#AI and Quantum Computing

[manmishra]

Quantum Computing and Artificial Intelligence: The Future of Technology

Discover how quantum computing is revolutionizing artificial intelligence. Learn about Quantum AI, its applications, quantum algorithms, and how it can accelerate AGI development. Explore the future of AI powered by quantum computing.

Quantum computing and artificial intelligence (AI) are two of the most revolutionary technological advancements in modern times. AI has already made significant progress using classical computers, but its potential is hindered by the computational limits of traditional computing systems. Quantum computing, with its immense processing power, is expected to drive AI into new frontiers, enabling…

[astra-ravana]

Technomancy: The Fusion Of Magick And Technology

Technomancy is a modern magickal practice that blends traditional occultism with technology, treating digital and electronic tools as conduits for energy, intent, and manifestation. It views computers, networks, and even AI as extensions of magickal workings, enabling practitioners to weave spells, conduct divination, and manipulate digital reality through intention and programming.

Core Principles of Technomancy

• Energy in Technology – Just as crystals and herbs carry energy, so do electronic devices, circuits, and digital spaces.

• Code as Sigils – Programming languages can function as modern sigils, embedding intent into digital systems.

• Information as Magick – Data, algorithms, and network manipulation serve as powerful tools for shaping reality.

• Cyber-Spiritual Connection – The internet can act as an astral realm, a collective unconscious where digital entities, egregores, and thought-forms exist.

Technomantic Tools & Practices

Here are some methods commonly utilized in technomancy. Keep in mind, however, that like the internet itself, technomancy is full of untapped potential and mystery. Take the time to really explore the possibilities.

Digital Sigil Crafting

• Instead of drawing sigils on paper, create them using design software or ASCII art.

• Hide them in code, encrypt them in images, or upload them onto decentralized networks for long-term energy storage.

• Activate them by sharing online, embedding them in file metadata, or charging them with intention.

Algorithmic Spellcasting

• Use hashtags and search engine manipulation to spread energy and intent.

• Program bots or scripts that perform repetitive, symbolic tasks in alignment with your goals.

• Employ AI as a magickal assistant to generate sigils, divine meaning, or create thought-forms.

Digital Divination

• Utilize random number generators, AI chatbots, or procedural algorithms for prophecy and guidance.

• Perform digital bibliomancy by using search engines, shuffle functions, or Wikipedia’s “random article” feature.

• Use tarot or rune apps, but enhance them with personal energy by consecrating your device.

Technomantic Servitors & Egregores

• Create digital spirits, also called cyber servitors, to automate tasks, offer guidance, or serve as protectors.

• House them in AI chatbots, coded programs, or persistent internet entities like Twitter bots.

• Feed them with interactions, data input, or periodic updates to keep them strong.

The Internet as an Astral Plane

• Consider forums, wikis, and hidden parts of the web as realms where thought-forms and entities reside.

• Use VR and AR to create sacred spaces, temples, or digital altars.

• Engage in online rituals with other practitioners, synchronizing intent across the world.

Video-game Mechanics & Design

• Use in-game spells, rituals, and sigils that reflect real-world magickal practices.

• Implement a lunar cycle or planetary influences that affect gameplay (e.g., stronger spells during a Full Moon).

• Include divination tools like tarot cards, runes, or pendulums that give randomized yet meaningful responses.

Narrative & World-Building

• Create lore based on historical and modern magickal traditions, including witches, covens, and spirits.

• Include moral and ethical decisions related to magic use, reinforcing themes of balance and intent.

• Introduce NPCs or AI-guided entities that act as guides, mentors, or deities.

Virtual Rituals & Online Covens

• Design multiplayer or single-player rituals where players can collaborate in spellcasting.

• Implement altars or digital sacred spaces where users can meditate, leave offerings, or interact with spirits.

• Create augmented reality (AR) or virtual reality (VR) experiences that mimic real-world magickal practices.

