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Companies such as Google and IBM have been working on quantum computers (QCs) for well over a decade. They assert QCs will be an order of magnitude faster when pitted against traditional computers. And, according to experts, one aspect of computing theyll have a profound impact on is cybersecurity.

While breakthroughs about QCs hit newswires occasionally, they still remain firmly in the domain of research. In fact, Google and XPRIZE have just announced a $5-million-dollar competition to help find a real-world use of the technology.

But if QCs arent here yet, why should businesses invest in defending against them?

David McNeely, CTO at Delinea, which provides privileged access management (PAM) solutions, believes while the immediate risk appears low, its about time companies start assessing their options.

Read |Investopia 2023: A look at quantum computing and AI

For one, he says, quantum computing is advancing rapidly. According to some theories, it is growing much faster than traditional computing, which follows Moores Law, says McNeely. If we take into account the increased financial investments made into quantum technology, the prospect of an exponentially faster, usable quantum computer by 2030 becomes more of a possible reality.

Commenting on whats at stake, McNeely says encryption secures our online interactions, but the rise of (QCs) poses a significant threat as these powerful machines have the potential to break current encryption algorithms.

Greg Welch, CEO, CyberProtonics, which develops quantum-proof cybersecurity solutions, agrees. Its not a question of if Q-day is coming, its a matter of when. Q-Day is the hypothetical day when QCs will be able to crack our public encryption systems.

According to Welch, Q-day threats arent just for corporate networks. He argues that with the prevalence of remote work, the new edge of the corporate network is the remote office.

Read: UAE identifies 10 future trends for the next decade

Any internet-connected device at home can be impacted by Q-day attacks, from streaming services to connected IoT devices and your home WiFi connected devices, says Welch. Organizations encrypting all their data at the source of creation can help those [remote] users secure their entire connected home.

From a security perspective, McNeely says it is always a good idea to stay one step ahead of cyber criminals.

He agrees that while QCs cant yet break current encryption, its reasonable to assume that hackers with resources, such as nation-state actors, are already thinking about how they will exploit this new technology.

He backs his argument pointing to a 2022 Deloitte poll, which highlighted the risk of criminals adopting the hack/harvest now, decrypt later (HNDL) technique. HNDL involves hackers stealing the encrypted data now, and then waiting until they can run it through a QC when available.

David Boast, general manager, MENA at Endava which among other things helps businesses secure their software agrees with this view. He says HNDL attacks warrant that organizations take a critical look at their present-day infrastructures and security measures.

Read |Investopia 2023: Quantum computing investment is the new economy next 2 years

Welch quantifies the threat pointing to estimates that note that theres a cyberattack every 11 seconds. He says the average financial impact of an attack for an affected organization is expected to be upwards of $10 million, adding up to a staggering $10.5 trillion.

Organizations should therefore invest now to protect against breaches and recognize encryption as just a part of a multi-layered security environment that must account for people, processes, and technologies, suggests Boast.

In that aspect, McNeely shares that the industry already has the technology to secure data with new types of encryption. He says the US National Institute of Standards and Technology (NIST) is currently working on standardizing post-quantum cryptography algorithms.

In fact, he says there are several promising candidates. He particularly points to IBMs CRYSTALS-Kyber algorithm, which is based on a mathematical problem that is much harder for QCs to solve. This makes it more resistant to both conventional and quantum attacks.

As per Boast, the fear of quantum threats will breathe life into a healthy market of quantum protection solutions. However, he believes, the actual appetite, pace and need for adoption of quantum-resistant security depends on several factors.

The challenge here will be to identify the right point at which to invest, says Boast. As with any rapidly maturing technology, the cost to adoption can be expected to rapidly decrease, thus almost burdening first movers.

Read: A quantum supremacy breakthrough could transform our world

Furthermore, he says organizations will have a tough time developing the skills necessary to deliver and maintain impactful deployments. Given the severity of the threat, however, he expects providers of critical services, such as BFSI, government entities, and such, to be forced into action.

Vitaliy Trifonov, head of services group at cybersecurity company Group-IB, suggests organizations transition to quantum-secure cryptography gradually. He says they either deploy parallel quantum solutions, do a phased migration, or a complete overhaul.

