# Googles Quantum Computer Turned Back On And What It Reveal Scared All Scientists

Quantum computing joining forces with AI would mark a significant collaboration between advanced software and hardware technologies. However, the potential outcomes of merging robotics with quantum computers are causing concern among scientists. What secrets will be unlocked? Will this collaboration work or ruin the entire research? Join us as we unravel the chilling revelation uncovered after Google's newest quantum computer was activated! Michio Kaku, a theoretical physicist, and futurist, often explores the exciting possibilities and challenges brought by new technologies, like quantum computers. As scientists, we love thinking about new, creative ideas that could change how we see the world. Kaku's concerns about quantum computers focus mainly on how they might affect security, privacy, and society, rather than fearing the technology itself. So,

how do these quantum computers work? Well, they play by different rules than regular computers. Instead of using regular bits that can only be 0 or 1, quantum computers use qubits, which can be 0, 1, or both at the same time! This mind-bending feature allows them to solve problems that classical computers can't even touch. Quantum computers have the power to explore vast landscapes of possibilities and give us reasonable answers. But building them is like something out of a science fiction movie. Qubits are made from exotic materials and kept at near-absolute zero temperatures to coax out their quantum properties. However, just having the hardware isn't enough. We need special instructions, like Quantum gates, to manipulate these qubits and perform calculations. But here's the tricky part: quantum systems are delicate

and prone to errors. Scientists have to use complex protocols to fix mistakes without messing up the qubits. It's like being quantum mechanics ninjas, always swooping in to correct errors without disturbing the delicate state of the qubits. And here's an interesting tidbit:

correct ideas often come from interacting with incorrect ones. So, keeping these quantum systems stable requires extreme measures. That's where dilution refrigerators come in. These superpowered freezers help minimize noise and keep the quantum magic running smoothly. Imagine constructing a computer and submerging it into a large tank of liquid helium. While the

astonishing capability of qubits existing in multiple states at once promises to transform how we solve problems, quantum computers are the ultimate computing machines because they operate on atoms and can perform calculations far beyond our current capabilities. However, keeping these machines running presents a unique challenge. Superconducting qubits, a common type, require temperatures near absolute zero, colder than even the depths of space. This is where dilution refrigerators come in – the unsung heroes of quantum computing. These powerful freezers tirelessly maintain an icy environment for the delicate qubits, but they come at a cost: they consume massive amounts of energy, sometimes tens of thousands of watts. Surprisingly,

while the computations themselves may be energy-efficient for specific tasks, the supporting infrastructure behind quantum computers comes with a hefty energy price tag. Dilution refrigerators and the intricate electronics needed to control and read qubit states are real power hogs. So, how do these mind-bending qubits translate into mind-blowing speed? Here's where the true magic of quantum computing unfolds. Imagine a million coins spinning in the air, each capable of landing on heads or tails. A regular computer can only check each coin one by one. However, a quantum computer can analyze all the possibilities at the same time, thanks to a phenomenon called superposition. The incredible parallel processing power of quantum

computers allows them to solve problems much faster than even the most powerful classical computers. But it's not just about raw power – quantum computers also use special algorithms designed to take advantage of the unique properties of qubits. For example, imagine searching through a massive unsorted database to find a specific item. A classical computer would have to check each entry one by one. However, a quantum algorithm could find the

item in a fraction of the time. This ability to dramatically speed up computations opens up new possibilities in fields like medicine, material science, and artificial intelligence, where complex simulations and calculations are currently limited by classical computing. Moreover, the advancements in quantum computing are set to change digital security. Redefining Cryptography and Security Traditional cryptographic systems, like public key cryptography, which secures our emails and financial transactions, rely on mathematical challenges that are difficult for classical computers to solve. However, with the increased computational capabilities of quantum computers, these cryptographic systems become vulnerable to attacks. This highlights the need for new cryptographic

methods to secure our digital information in the era of quantum computing. However, this security relies on the limitations of classical computing. In the world of quantum computing, the rules of the game change entirely. Qubits, the building blocks of quantum computers, can exist in multiple states simultaneously, allowing quantum computers to perform many calculations at once. This ability poses a direct threat to the foundation of public key cryptography.