Advanced Technomancy

The fusion of technology and magick is inevitable because both are fundamentally about shaping reality through will and intent. As humanity advances, our tools evolve alongside our spiritual practices, creating new ways to harness energy, manifest desires, and interact with unseen forces. Technology expands the reach and power of magick, while magick brings intention and meaning to the rapidly evolving digital landscape. As virtual reality, AI, and quantum computing continue to develop, the boundaries between the mystical and the technological will blur even further, proving that magick is not antiquated—it is adaptive, limitless, and inherently woven into human progress.

Cybersecurity & Warding

• Protect your digital presence as you would your home: use firewalls, encryption, and protective sigils in file metadata.

• Employ mirror spells in code to reflect negative energy or hacking attempts.

• Set up automated alerts as magickal wards, detecting and warning against digital threats.

Quantum & Chaos Magic in Technomancy

• Use quantum randomness (like random.org) in divination for pure chance-based outcomes.

• Implement chaos magick principles by using memes, viral content, or trend manipulation to manifest desired changes.

AI & Machine Learning as Oracles

• Use AI chatbots (eg GPT-based tools) as divination tools, asking for symbolic or metaphorical insights.

• Train AI models on occult texts to create personalized grimoires or channeled knowledge.

• Invoke "digital deities" formed from collective online energies, memes, or data streams.

Ethical Considerations in Technomancy

• Be mindful of digital karma—what you send out into the internet has a way of coming back.

• Respect privacy and ethical hacking principles; manipulation should align with your moral code.

• Use technomancy responsibly, balancing technological integration with real-world spiritual grounding.

As technology evolves, so will technomancy. With AI, VR, and blockchain shaping new realities, magick continues to find expression in digital spaces. Whether you are coding spells, summoning cyber servitors, or using algorithms to divine the future, technomancy offers limitless possibilities for modern witches, occultists, and digital mystics alike.

"Magick is technology we have yet to fully understand—why not merge the two?"

[didmyownresearch]

Why there's no intelligence in Artificial Intelligence

You can blame it all on Turing. When Alan Turing invented his mathematical theory of computation, what he really tried to do was to construct a mechanical model for the processes actual mathematicians employ when they prove a mathematical theorem. He was greatly influenced by Kurt Gödel and his incompleteness theorems. Gödel developed a method to decode logical mathematical statements as numbers and in that way was able to manipulate these statements algebraically. After Turing managed to construct a model capable of performing any arbitrary computation process (which we now call "A Universal Turing Machine") he became convinced that he discovered the way the human mind works. This conviction quickly infected the scientific community and became so ubiquitous that for many years it was rare to find someone who argued differently, except on religious grounds.

There was a good reason for adopting the hypothesis that the mind is a computation machine. This premise was following the extremely successful paradigm stating that biology is physics (or, to be precise, biology is both physics and chemistry, and chemistry is physics), which reigned supreme over scientific research since the eighteenth century. It was already responsible for the immense progress that completely transformed modern biology, biochemistry, and medicine. Turing seemed to supply a solution, within this theoretical framework, for the last large piece in the puzzle. There was now a purely mechanistic model for the way brain operation yields all the complex repertoire of human (and animal) behavior.

Obviously, not every computation machine is capable of intelligent conscious thought. So, where do we draw the line? For instance, at what point can we say that a program running on a computer understands English? Turing provided a purely behavioristic test: a computation understands a language if by conversing with it we cannot distinguish it from a human.

This is quite a silly test, really. It doesn't provide any clue as to what actually happens within the artificial "mind"; it assumes that the external behavior of an entity completely encapsulates its internal state; it requires "man in the loop" to provide the final ruling; it does not state for how long and on what level should this conversation be held. Such a test may serve as a pragmatic common-sense method to filter out obvious failures, but it brings us not an ounce closer to understanding conscious thinking.

Still, the Turing Test stuck. If anyone tried to question the computational model of the mind, he was then confronted with the unavoidable question: what else can it be? After all, biology is physics, and therefore the brain is just a physical machine. Physics is governed by equations, which are all, in theory, computable (at least approximately, with errors being as small as one wishes). So, short of conjuring supernatural soul that magically produces a conscious mind out of biological matter, there can be no other solution.

