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Last week, Irans military unveiled what it called the first product of the quantum processing algorithm of the Imam Khomeini Naval University of Nowshahr. During a ceremony at the university, the Islamic Republics military revealed a bit of electronics sealed under glass. It appeared to be a common development board, available widely online for around $600.

According to multiple state-linked news agencies in Iran, the computer will help Iran detect disturbances on the surface of water using algorithms. Iranian Rear Admiral Habibollah Sayyari showed off the board during the ceremony and spoke of Irans recent breakthroughs in the world of quantum technology.

The touted quantum device appears to be a development board manufactured by a company called Diligent. The brand ZedBoard appears clearly in pictures. According to the companys website, the ZedBoard has everything the beginning developer needs to get started working in Android, Linux, and Windows. It does not appear to come with any of the advanced qubits that make up a quantum computer, and suggested uses include "video processing, reconfigurable computing, motor control, software acceleration," among others.

I'm sure this board can work perfectly for people with more advanced [Field Programmable Gate Arrays] experience, however, I am a beginner and I can say that this is also a good beginner-friendly board, said one review on Diligents website. Those interested in the board can buy one on Amazon for $589.

Quantum devices used for locating ships and navigating at sea are real. The U.K. Navy recently tested one such device, which uses ultracold atoms to act as a kind of accelerometer, at sea. It looks nothing like the device unveiled by Iran.

Image: Imperial College London

Its impossible to know if Iran has figured out how to use off-the-shelf dev boards to make quantum algorithms, but its not likely. True quantum devices are experimental pieces of equipment that don't typically resemble circuit boards of the kind you'd find in a home desktop, although researchers have reported being able to simulate some quantum processes on classical computers. Even if Iran is merely claiming that the device was manufactured with the help of quantum algorithms, they may not have been neededthe device is still a ZedBoard that anyone can buy, without any visible modifications.

This isnt the first time Iran has shown off tech with a less than credible pedigree. In 2020, the Iranian Army revealed a device it claimed could detect COVID and AIDS. It appeared to be similar to another device that was previously sold as a bomb detector.

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TRAVERSE CITY, Michigan In recent years, educational institutions in South Africa have begun investing in quantum computing. Schools like the University of the Witwatersrand, Stellenbosch University and the University of KwaZulu-Natal have all developed an interest in exploring this exciting technology and its uses. But what is quantum computing? More importantly, what are the ways quantum computing benefits development in Africa?

Quantum computingutilizes superposition and entanglement, two basic elements of quantum mechanics, to process information at phenomenal speeds. With superposition, a particle can effectively be in two states at the same time, though it is only seen as one or the other when measured. Entanglement allows for a connection between two particles so that they react to any changes in state between them immediately. This makes quantum computing far faster and more accurate when measuring data and performing simulations than traditional computing technology.

Quantum computers will not replace current-day computers any time soon, but can be applied to several applications as a serious boon for anyone that uses them. Here are five notable ways quantum computing benefits development in Africa.

In countries like the Democratic Republic of Congo, where the average annual salary is as low as U.S. $490 a year,the cost of HIV drugs(average U.S. $75 a year) is restrictive for most people. Traditional methods of drug development and production keep costs high; however, quantum computing may change this. The processing power of quantum computers would allow for bettermodeling of the molecular structuresof specific viruses, which exponentially reduces the cost and time for drug development. While doctors exploring the use of quantum computing in medicine at IBM Research-Africa acknowledge that the development of new HIV drugs is years away, the potential for future relief is welcoming.

Another way quantum computing benefits development in Africa is byassisting infrastructure. Many countries throughout Africa suffer from poorly implemented services and systems, particularly roads and transportation. The Million Neighborhoods Map, launched in 2019, details informal settlements and impoverished sectors throughout many cities in Africa and beyond. Quantum computing, however, can help inoptimizing roadsto more efficiently reach those in need of them. In addition, quantum computing can make travel routes and service deliveries more reliable. An example of this is when 4,000 or more taxis with an optimization problem tested the capabilities of quantum computing, which delivered 5,000 solutions within seconds.

Setting up stable and working energy grid systems in Africa is often a tough and complex task. Many regions of the continent have little to no power, while what little systems there are struggle to keep up with demand. Transmission across power grids can also be a problem, with over 15% of a grids energy lost in the wires. With quantum computing, algorithms can be applied to local electrical grids that assist in reducing energy overloads.Simulations and modelingdone through computing can help build improved and efficient smart grid technologies, making energy grid construction easier and accessible for communities. These simulations can also assist in building wind turbines and improved transmission systems, demonstrating more ways quantum computing benefits development in Africa.