For example, RSA encryption, which relies on the difficulty of factoring the product of two large prime numbers, could potentially be undone by a quantum computer in mere seconds or minutes – a task that would take a classical computer thousands of years. Quantum computers have the power to compute things that are currently beyond our reach. The introduction of Shor's algorithm in 1994 was an innovative moment, demonstrating the potential of quantum computers to solve previously overwhelming problems at unimaginable speeds. This algorithm can efficiently factor large numbers and solve discrete logarithm problems, making encryption methods like RSA, Diffie-Hellman, and ECC vulnerable. The implications of this are profound, highlighting the urgent need for new cryptographic standards resistant to the capabilities of quantum computing. However,

on the flip side of this looming threat is the emergence of quantum cryptography, offering a glimmer of hope in securing communications against the prowess of quantum computing. Quantum key distribution, QKD, a novel approach in this field, leverages the principles of quantum mechanics to secure communication channels. The foundation of QKD's security lies not in computational complexity, but in the physical properties of quantum particles. A standout feature of this method is its incorporation of the no-cloning theorem, which states that it is impossible to exactly copy an unknown quantum state. This concept ensures that if someone tries

to listen in on a conversation, it will be noticed because when you try to measure a quantum system, it changes, which tells the people talking that there's a security problem. As we learn more about quantum computing, it's amazing to see how it can change the world. It's like a huge step forward in computer power, promising to solve problems that regular computers couldn't touch. But, like with any big new technology, quantum computing brings both good things and challenges.

It's not just about making encryption stronger. Quantum computers might be able to break the codes we use now sooner than we think, which could mess up our digital privacy. Stuff that's safe now might not be safe later, leaving people open to problems like someone stealing their identity or their money. And it's not just about keeping secrets safe. Quantum computing is also going to shake things up in finance. It's going to make a big change in how we look at money and trading,

and it could change digital currencies in a big way. So, while it's exciting, it's also a little scary to think about all the changes it might bring. The Challenges to Global Stability and Fairness Cryptocurrencies, like Bitcoin and Ethereum, are known for their strong security, relying on complicated codes to keep transactions safe. But the power of quantum computing could change all that. Quantum computers are so smart that they might be able to crack these codes, which could lead to some serious problems. Imagine someone being able to spend the same money twice or even create new money without permission. It's not just a small issue—it could mess up the whole system, allowing bad people to mess with transaction records or steal money. Even just the idea

that quantum computers could break into cryptocurrencies could make people lose faith in them, causing prices to drop and making people rethink using digital money. And it's not just about cryptocurrencies. Quantum computing could shake up the world of finance in a big way. It could help us understand financial systems better and predict what might happen in

the market way faster and more accurately than regular computers ever could. That could change how we handle money and investments, making things more efficient but also more complicated. This big step forward in how we analyze things with quantum computing could completely change how we think about and deal with financial markets. But, it's not all easy sailing. Some big problems come with this quantum advantage, especially when it comes to fairness in the market.

Think about it— if only a few people had access to quantum computers. They could predict what's going to happen in the market way before everyone else, giving them a huge advantage. This wouldn't just mess up how the market works, but it would also bring up some serious questions about whether it's fair or not. To make sure everyone has a fair chance in the market, we might need to change the rules. We'd have to make sure that people with quantum computers

can't use them to cheat or mess with how the market works. It's all about making sure the market stays fair and trustworthy, even as technology keeps moving forward. Another potential risk of quantum computing is that it could make some countries more vulnerable to warfare. Quantum computers are incredibly powerful and can solve tough problems, which is useful for tackling challenges in our world. However, they also pose a challenge when it comes to countries and their military strategies. For example, one country has quantum computing technology, while another doesn't. This creates a big

problem. The country with quantum computing has a significant advantage. They can use it to outsmart and outmaneuver their opponent at every turn. Meanwhile, the other country, lacking quantum technology, struggles to defend its important stuff like military secrets or critical assets. This unequal access to advanced technology could lead to an information gap in warfare, putting some nations at a disadvantage. So, while quantum

tech holds great promise, it also raises concerns about fairness and security in global conflicts. The shortage of helium is something that often goes unnoticed when we talk about quantum computing. But, just like your computer needs a fan to stay cool, quantum technology relies on helium for cooling. Helium isn't unlimited, and it can be pretty expensive. This brings up

two big concerns. First, companies using quantum computers need a steady supply of helium to keep their machines running smoothly. Think of it like needing a specific ingredient for your favorite recipe—if it's hard to find, cooking becomes a real challenge. Similarly, if companies can't get enough helium, operating quantum computers becomes a struggle. Secondly,

because helium isn't plentiful, there's a risk that only a few organizations will have the capability to operate quantum computers. This concentration of expertise might not be good for the overall progress of quantum computing. So, the availability of helium is more crucial than we might realize for the future of this advanced technology. The growing concerns posed by quantum computing highlight the pressing need to switch to post-quantum cryptographic methods, as current encryption techniques are at risk of being compromised by quantum attacks. Let's look into some aspects of public key encryption methods and their contenders.

