiles. The segments cover various aspects of the market, from the trends that are affecting the market to major market players, in turn providing a well-rounded assessment of the market. In terms of the market outlook section, the report provides a study of the major market dynamics that are playing a substantial role in the market. The market outlook section is further categorized into sections; drivers, restraints, opportunities and challenges. The drivers and restraints cover the internal factors of the market whereas opportunities and challenges are the external factors that are affecting the market. The market outlook section also comprises Porters Five Forces analysis (which explains buyers bargaining power, suppliers bargaining power, threat of new entrants, threat of substitutes, and degree of competition in the Quantum Computing) in addition to the market dynamics.

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Leading Quantum Computing manufacturers/companies operating at both regional and global levels:

Quantum Computing Market Scope Of The Report

This report offers past, present as well as future analysis and estimates for the Quantum Computing market. The market estimates that are provided in the report are calculated through an exhaustive research methodology. The research methodology that is adopted involves multiple channels of research, chiefly primary interviews, secondary research and subject matter expert advice. The market estimates are calculated on the basis of the degree of impact of the current market dynamics along with various economic, social and political factors on the Quantum Computing market. Both positive as well as negative changes to the market are taken into consideration for the market estimates.

Quantum Computing Market Competitive Landscape & Company Profiles

The competitive landscape and company profile chapters of the market report are dedicated to the major players in the Quantum Computing market. An evaluation of these market players through their product benchmarking, key developments and financial statements sheds a light into the overall market evaluation. The company profile section also includes a SWOT analysis (top three companies) of these players. In addition, the companies that are provided in this section can be customized according to the clients requirements.

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Quantum Computing Market Research Methodology

The research methodology adopted for the analysis of the market involves the consolidation of various research considerations such as subject matter expert advice, primary and secondary research. Primary research involves the extraction of information through various aspects such as numerous telephonic interviews, industry experts, questionnaires and in some cases face-to-face interactions. Primary interviews are usually carried out on a continuous basis with industry experts in order to acquire a topical understanding of the market as well as to be able to substantiate the existing analysis of the data.

Subject matter expertise involves the validation of the key research findings that were attained from primary and secondary research. The subject matter experts that are consulted have extensive experience in the market research industry and the specific requirements of the clients are reviewed by the experts to check for completion of the market study. Secondary research used for the Quantum Computing market report includes sources such as press releases, company annual reports, and research papers that are related to the industry. Other sources can include government websites, industry magazines and associations for gathering more meticulous data. These multiple channels of research help to find as well as substantiate research findings.

Table of Content

1 Introduction of Quantum Computing Market

1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology of Verified Market Research

3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Quantum Computing Market Outlook

4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 Quantum Computing Market, By Deployment Model

5.1 Overview

6 Quantum Computing Market, By Solution

6.1 Overview

7 Quantum Computing Market, By Vertical

7.1 Overview

8 Quantum Computing Market, By Geography

8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Quantum Computing Market Competitive Landscape

9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix

11.1 Related Research

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Verified Market Research is a leading Global Research and Consulting firm servicing over 5000+ customers. Verified Market Research provides advanced analytical research solutions while offering information enriched research studies. We offer insight into strategic and growth analyses, Data necessary to achieve corporate goals and critical revenue decisions.

Our 250 Analysts and SMEs offer a high level of expertise in data collection and governance use industrial techniques to collect and analyse data on more than 15,000 high impact and niche markets. Our analysts are trained to combine modern data collection techniques, superior research methodology, expertise and years of collective experience to produce informative and accurate research.

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Quantum Computing Market Growth Trends, Key Players, Competitive Strategies and Forecasts to 2026 - Jewish Life News

IQM Finland Oy (IQM), a European startup which makes hardware for quantum computers, has raised a 15M equity investment round from the EIC Accelerator program for the development of quantum computers. This is in addition to a raise of 3.3M from the Business Finland government agency. This takes the companys funding to over 32M. The company previously raised a 11.4M seed round.

IQM has hired a lot of engineers in its short life, and now says it plans to hire one quantum engineer per week on the pathway to commercializing its technology through the collaborative design of quantum-computing hardware and applications.

Dr. Jan Goetz, CEO and co-founder of IQM said: Quantum computers will be funded by European governments, supporting IQM s expansion strategy to build quantum computers in Germany, in a statement.