Regardless of the approach, he insists, its time for organizations to take action. Waiting for quantum-resilient cryptographic standards and regulations may leave organizations vulnerable, says Trifonov. Embracing the quantum era now is essential to safeguard sensitive data and reap its benefits confidently.

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Is it time for companies to quantum-proof their data? - Economy Middle East

Leading the breakthrough are Benedikt Tissot and Guido Burkard, who are using an advanced method to enable the smooth exchange of information between quantum bits (qubits) using photons as messengers.

Thats the promise of quantum computers. But qubits are the building blocks of quantum computers and are very delicate. Theyre like tiny atomic-scale systems that can easily lose their information.

Theyre turning to photons, which make up light, to ferry quantum information between qubits. This can be described as making qubits fly hence the term flying qubits.

We are proposing a paradigm shift from optimising the control during the generation of the photon to directly optimising the temporal shape of the light pulse in the flying qubit, explains Guido Burkard.

Their method revolves around stimulated Raman emissions, a technique that transforms qubits into photons in a highly controlled way.

What sets their approach apart is fine-tuning the light pulses shape rather than just focusing on creating photons.

Its like sending messages over long distances using light instead of wires. In regular computers, we use electrons to carry information. In quantum computers, its about converting qubits into a form that photons can easily carry.

Tissot and Burkard have developed a system with multiple levels of control over the photons. To adjust exactly how, when, and where the information flows.

While using stimulated Raman emissions isnt new, using them to send qubit states directly is groundbreaking. And its not just about sending information its about doing it with precision and accuracy.

We need to consider several aspects, says Tissot: We want to control the direction in which the information flows as well as when, how quickly and where it flows to. Thats why we need a system that allows for a high level of control.

Their work, published in Physical Review Research in February 2024, dives deep into the intricate workings of this method.

Tissot and Burkard are paving the way for a new era in computers, one where the exchange of information between qubits is not just possible but efficient and reliable.

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The power of photons: A quantum leap in computing - Open Access Government

Amidst rapid technological advancements, Tiny AI is emerging as a silent powerhouse. Imagine algorithms compressed to fit microchips yet capable of recognizing faces, translating languages, and predicting market trends. Tiny AI operates discreetly within our devices, orchestrating smart homes and propelling advancements in personalized medicine.

Tiny AI excels in efficiency, adaptability, and impact by utilizing compact neural networks, streamlined algorithms, and edge computing capabilities. It represents a form of artificial intelligence that is lightweight, efficient, and positioned to revolutionize various aspects of our daily lives.

Looking into the future, quantum computing and neuromorphic chips are new technologies taking us into unexplored areas. Quantum computing works differently than regular computers, allowing for faster problem-solving, realistic simulation of molecular interactions, and quicker decryption of codes. It is not just a sci-fi idea anymore; it's becoming a real possibility.

On the other hand, neuromorphic chips are small silicon-based entities designed to mimic the human brain. Beyond traditional processors, these chips act as synaptic storytellers, learning from experiences, adapting to new tasks, and operating with remarkable energy efficiency. The potential applications include real-time decision-making for robots, swift medical diagnoses, and serving as a crucial link between artificial intelligence and the intricacies of biological systems.

Quantum computing, a groundbreaking field at the intersection of physics and computer science, promises to revolutionize computation as we know it. At its core lies the concept of qubits, the quantum counterparts to classical bits. Unlike classical bits, which can only be in one of two states (0 or 1), qubits can simultaneously exist in a superposition of both states. This property enables quantum computers to perform complex calculations exponentially faster than classical computers.

Superposition allows qubits to explore multiple possibilities simultaneously, leading to parallel processing. Imagine a coin spinning in the airbefore it lands, it exists in a superposition of heads and tails. Similarly, a qubit can represent both 0 and 1 until measured.

However, qubits do not stop there. They also exhibit a phenomenon called entanglement. When two qubits become entangled, their states become intrinsically linked. Changing the state of one qubit instantaneously affects the other, even if they are light-years apart. This property opens exciting possibilities for secure communication and distributed computing.