We'll start by examining some performance tests regarding key exchange and digital signatures. Public key encryption involves using a pair of keys: one to encrypt and another to decrypt. For example, Alice's public key encrypts, while her private key decrypts. On the other hand,

for digital signatures, we flip the process. We use Alice's private key to sign a message and her public key to verify the signature. In key exchange, we typically use methods like the elliptic curve Diffie-Hellman, where both Bob and Alice generate private keys, exchange public keys, and derive a shared key using a key derivation function. However, these methods face risks from

quantum computers. RSA, ElGamal, and elliptic curve methods are all vulnerable to attacks from quantum computers. Therefore, we need to transition to post-quantum crypto methods. For elliptic curve methods, we use a base point on a curve and a secret key to generate a public key. Unfortunately, methods like ECDSA are vulnerable to quantum computers. New methods like Curve25519, SECP256K1, and P256 offer standard signature and key exchange methods but are also susceptible to quantum attacks. Now, let's explore the existing hash-based methods and their performance.

Navigating the Future of Digital Security In the past, creating a large number of private keys required a stateful system, often referred to as a "miracle tree." This method had its limitations. However, newer techniques have emerged to enhance security and efficiency by making the process stateless and integrating symmetric key methods. One such

approach is McEliece, a code-based method with a long-standing track record of security. Another method is multivariate polynomial quadratics, which includes techniques like the oil and vinegar method, where polynomials are created with a hidden trap door. Learning with errors, LWE, combined with lattice-based methods is gaining popularity due to its ability to produce compact digital signatures and facilitate key exchange. Lastly, isogenies use elliptic curves to transition from one curve to another. We have a variety of options, including hash-based methods, multivariate quadratics, code-based methods, and isogenies. Determining the best method for post-quantum cryptography is crucial. In 2016, the U.S. Department of Commerce's National Institute of Standards and Technology,

NIST initiated a post-quantum cryptography project, conducting three rounds of evaluations. They've chosen the first batch of encryption tools built to withstand future quantum computers. These powerful computers could potentially break the security measures used in our everyday digital activities, like online banking and email. These four selected encryption algorithms will be part of NIST's post-quantum cryptographic standard, which is expected to be finalized in about two years. Commerce Secretary Gina M. Raimondo highlighted the importance of this announcement. She emphasized that it's a crucial step in protecting our sensitive data from potential cyberattacks by quantum computers. With NIST's expertise and dedication to advanced technology,

businesses can innovate while ensuring the trust and confidence of their customers. This decision came after a six-year effort managed by NIST. They called upon cryptographers worldwide to develop encryption methods that could resist attacks from more powerful quantum computers. The selection marks the beginning of the final phase of NIST's project to standardize post-quantum cryptography. NIST is always looking ahead to understand what the U.S. industry and society

might need in the future. We know that once quantum computers powerful enough to break our current encryption systems are created, our information could be at risk," explained Laurie E. Locascio, the Under Secretary of Commerce for Standards and Technology and NIST Director. "That's why our post-quantum cryptography program has brought together the brightest minds in cryptography from around the world to develop these quantum-resistant algorithms.

These algorithms will set a standard and greatly enhance the security of our digital information." In addition to the four selected algorithms, NIST is considering four more for inclusion in the standard. They will announce the finalists from this group at a later date. NIST is releasing its choices in two stages to ensure a wide range of defense tools. Cryptographers have recognized from the start of NIST's effort that different systems and tasks require encryption. A useful standard would offer solutions tailored for various situations, use different approaches for encryption, and provide multiple algorithms for each use case in case one is found to be vulnerable. Encryption uses math to protect sensitive electronic information, including

the secure websites we surf and the emails we send. Widely used public-key encryption systems, which rely on math problems that even the fastest conventional computers find intractable, ensure these websites and messages are inaccessible to unwelcome third parties. However, a sufficiently capable quantum computer, which would be based on a different technology than conventional computers could solve these math problems quickly, defeating encryption systems. To counter this threat, the four quantum-resistant algorithms rely on math problems that both conventional and quantum computers should have difficulty solving, thereby defending privacy both now and down the road. The algorithms are designed for two main tasks for which encryption is typically used: general encryption, used to protect information exchanged across a public network; and digital signatures, used for identity authentication. All four of the algorithms were created by experts collaborating

from multiple countries and institutions. NIST has chosen the CRYSTALS-Kyber algorithm for general encryption, especially when we're accessing secure websites. When it comes to verifying identities in digital transactions or signing documents online, digital signatures play a crucial role.