The news comes as the Finnish government announced only last week that it would acquire a quantum computer with 20.7M for the Finnish State Research center VTT.

It has been a mind-blowing forty-million past week for quantum computers in Finland. IQM staff is excited to work together with VTT, Aalto University, and CSC in this ecosystem, rejoices Prof. Mikko Mttnen, Chief Scientist and co-founder of IQM.

Previously, the German government said it would put 2bn into commissioning at least two quantum computers.

IQM thus now plans to expand its operations in Germany via its team in Munich.

IQM will build co-design quantum computers for commercial applications and install testing facilities for quantum processors, said Prof. Enrique Solano, CEO of IQM Germany.

The company is focusing on superconducting quantum processors, which are streamlined for commercial applications in a Co-Design approach. This works by providing the full hardware stack for a quantum computer, integrating different technologies, and then invites collaborations with quantum software companies.

IQM was one of the 72 to succeed in the selection process of the EIC. Altogether 3969 companies applied for this funding.

See the rest here:
European quantum computing startup takes its funding to 32M with fresh raise - TechCrunch

In March, I explored the enterprise readiness of quantum computing in Quantum computing is right around the corner, but cooling is a problem. What are the options? I also detailed potential industry use cases, from supply chain to banking and finance. But what are the industry giants pursuing?

Recently, I listened to two somewhat different perspectives on quantum computing. One is Googles (public) ten-year plan.

Google plans to search for commercially viable applications in the short term, but they dont think there will be many for another ten years - a time frame I've heard one referred to as bound but loose. What that meant was, no more than ten, maybe sooner. In the industry, the term for the current state of the art is NISQ Noisy, Interim Scale Quantum Computing.

The largest quantum computers are in the 50-70 qubit range, and Google feels NISQ has a ceiling of maybe two hundred. The "noisy" part of NISQ is because the qubits need to interact and be nearby. That generates noise. The more qubits, the more noise, and the more challenging it is to control the noise.

But Google suggests the real unsolved problems in fields like optimization, materials science, chemistry, drug discovery, finance, and electronics will take machines with thousands of qubits and even envision one million on a planar array etched in aluminum. Major problems need solving such noise elimination, coherence, and lifetime (a qubit holds its position in a tiny time slice).

In the meantime, Google is seeking customers to work with them to find applications working with Google researchers. Quantum computing needs algorithms as much as it needs qubits. It requires customers with a strong in-house science team and a commitment of three years. Whatever is discovered will be published as open source.

In summary, Google does not see commercial value in NISQ. They are using NISQ to discover what quantum computing can do that has any commercial capability.

First of all, if you have a picture in your mind of a quantum computer, chances are you are not including an essential element a conventional computer. According toQuantum Computing, Progress, and Prospects:

Although reports in the popular press tend to focus on the development of qubits and the number of qubits in the current prototypical quantum computing chip, any quantum computer requires an integrated hardware approach using significant conventional hardware to enable qubits to be controlled, programmed, and read out.

The author is undoubtedly correct. Most material about quantum computers never mentions this, and it raises quite a few issues that can potentially dilute the gee-whiz aspect. I'd heard this first from Itamar Sivan, Ph.D., CEO, Quantum Machines. He followed with the quip that technically, quantum computers aren't computers. Its that simple. They are not Turing Machines. File this under the category of "You're Not Too Old to Learn Something New.

From (Hindi) Theory of Computation - Turing Machine:

A Turing machine is a mathematical model of computation that defines an abstract machine, which manipulates symbols on a strip of tape according to a table of rules. Despite the model's simplicity, given any computer algorithm, a Turing machine capable of simulating that algorithm's logic can be constructed.

Dr. Sivan clarified this as follows:

Any computer to ever be used, from the early-days computers, to massive HPCs, are all Turing-machines, and are thereforeequivalent to one another. All computers developedand manufactured in the last decades, are all merelybigger and more compact variations of one another. A quantum computer however is not MERELY a more advanced Turing machine, it is a different type of machine, and classical Turing machines are not equivalent to quantum computers as they are equivalent to one another.

Therefore, the complexity of running particular algorithms on quantum computers is different from the complexity of running them on classical machines. Just to make it clear, a quantum computer can be degenerated to behave like a classical computer, but NOT vice-versa.

There is a lot more to this concept, but most computers you've ever seen or heard of are Turing Machines, except Quantum computers. This should come as no surprise because anything about quantum mechanics is weird and counter-intuitive, so why would a quantum computer be any different?