Finances and security are major worries for any person or country. The risk of fraud and other cyberattacks is present for anyone in Africa who has access to online banking. On the opposite end, many areas may not have access to a bank at all, making financial stability a challenge for some people. Improving these systemsand protecting valuable assets, however, is another way quantum computing benefits development in Africa. With the complex systems modeling that quantum computing is capable of, financial data can be used to improve fraud detection and find faults in ones banking situations. These systems can also open Africa to credit scoring, making credit offers widely available to others where banks are inaccessible. Meanwhile, banks can utilize post-quantum cryptography algorithms to make data transfers easier and safer, improving cybersecurity immensely.

With the dangers of climate change increasing, freak weather events pose a great danger to many parts of Africa. Drought in particular is a frequent worry for many sections of the continent, endangering local agriculture and threatening famine.Quantum computing can analyzeimmense amounts of weather data to better predict weather patterns and make large-scale weather events easier to respond to. This improved weather forecasting would be especially beneficial to African countries where such events can be devastating to a community and food security.

It will still be many years before quantum computing becomes widespread. Yet already, there are many possibilities by which quantum computing benefits development in Africa. Alongside others around the world, Africa can become a leading force in quantum technology for everyones betterment.

Kenneth BerendsPhoto: Flickr

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[Vancouver, Canada, 12-06-2023] Emergen Research, a leading provider of market research solutions, is thrilled to announce the release of its highly anticipated Quantum Computing for Enterprise market research report. This innovative offering aims to empower businesses across industries with valuable insights and data-driven strategies to drive growth and success.

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The global quantum computing for enterprise market is forecast to reach a market size of USD 3,907.4 Million by 2027, and register a steady revenue growth rate, according to latest analysis by Emergen Research. Growth of the global quantum computing for enterprise market in terms of revenue is expected to increase substantially over the forecast period due to increasing global demand for quantum computing simulation for drug discovery and to identify new chemical compounds. Rising need to accelerate the learning process of Artificial Intelligence (AI) is also a major factor expected to further boost global quantum computing for enterprise market growth during the forecast period. Rising adoption of quantum computing in the automotive industry is expected to further support growth in market size in future.

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Supercomputers have long been at the forefront of technological advancements, enabling researchers to tackle complex problems and simulate processes that would be impossible to study in real-world conditions. These high-performance computing (HPC) systems have been instrumental in advancing fields such as climate modeling, drug discovery, and astrophysics. However, as the demand for more powerful supercomputers continues to grow, so does the need for a more energy-efficient solution to power these behemoths.

Traditionally, supercomputers have relied on thousands of processors working in parallel to perform complex calculations at breakneck speeds. While this approach has yielded impressive results, it has also led to a significant increase in power consumption. As a result, researchers and engineers have been exploring alternative computing paradigms that could potentially offer both increased performance and reduced energy requirements.

One such promising technology is quantum computing, which leverages the principles of quantum mechanics to perform calculations that would be impossible for classical computers. Unlike traditional computing, which relies on bits that can be either a 0 or a 1, quantum computing uses qubits, which can exist in multiple states simultaneously. This allows quantum computers to perform multiple calculations at once, potentially leading to exponential speedups in certain problem-solving tasks.

The potential applications of quantum computing in the realm of high-performance computing are vast. For example, quantum computers could be used to simulate the behavior of molecules and materials at the quantum level, leading to breakthroughs in materials science and drug discovery. Additionally, quantum computers could be used to optimize complex systems, such as transportation networks and supply chains, leading to increased efficiency and reduced costs.

Despite the potential benefits of quantum computing, there are still significant challenges that must be overcome before this technology can be widely adopted in the HPC space. One of the primary obstacles is the development of error-correcting techniques that can mitigate the inherent instability of qubits. Additionally, researchers must find ways to scale up the number of qubits in a quantum computer, as current prototypes typically have only a few dozen qubits.

Another challenge facing the adoption of quantum computing in the HPC space is the development of suitable software and algorithms. While some progress has been made in this area, there is still much work to be done in order to fully harness the power of quantum computing. This includes the development of new programming languages and tools that can effectively leverage the unique capabilities of quantum computers.

Despite these challenges, there is a growing consensus among researchers and industry leaders that quantum computing represents the future of high-performance computing. As a result, significant investments are being made in the development of this technology, both by governments and private companies. For example, the United States recently announced a $1 billion initiative to support research in quantum computing and artificial intelligence, while companies such as IBM, Google, and Intel are actively working on developing their own quantum computing platforms.

In conclusion, the quest for more powerful and energy-efficient supercomputers has led researchers to explore the potential of quantum computing as a viable alternative to traditional high-performance computing paradigms. While there are still significant challenges to be overcome, the potential benefits of quantum computing in terms of performance and energy efficiency make it an attractive option for the future of supercomputing. As research and development in this area continue to progress, it is likely that we will see quantum computing play an increasingly important role in tackling the power problem in high-performance computing.

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Over the last few months, artificial intelligence (AI) has dominated headlines everywhere. And now, it looks like this new-age technology has taken over big tech companies, enterprises, startups, schools, and life itself. From music, art, and films to homework, news, and more, AI is the next big thing.