Classical bits are like light switcheseither on or off. They follow deterministic rules, making them predictable and reliable. However, their limitations become apparent when tackling complex problems. For instance, simulating quantum systems or factoring large numbers (essential for encryption breaking) is computationally intensive for classical computers.

In 2019, Google achieved a significant milestone known as quantum supremacy. Their quantum processor, Sycamore, solved a specific problem faster than the most advanced classical supercomputer. While this achievement sparked excitement, challenges remain. Quantum computers are notoriously error-prone due to decoherenceinterference from the environment that disrupts qubits.

Researchers are working on error correction techniques to mitigate decoherence and improve scalability. As quantum hardware advances, applications emerge. Quantum computers could revolutionize drug discovery by simulating molecular interactions, optimize supply chains by solving complex logistics problems, and break classical encryption algorithms.

Neuromorphic chips mimic the complex structure of the human brain. They are designed to perform tasks in a brain-inspired way. These chips aim to replicate the brains efficiency and adaptability. Inspired by its neural networks, these chips intricately weave silicon synapses, seamlessly connecting in a cerebral dance.

Unlike conventional computers, neuromorphic chips redefine the paradigm by integrating computation and memory within a single unitdistinct from the traditional separation in Central Processing Units (CPUs) and Graphics Processing Units (GPUs).

Unlike traditional CPUs and GPUs, which follow a von Neumann architecture, these chips intertwine computation and memory. They process information locally, like human brains, leading to remarkable efficiency gains.

Neuromorphic chips excel at edge AIperforming computations directly on devices rather than cloud servers. Consider your smartphone recognizing faces, understanding natural language, or even diagnosing diseases without sending data to external servers. Neuromorphic chips make this possible by enabling real-time, low-power AI at the edge.

A significant stride in neuromorphic technology is the NeuRRAM chip, which emphasizes in-memory computation and energy efficiency. In addition, NeuRRAM embraces versatility, adapting seamlessly to various neural network models. Whether for image recognition, voice processing, or predicting stock market trends, NeuRRAM confidently asserts its adaptability.

NeuRRAM chips run computations directly in memory, consuming less energy than traditional AI platforms. It supports various neural network models, including image recognition and voice processing. The NeuRRAM chip bridges the gap between cloud-based AI and edge devices, empowering smartwatches, VR headsets, and factory sensors.

The convergence of quantum computing and neuromorphic chips holds immense promise for the future of Tiny AI. These seemingly disparate technologies intersect in fascinating ways. Quantum computers, with their ability to process vast amounts of data in parallel, can enhance the training of neuromorphic networks. Imagine a quantum-enhanced neural network that mimics the brains functions while leveraging quantum superposition and entanglement. Such a hybrid system could revolutionize generative AI, enabling faster and more accurate predictions.

As we head toward the continuously evolving artificial intelligence discipline, several additional trends and technologies bring opportunities for integration into our daily lives.

Customized Chatbots are leading in a new era of AI development by democratizing access. Now, individuals without extensive programming experience can craft personalized chatbots. Simplified platforms allow users to focus on defining conversational flows and training models. Multimodal capabilities empower chatbots to engage in more nuanced interactions. We can think of it as an imaginary real estate agent seamlessly blending responses with property images and videos, elevating user experiences through a fusion of language and visual understanding.

The desire for compact yet powerful AI models drives the rise of Tiny AI, or Tiny Machine Learning (Tiny ML). Recent research efforts are focused on shrinking deep-learning architectures without compromising functionality. The goal is to promote local processing on edge devices such as smartphones, wearables, and IoT sensors. This shift eliminates reliance on distant cloud servers, ensuring enhanced privacy, reduced latency, and energy conservation. For example, a health-monitoring wearable analyze vital signs in real time, prioritizing user privacy by processing sensitive data on the device.

Similarly, federated learning is emerging as a privacy-preserving method, allowing AI models to be trained across decentralized devices while keeping raw data local. This collaborative learning approach ensures privacy without sacrificing the quality of AI models. As federated learning matures, it is poised to play a pivotal role in expanding AI adoption across various domains and promoting sustainability.