According to Sivan, a quantum computer needs three elements to perform: a quantum computer and an orchestration platform of (conventional) hardware and software. There is no software in a quantum computer. The platform manages the progress of their algorithm through, mostly laser beams pulses. The logic needed to operate the quantum computer resides with and is controlled by the orchestration platform.

The crucial difference in Google's and Quantum Machines' strategy is that Google views the current NISQ state of affairs as a testbed for finding algorithms and applications for future development. At the same time, Sivan and his company produced an orchestration platform to put the current technology in play. Their platform is quantum computer agnostic it can operate with any of them. Sivan feels that focusing solely on the number of qubits is just part of the equation. According to Dr. Sivan:

While today's most advanced quantum computers only have a relatively small number of available qubits (53 for IBM's latest generation and 54 for Google's Sycamore processor), we cannot maximize the potential of even this relatively small count. We are leaving a lot on the table with regards to what we can already accomplish with the computing power we already have. While we should continue to scale up the number of qubits, we also need to focus on maximizing what we already have.

Ive asked a few quantum computer scientists if quantum computers can solve the Halting Problem.In Wikipedia:

The halting problem is determining, from a description of an arbitrarycomputer programand an input, whether the program will finish running, or continue to run forever.Alan Turingproved in 1936 that a generalalgorithmto solve the halting problem for all possible program-input pairs could not exist.

That puts it in a class of problems that are undecidable. Oddly, opinion was split onthequestion, despite Turings Proof. Like Simplico said to Galileo inDialogues Concerning Two New Sciences, If Aristotle had not said otherwise I would have believed it.

There are so many undecidable problems in math that I wondered if some of these might fall out.For example, straight from current AI problems, Planning in aPartially observable Markov decision process is considered undecidable. A million qubits? Maybe not. After all, Dr. Sivan pointed out that toreplicate in a classical processor, the information in just a 300 qubit quantum processor would require more transistors than all of the atoms inthe universe.

I've always believed that action speaks louder than words. While Google is taking the long view, Quantum Machines provides the platform to see how far we can go with current technology. Googles tactics are familiar. Every time you use TensorFlow, it gets better. Every time play with their autonomous car, it gets better. Their collaboration with a dozen or so technically advanced companies makes their quantum technology better.

Continue reading here:
The technical realities of functional quantum computers - is Googles ten-year plan for Quantum Computing viable? - Diginomica

Quantum computers stand a good chance of changing the face computing, and that goes double for encryption. For encryption methods that rely on the fact that brute-forcing the key takes too long with classical computers, quantum computing seems like its logical nemesis.

For instance, the mathematical problem that lies at the heart of RSA and other public-key encryption schemes is factoring a product of two prime numbers. Searching for the right pair using classical methods takes approximately forever, but Shors algorithm can be used on a suitable quantum computer to do the required factorization of integers in almost no time.

When quantum computers become capable enough, the threat to a lot of our encrypted communication is a real one. If one can no longer rely on simply making the brute-forcing of a decryption computationally heavy, all of todays public-key encryption algorithms are essentially useless. This is the doomsday scenario, but how close are we to this actually happening, and what can be done?

To ascertain the real threat, one has to look at the classical encryption algorithms in use today to see which parts of them would be susceptible to being solved by a quantum algorithm in significantly less time than it would take for a classical computer. In particular, we should make the distinction between symmetric and asymmetric encryption.

Symmetric algorithms can be encoded and decoded with the same secret key, and that has to be shared between communication partners through a secure channel. Asymmetric encryption uses a private key for decryption and a public key for encryption onlytwo keys: a private key and a public key. A message encrypted with the public key can only be decrypted with the private key. This enables public-key cryptography: the public key can be shared freely without fear of impersonation because it can only be used to encrypt and not decrypt.

As mentioned earlier, RSA is one cryptosystem which is vulnerable to quantum algorithms, on account of its reliance on integer factorization. RSA is an asymmetric encryption algorithm, involving a public and private key, which creates the so-called RSA problem. This occurs when one tries to perform a private-key operation when only the public key is known, requiring finding the eth roots of an arbitrary number, modulo N. Currently this is unrealistic to classically solve for >1024 bit RSA key sizes.