NIST has carefully selected three algorithms for this purpose: CRYSTALS-Dilithium, FALCON, and SPHINCS+. CRYSTALS-Dilithium stands out as the primary choice, recommended by NIST for its high efficiency. However, FALCON is preferred in situations where smaller signatures are needed. On the other hand, SPHINCS+ offers a different approach based on hash functions, making it valuable as a backup option. Meanwhile, four other algorithms are still being considered for general encryption, following various approaches that don't involve structured lattices or hash functions. During this developmental phase, NIST encourages security experts to explore these new algorithms but advises against integrating them into systems just yet, as there may be slight changes before finalization.

To prepare for the transition, users can assess their systems for applications using public-key cryptography, which will need replacement before quantum computers become a cryptographic concern. Additionally, they can inform their IT departments and vendors about the upcoming changes. Now, with the NIST project reaching its final stages, the need to transition from traditional public key methods to post-quantum cryptography is becoming increasingly urgent. Currently, there are four contenders for key exchange and three finalists for digital signatures. NIST aims to ensure diversity by considering alternative winners. This approach ensures that if one method, such as a lattice-based approach,

is selected, there will be alternative options available during the standardization process. The Diversity of Encryption Methods We have a variety of encryption methods to consider, each with its unique characteristics. Michaelis, a well-established code-based method, has been in use for quite some time. Kyber and Saber are lattice-based methods, while NTRU is another lattice method that's gained recognition. Lattice methods might dominate the key exchange arena. When it comes to digital signatures, we see a mix of lattice methods like Dilithium and Falcon, along with a multivariate polynomial method called Rainbow, which is also known as an "oil and vinegar" method. As for alternatives,

we have methods like BIKE, Frodo, HQC, and PSYCH, which is the only isogeny-based method among them. The finalists for key exchange include McLees, Kyber, NTRU, and Saber, each varying in the sizes of keys they generate and their performance. To evaluate their performance, we utilized the LibOQS library, which enables testing on different systems. In terms of signature schemes supported by this library, we have Sphinx, Rainbow, Picnic, Falcon, and Dilithium. For key exchange, we have McLees, Frodo, Kyber, NTRU, Saber, and PSYCH, each offering different levels of security and efficiency. Level one serves as the baseline, providing security equivalent to 128 bits, similar to what we get with EES. Level three offers

192-bit security, while level five offers 256-bit security. Each level has its own implementation tailored to its specific security requirements. We can examine the impact of these implementations on a Raspberry Pi. However, if we consider the number of cycles more broadly, it depends heavily on the clock speed, which ultimately determines the total time taken for execution. The results are color-coded for clarity. Values between one and two times the baseline are shown in green, between two and ten in yellow, and various shades of orange indicate factors ranging from 10 to over 1000 times the baseline. Dark orange indicates a factor exceeding 1000 times the baseline.

When it comes to key generation and exchange, Bob encrypts a key, encapsulates it, and sends it to Alice. Alice then decapsulates it using her public key. These operations are crucial not only for public key encryption but also for key exchange protocols.Looking at the number of cycles per second taken by each method, Kyber stands out at the top, performing well in both encapsulation and decapsulation. It consistently receives a scoring factor of 10 in each area. Following Kyber, we have Cyber Ichiba and Saber, with a lightweight version of Saber performing decently, particularly in level one. NTRU joins alongside Saber and Kyber, showing strong performance. As we move down the list, we encounter methods like HQC and BIKE,

followed by Frodo KM. Further down, we see the isogeny-based methods making their appearance. At the end of the list, we find the code-based method of McLease. Generally, non-lattice methods seem to struggle a bit more with key generation but perform reasonably well in encapsulation and decapsulation. However, exogenous-based methods within the code base struggle significantly, with large factors indicating performance issues, especially with key sizes produced by McLease, which are much larger compared to lattice-based methods.