From an energy efficiency standpoint, battery-less IoT Sensors are revolutionizing AI applications for Internet of Things (IoT) devices. Operating without traditional batteries, these sensors leverage energy harvesting techniques from ambient sources like solar or kinetic energy. The combination of Tiny AI and battery-less sensors transforms smart devices, enabling efficient edge computing and environmental monitoring.

Decentralized Network Coverage is also emerging as a key trend, guaranteeing inclusivity. Mesh networks, satellite communication, and decentralized infrastructure ensure AI services reach even the most remote corners. This decentralization bridges digital divides, making AI more accessible and impactful across diverse communities.

Despite the excitement surrounding these advancements, challenges persist. Quantum computers are notoriously error-prone due to decoherence. Researchers continuously struggle with error correction techniques to stabilize qubits and improve scalability. In addition, neuromorphic chips face design complexities, balancing accuracy, energy efficiency, and versatility. Additionally, ethical considerations arise as AI becomes more pervasive. Furthermore, ensuring fairness, transparency, and accountability remains a critical task.

In conclusion, the next generation of Tiny AI, driven by Quantum Computing, Neuromorphic Chips, and emerging trends, promises to reshape the technology. As these advancements unfold, the combination of quantum computing and neuromorphic chips symbolizes innovation. While challenges persist, the collaborative efforts of researchers, engineers, and industry leaders pave the way for a future where Tiny AI transcends boundaries, leading to a new era of possibilities.

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The Next Generation of Tiny AI: Quantum Computing, Neuromorphic Chips, and Beyond - Unite.AI

Here at Zeihan On Geopolitics we select a single charity to sponsor. We have two criteria:

First, we look across the world and use our skill sets to identify where the needs are most acute. Second, we look for an institution with preexisting networks for both materials gathering and aid distribution. That way we know every cent of our donation is not simply going directly to where help is needed most, but our donations serve as a force multiplier for a system already in existence. Then we give what we can.

Today, our chosen charity is a group called Medshare, which provides emergency medical services to communities in need, with a very heavy emphasis on locations facing acute crises. Medshare operates right in the thick of it. Until future notice, every cent we earn from every book we sell in every format through every retailer is going to Medshares Ukraine fund.

And then theres you.

Our newsletters and videologues are not only free, they willalwaysbe free. We also will never share your contact information with anyone. All we ask is that if you find one of our releases in any way useful, that you make a donation to Medshare. Over one third of Ukraines pre-war population has either been forced from their homes, kidnapped and shipped to Russia, or is trying to survive in occupied lands. This is our way to help who we can. Please, join us.

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In today's fast-paced and ever-changing world, staying ahead of market trends is crucial for businesses to thrive. Traditional methods of market analysis and prediction are often limited by their reliance on classical computing algorithms. However, a revolutionary technology called Quantum AI is changing the game, offering unprecedented capabilities for adapting to changing market dynamics.

The essence of Quantum AI lies in the convergence of quantum computing and artificial intelligence. To comprehend the power of Quantum AI in market analysis, it is imperative to grasp the fundamental concepts behind quantum computing.

Quantum AI represents a cutting-edge technological frontier that holds immense promise for revolutionizing various industries, and with this knowledge in mind, your Quantum AI journey begins. By leveraging the principles of quantum mechanics and artificial intelligence, Quantum AI opens up a realm of possibilities that were previously unimaginable. The fusion of these two advanced fields not only enhances computational capabilities but also paves the way for groundbreaking advancements in data analysis, machine learning, and predictive modeling.

Quantum computing is based on the principles of quantum mechanics, which deal with the behavior of matter and energy at the atomic and subatomic levels. Unlike classical computers that use bits to represent information as either a 0 or a 1, quantum computers utilize quantum bits, or qubits, which can exist in multiple states simultaneously. This enables quantum computers to perform complex calculations at an unimaginable speed.

At the core of quantum computing is the phenomenon of superposition, where qubits can exist in a state of 0, 1, or any quantum superposition of these states. This unique property allows quantum computers to explore multiple solutions to a problem simultaneously, leading to exponential speedup in certain computational tasks. Additionally, quantum entanglement, another key principle in quantum mechanics, enables qubits to be interconnected in such a way that the state of one qubit is dependent on the state of another, regardless of the physical distance between them.