Here we see again the thing that makes quantum computing so fascinating: the ability to quickly solve non-deterministic polynomial (NP) problems. Whereas some NP problems can be solved quickly by classical computers, they do this by approximating a solution. NP-complete problems are those for which no classical approximation algorithm can be devised. An example of this is the Travelling Salesman Problem (TSP), which asks to determine the shortest possible route between a list of cities, while visiting each city once and returning to the origin city.

Even though TSP can be solved with classical computing for smaller number of cities (tens of thousands), larger numbers require approximation to get within 1%, as solving them would require excessively long running times.

Symmetric encryption algorithms are commonly used for live traffic, with only handshake and the initial establishing of a connection done using (slower) asymmetric encryption as a secure channel for exchanging of the symmetric keys. Although symmetric encryption tends to be faster than asymmetric encryption, it relies on both parties having access to the shared secret, instead of being able to use a public key.

Symmetric encryption is used with forward secrecy (also known as perfect forward secrecy). The idea behind FS being that instead of only relying on the security provided by the initial encrypted channel, one also encrypts the messages before they are being sent. This way even if the keys for the encryption channel got compromised, all an attacker would end up with are more encrypted messages, each encrypted using a different ephemeral key.

FS tends to use Diffie-Hellman key exchange or similar, resulting in a system that is comparable to a One-Time Pad (OTP) type of encryption, that only uses the encryption key once. Using traditional methods, this means that even after obtaining the private key and cracking a single message, one has to spend the same effort on every other message as on that first one in order to read the entire conversation. This is the reason why many secure chat programs like Signal as well as increasingly more HTTPS-enabled servers use FS.

It was already back in 1996 that Lov Grover came up with Grovers algorithm, which allows for a roughly quadratic speed-up as a black box search algorithm. Specifically it finds with high probability the likely input to a black box (like an encryption algorithm) which produced the known output (the encrypted message).

As noted by Daniel J. Bernstein, the creation of quantum computers that can effectively execute Grovers algorithm would necessitate at least the doubling of todays symmetric key lengths. This in addition to breaking RSA, DSA, ECDSA and many other cryptographic systems.

The observant among us may have noticed that despite some spurious marketing claims over the past years, we are rather short on actual quantum computers today. When it comes to quantum computers that have actually made it out of the laboratory and into a commercial setting, we have quantum annealing systems, with D-Wave being a well-known manufacturer of such systems.

Quantum annealing systems can only solve a subset of NP-complete problems, of which the travelling salesman problem, with a discrete search space. It would for example not be possible to run Shors algorithm on a quantum annealing system. Adiabatic quantum computation is closely related to quantum annealing and therefore equally unsuitable for a general-purpose quantum computing system.

This leaves todays quantum computing research thus mostly in the realm of simulations, and classical encryption mostly secure (for now).

When can we expect to see quantum computers that can decrypt every single one of our communications with nary any effort? This is a tricky question. Much of it relies on when we can get a significant number of quantum bits, or qubits, together into something like a quantum circuit model with sufficient error correction to make the results anywhere as reliable as those of classical computers.

At this point in time one could say that we are still trying to figure out what the basic elements of a quantum computer will look like. This has led to the following quantum computing models:

Of these four models, quantum annealing has been implemented and commercialized. The others have seen many physical realizations in laboratory settings, but arent up to scale yet. In many ways it isnt dissimilar to the situation that classical computers found themselves in throughout the 19th and early 20th century when successive computers found themselves moving from mechanical systems to relays and valves, followed by discrete transistors and ultimately (for now) countless transistors integrated into singular chips.

It was the discovery of semiconducting materials and new production processes that allowed classical computers to flourish. For quantum computing the question appears to be mostly a matter of when well manage to do the same there.

Even if in a decade or more from the quantum computing revolution will suddenly make our triple-strength, military-grade encryption look as robust as DES does today, we can always comfort ourselves with the knowledge that along with quantum computing we are also increasingly learning more about quantum cryptography.

In many ways quantum cryptography is even more exciting than classical cryptography, as it can exploit quantum mechanical properties. Best known is quantum key distribution (QKD), which uses the process of quantum communication to establish a shared key between two parties. The fascinating property of QKD is that the mere act of listening in on this communication will cause measurable changes. Essentially this provides unconditional security in distributing symmetric key material, and symmetric encryption is significantly more quantum-resistant.

All of this means that even if the coming decades are likely to bring some form of upheaval that may or may not mean the end of classical computing and cryptography with it, not all is lost. As usual, science and technology with it will progress, and future generations will look back on todays primitive technology with some level of puzzlement.