If we were to devise a scoring system, here's how the methods stack up: Kyber emerges as the fastest across key generation, encapsulation, and decapsulation cycles, followed by Saber, NTRU, and then BIKE, Michaelis, HQC, and so forth. Generally, it's the lattice methods that shine brightest across all aspects, with Kyber notably outperforming the rest. Now, turning our attention to key sizes, we notice something interesting: the isogeny methods excel in producing smaller key sizes for both public and cipher keys, as well as secret and shared values. Comparatively, Kyber and other methods generate larger public keys, although typically only two to three times larger than those from isogeny methods. However, their private keys tend to be substantially larger than the compressed versions produced by isogeny methods. When we delve deeper, we find that McLease and other code-based methods encounter challenges, particularly in the size of the public and private keys they generate, although the resulting cipher tends to be relatively small compared to others. Once more, across various aspects, the lattice methods

consistently show commendable performance, particularly in terms of their key size. Now, let's delve into digital signatures, including Dilithium, Falcon, and Rainbow, along with their alternative counterparts. While the evaluations for the Raspberry Pi are detailed, let's take a broader view and examine the number of cycles required. Interestingly, the hash-based zero-knowledge proof method, Picnic, emerges as the leader in key generation.

However, it falls short when it comes to signing and verification. Moving on to the hash-based methods, we observe a general struggle across key generation, signing, and verification. Similarly, the struggles persist with the hash-based methods across various aspects. However, lattice-based methods continue to perform admirably, offering a balanced compromise in key generation, signing, and verification. Regarding key sizes, we notice that the hash-based methods tend to excel. However, their signature

sizes are not as impressive. For instance, Picnic encounters challenges in signature size, and Rainbow, the multivariate method, struggles with both public and secret key sizes. Overall, each method exhibits its own set of strengths and weaknesses. However, the lattice methods appear to offer the best compromise, although they may not always produce the smallest key or signature sizes. Yet, they remain generally acceptable across all aspects. In summary, hash-based methods excel in generating small key sizes for digital signatures but tend to produce larger overall signatures. They also appear to be slower when tested on the Raspberry Pi. On the other hand, multivariate methods have larger key sizes but smaller signatures. However, they too

exhibit sluggish performance on the Raspberry Pi. In contrast, lattice methods emerge as a favorable compromise for both key and signature sizes. Among them, Dilithium stands out as the top performer for key exchange. When tested on the Raspberry Pi, Kyber demonstrates the best performance, while slower methods like NTRU and the isogeny-based methods lag in terms of key generation speed. Raspberry Pi has proven to be a valuable tool for testing the CRYSTALS-Kyber algorithm. Raspberry Pis are tiny computers. The biggest one is about the size of a deck of cards, while the smallest is

just a bit bigger than a stick of gum. You might look at one and think, "That doesn't look like any computer I've seen before!" But appearances can be deceiving. Despite its quirky looks, a Raspberry Pi has everything a regular computer has. It's got ports for connecting a monitor, keyboard, mouse, and even the internet. It's the real deal—a full-fledged computer! The only difference is,

that while most computers run Windows or Mac, Raspberry Pi runs something called Linux. Specifically, it's a version called Raspbian, made just for Raspberry Pi. But here's the thing: not all Raspberry Pis are the same. There are several versions, each with its features.

First up, we have the Pi Zero. It's the tiny one, but don't let its size fool you—it packs a punch! However, because it's so small, you might need a few extra adapters to connect everything to it. Once you get the hang of it, the Pi Zero is perfect for projects with limited space. It's the cheapest option, however, it's not as powerful as its bigger siblings. Now,

we have the Model A series, the middle child of the Raspberry Pi family, so to speak. It's not as speedy as the Model B, but it's not as tiny as the Pi Zero either. It's got a faster processor than the Pi Zero, along with a full-sized USB port, audio port, and HDMI port. The newer versions even come with built-in wireless and Bluetooth. Next up, we have the Model B series. This one's got all the bells and whistles. It's so powerful it can probably knit a Hogwarts sweater while rescuing a cat from a tree. It boasts four USB ports, a full-sized Ethernet jack, and up to four gigabytes of memory. With a quad-core

processor and support for dual monitors, it's more powerful than many laptops on the market. But here's the real magic of Raspberry Pi: the GPIO pins. These little pins allow you to send and receive electrical signals, controlled right from the operating system. They're what make Raspberry Pi so versatile and popular among makers and hobbyists alike. Being able to manipulate electrical signals allows you to control various electronic devices.

Starting with simple applications, such as LED lights, motors, buttons, switches, radio signals, audio signals, and LCDs, the possibilities are endless. You could design your mini arcade system with joysticks and buttons. The GPIO pins of the Raspberry Pi bridge the gap between computing and the physical world, offering endless creative opportunities.

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*2024-04-09 13:39*