Artificial intelligence, on the other hand, focuses on the development of intelligent machines capable of simulating human-like behavior. By combining the computational power of quantum computers with AI algorithms, Quantum AI harnesses the potential for enhanced problem-solving, optimization, and prediction capabilities.

Quantum AI algorithms have the capacity to process and analyze vast amounts of data with unprecedented efficiency, leading to more accurate insights and predictions. The synergy between quantum computing and AI not only accelerates the pace of innovation but also unlocks new frontiers in machine learning, natural language processing, and robotics. As Quantum AI continues to evolve, it is poised to redefine the boundaries of what is possible in the realm of advanced computing and artificial intelligence.

Market analysis involves examining vast amounts of data to identify patterns, make predictions, and inform decision-making. Quantum AI offers unique advantages in this regard, revolutionizing traditional approaches and opening up new possibilities.

Quantum AI combines the principles of quantum mechanics with artificial intelligence, creating a powerful tool for market analysis. By harnessing the properties of superposition and entanglement, Quantum AI can process and analyze data in ways that classical computers cannot. This allows for more sophisticated modeling of market dynamics and more accurate predictions of future trends.

One of the key strengths of Quantum AI lies in its predictive capabilities. By leveraging the immense computational power of quantum computers, it becomes possible to analyze extensive historical data and identify complex patterns and trends. Quantum AI algorithms can uncover hidden correlations and make highly accurate predictions, empowering businesses with actionable insights.

Furthermore, Quantum AI can handle non-linear relationships and high-dimensional data with ease, providing a more comprehensive understanding of market behavior. This enhanced predictive ability enables businesses to anticipate market shifts, optimize investment strategies, and mitigate risks effectively.

Another significant advantage of Quantum AI is its ability to adapt to real-time market changes and make informed decisions on the fly. Traditional market analysis methods often struggle to keep up with rapidly evolving trends. Quantum AI, however, excels in handling large volumes of data in real-time, enabling businesses to react promptly to emerging opportunities and risks.

Moreover, Quantum AI's adaptive nature allows for dynamic decision-making processes that can adjust strategies in response to changing market conditions. This agility is crucial in today's fast-paced and volatile business environment, where timely decisions can mean the difference between success and failure.

Understanding and capitalizing on market trends is vital for businesses to stay competitive. Quantum AI offers unique advantages in identifying and leveraging emerging market trends.

By processing vast amounts of data from multiple sources, Quantum AI can detect subtle shifts and patterns that may indicate emerging market trends. This provides businesses with a competitive edge, allowing them to anticipate changes and adapt their strategies proactively.

Market forecasting is an essential aspect of market analysis, helping businesses make informed decisions about future market conditions. Quantum AI's ability to process vast amounts of data and identify hidden patterns and correlations allows for more accurate and reliable market forecasting. This assists businesses in making strategic decisions to drive growth and profitability.

As Quantum AI continues to evolve, its potential applications in market analysis are vast. However, several challenges and considerations need to be addressed to fully realize the benefits of this revolutionary technology.

One of the main challenges in adopting Quantum AI for market analysis is the need for highly specialized skills and resources. Quantum computing is a complex field that requires expertise in quantum physics and computer science. Collaboration between different disciplines and investments in research and development are crucial to overcoming these challenges.

The impact of Quantum AI in market analysis extends across various industries. From finance and healthcare to retail and manufacturing, businesses can leverage the power of Quantum AI to gain a competitive advantage. The ability to gather valuable insights and make informed decisions based on accurate market analysis has the potential to transform industries and reshape market dynamics.

In conclusion, Quantum AI represents a monumental leap forward in market analysis. By combining the computational power of quantum computing with the intelligence of AI algorithms, businesses can enhance their ability to adapt to changing market trends. The predictive capabilities, real-time adaptability, and accurate market analysis offered by Quantum AI can empower businesses to make informed decisions and stay ahead of the competition. Embracing Quantum AI is not only an investment in the future but also a means to drive innovation and growth in an ever-evolving market landscape.

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