For now, using TLS 1.3 and any other protocols that support forward secrecy, and symmetric encryption in general, is your best bet.

Read the original:
Quantum Computing And The End Of Encryption - Hackaday

On Tuesday, 2 June, student of the University of Tartu Institute of Computer Science Mykhailo Nitsenko defended his thesis Quantum Circuit Fusion in the Presence of Quantum Noise on NISQ Devices, the first masters thesis defended in the field of quantum computing at the University of Tartu.

In his thesis supervised by Dirk Oliver Theis and Dominique Unruh, Mykhailo Nitsenko studied a concept called circuit fusion, which proposes to reduce stochastic noise in estimating the expectation values of measurements at the end of quantum computations. But near-term quantum computing devices are also subject to quantum noise (such as decoherence etc.), and circuit fusion aggravates that problem.

Mykhailo Nitsenko ran thousands of experiments on IBMs cloud quantum computers and used Fourier analysis techniques to quantify and visualise noise and the resulting information loss.

According to Mykhailo Nitsenko, before he enrolled in the University of Tartu he had a strong opinion that quantum computing is an abstract idea that we will never be able to use or even implement. I just could not imagine how it is even possible to do computations on things without directly observing them. Quantum computing class showed me how it is done, and it became apparent to me that it is something I want to dedicate my academic efforts to, said Nitsenko.

If you dont want to wait for fault-tolerant quantum computers, you may endeavour to use the noisy quantum computing devices that can be built already now. In that case, researching the effects of quantum noise on computations becomes important: these effects must be mitigated, said Dirk Oliver Theis, Associate Professor of Theoretical Computer Science at the University of Tartu Institute of Computer Science. Theis added that he had expected that the mathematics which Mykhailo Nitsenko implemented in his thesis would help us understand some aspects of quantum noise which can be devastating to quantum computations, rendering the result pure gibberish.

In near-term quantum computing, one tries to run quantum circuits which are just short enough so that the correct output can be somehow reconstructed from the distorted measurement results. But quantum noise affects the results of computations on near-term quantum computers in complicated ways. In the mathematical approach based on Fourier analysis that Nitsenko implemented, some effects were predictable, such as a decrease in the amplitudes due to decoherence. What was surprising was that the low frequencies of the quantum noise showed distinct patterns. In future research, this might be exploited to mitigate the effect of quantum noise on the computation, said Theis.

This year, the Information Technology Foundation for Education (HITSA) granted funding to the University of Tartu Institute of Physics to continue and increase the training and research in the field of quantum computing at the university. With the support of this funding, new interdisciplinary courses focusing on quantum programming will be created.

Excerpt from:
First master's thesis in Quantum Computing defended at the University of Tartu - Baltic Times

As time goes on, the value of efficiency and convenience becomes more and more important. Weve seen this in many examples from talk-to-text, to ordering food directly to your door without ever even speaking to another human.

Now coming into the convenience game is a keyboard that allows you to scan instead of type. Anyline is the new keyboard that instantly collects data with the snap of a camera.

Scan ID information, serial numbers, vouchers, IBANs, and barcodes in an instant with your smartphone, as it is compatible with Android and iOS. The app also allows you to scan things such as gift card barcodes, phone numbers you see on street advertisements, and more so, in a sense, it brings CTRL + C to real life.

With your smartphone, you can instantly collect data with the scan function on your keyboard. The platform is compatible with messenger, email, and browser apps. You scan the data and instantly paste it where you want it, saving the time of manual data entry.

This would be useful for scanning things to your notes section that you may refer to often, like your health insurance ID number, your WiFi router information, credit card info and what not.With anything else like this, the concern of privacy is always there so make sure youre doing what you can to protect your information (using a passcode and/or Face ID, not using shared/public networks, etc.) While you should know it by heart, I would recommend not ever scanning your social security number.

However, something like this does save a lot of time as it doesnt involve mistyping it picks up a barcode accurately. Also, you wont need someone reading something back to you so you can accurately type it down into your phone.

This could be a simple way to save time and become a more efficient person in general, and it makes it easier to share information with others. This is also super helpful for people who have trouble reading the teeny tiny type that barcodes are often displayed in.

Comment your thoughts below, and share any tips you use to help further your efficiency!

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The future of quantum computing is Azure bright and you can try it - The American Genius