Suffice it to say, it is important to keep up with why AI is this interesting, what it is made up of, and why exactly it is a big deal.

In this article, we will navigate the vast landscape of artificial intelligence terminology across machine learning (ML), natural language processing (NLP), deep learning, quantum computing, and a lot more.

While numerous scientists and engineers laid the groundwork for AI since the 1940s, American Computer scientist John McCarthy coined the term Artificial Intelligence in 1955. A year later, he, along with other scientists, held the first AI conference at Dartmouth University.

In the 1980s, there was a shift towards neural networks and machine learning approaches. Researchers explored algorithms inspired by the structure and functioning of the human brain, enabling machines to learn from data and improve their performance over time.

The late 1980s and early 1990s witnessed a period known as the "AI Winter" when interest and funding significantly declined in this area due to unmet expectations. However, the field experienced a resurgence in the late 1990s with advancements in areas such as data mining, natural language processing, and computer vision.

In recent years, the availability of vast amounts of data and advancements in computational power have fuelled breakthroughs in AI. Deep learning, a subfield of machine learning that utilises neural networks with multiple layers, has led to significant advancements in image and speech recognition, natural language processing, and other AI applications.

An algorithm is a definite set of instructions that allow a computer to perform a certain task. AI algorithms help a computer understand how to perform certain tasks and achieve the desired results on its own. In other words, algorithms set the process for decision-making.

Machine learning is a subset of AI. It effectively enables machines to learn using algorithms, data and statistical models to make better decisions. While AI is a broad term that refers to the ability of computers to mimic human thought and behaviours, ML is an application of AI used to train computers to do specific tasks using data and pattern recognition.

A subset of ML, deep learning trains computers to do what humans canlearn by example. Computer models can be taught to perform tasks by recognising patterns in images, text, or sound, sometimes surpassing humans in their ability to make connections. Computer scientists leverage large sets of data and neural network architectures to teach computers to perform these tasks.

DL is employed in cutting-edge technology like driverless cars to process a stop sign or differentiate between a human and a lamp post.

Yet another application of ML, natural language processing helps machines understand, interpret, and process human language to perform routine tasks. It uses rules of linguistics, statistics, ML, and DL to equip computers to fully understand what a human is communicating through text or audio and perform relevant tasks. AI virtual assistants and AI voice recognition systems like voice-operated GPS are examples of NLP.

Computer vision is a form of AI that trains computers to recognise visual input. For instance, a machine will be able to analyse and interpret images, videos and other visual objects to perform certain tasks that are expected of it.

An example is medical professionals using this technology to scan MRIs, X-rays or ultrasounds to detect health problems in humans.

Robotics is a branch of engineering, computer science, and AI that designs machines to perform human-like tasks without human intervention. These robots can be used to perform a wide variety of tasks that are either too complex and difficult for humans or are repetitive or both. For example, building a robotic arm to assemble cars in an assembly line is an example of a robot.

Data science uses large sets of structured and unstructured data to generate insights that data scientists and others can use to make informed decisions. Often, data science employs ML practices to find solutions to different challenges and solve real-world problems.

For instance, financial institutions may employ data science to analyse a customers financial situation and bill-paying history to make better decisions on lending.

An extension of data science is data mining. It involves extracting useful and pertinent information from a large data set and providing valuable insights. It is also known as knowledge discovery in data (KDD). Data mining has numerous applications, including in sales and marketing, education, fraud detection, and improving operational efficiency.

Quantum computing uses theories of quantum physics to solve complex problems that classic computing cannot solve. It is used to run complex simulations in a matter of seconds by converting real-time language into quantum language.

Google has a quantum computer that they claim is 100 million times faster than an average computer. Quantum computing can be used in a variety of fields ranging from cybersecurity to pharmaceuticals to solve big problems with fewer resources.

A chatbot employs AI and NLP to simulate human conversations. It can operate through text or voice conversations. Chatbots use AI to analyse millions of conversations, learn from human responses and mimic them to provide human-like responses. This tech has found great usage in customer service and as AI virtual assistants.

All AI and ML tools are human-trained. This means that any inherent human bias can reflect AI bias. AI bias is a term that refers to the tendency of machines to adopt human biases because of how and by whom they are coded or trained. Algorithms can often reinforce human biases.

For instance, a facial recognition platform may be able to recognise Caucasian people better than people of colour because of the data set it has been fed. It is possible to reduce AI bias through more testing in real-life circumstances, accounting for these biases and improving how humans operating these systems are educated.

Today, AI represents the human capacity to create, innovate, and push the boundaries of what was once thought impossible.

So, whether you are an AI enthusiast, a curious learner, or a decision-maker shaping the future, it is essential to equip yourself with the right knowledge to survive in a world that is being increasingly powered by AI and its tools.

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