Nevertheless, not everyone conformed to the new dogma. There were two tiers of reservations to computational Artificial Intelligence. The first, maintained, for example, by the Philosopher John Searl, didn't object to idea that a computation device may, in principle, emulate any human intellectual capability. However, claimed Searl, a simulation of a conscious mind is not conscious in itself.

To demonstrate this point Searl envisioned a person who doesn't know a single word in Chinese, sitting in a secluded room. He receives Chinese texts from the outside through a small window and is expected to return responses in Chinese. To do that he uses written manuals that contain the AI algorithm which incorporates a comprehensive understanding of the Chinese language. Therefore, a person fluent in Chinese that converses with the "room" shall deduce, based on Turing Test, that it understands the language. However, in fact there's no one there but a man using a printed recipe to convert an input message he doesn't understands to an output message he doesn't understands. So, who in the room understands Chinese?

The next tier of opposition to computationalism was maintained by the renowned physicist and mathematician Roger Penrose, claiming that the mind has capabilities which no computational process can reproduce. Penrose considered a computational process that imitates a human mathematician. It analyses mathematical conjecture of a certain type and tries to deduce the answer to that problem. To arrive at a correct answer the process must employ valid logical inferences. The quality of such computerized mathematician is measured by the scope of problems it can solve.

What Penrose proved is that such a process can never verify in any logically valid way that its own processing procedures represent valid logical deductions. In fact, if it assumes, as part of its knowledge base, that its own operations are necessarily logically valid, then this assumption makes them invalid. In other words, a computational machine cannot be simultaneously logically rigorous and aware of being logically rigorous.

A human mathematician, on the other hand, is aware of his mental processes and can verify for himself that he is making correct deductions. This is actually an essential part of his profession. It follows that, at least with respect to mathematicians, cognitive functions cannot be replicated computationally.

Neither Searl's position nor Penrose's was accepted by the mainstream, mainly because, if not computation, "what else can it be?". Penrose's suggestion that mental processes involve quantum effects was rejected offhandedly, as "trying to explicate one mystery by swapping it with another mystery". And the macroscopic hot, noisy brain seemed a very implausible place to look for quantum phenomena, which typically occur in microscopic, cold and isolated systems.

Fast forward several decades. Finaly, it seemed as though the vision of true Artificial Intelligence technology started bearing fruits. A class of algorithms termed Deep Neural Networks (DNN) achieved, at last, some human-like capabilities. It managed to identify specific objects in pictures and videos, generate photorealistic images, translate voice to text, and support a wide variety of other pattern recognition and generation tasks. Most impressively, it seemed to have mastered natural language and could partake in an advanced discourse. The triumph of computational AI appeared more feasible than ever. Or was it?   

During my years as undergraduate and graduate student I sometimes met fellow students who, at first impression, appeared to be far more conversant in the academic courses subject matter than me. They were highly confident and knew a great deal about things that were only briefly discussed in lectures. Therefore, I was vastly surprised when it turned out they were not particularly good students, and that they usually scored worse than me in the exams. It took me some time to realize that these people hadn't really possessed a better understanding of the curricula. They just adopted the correct jargon, employed the right words, so that, to the layperson ears, they had sounded as if they knew what they were talking about.

I was reminded of these charlatans when I encountered natural language AIs such as Chat GPT. At first glance, their conversational abilities seem impressive – fluent, elegant and decisive. Their style is perfect. However, as you delve deeper, you encounter all kinds of weird assertions and even completely bogus statements, uttered with absolute confidence. Whenever their knowledge base is incomplete, they just fill the gap with fictional "facts". And they can't distinguish between different levels of source credibility. They're like Idiot Savants – superficially bright, inherently stupid.

What confuses so many people with regard to AIs is that they seem to pass the (purely behavioristic) Turing Test. But behaviorism is a fundamentally non-scientific viewpoint. At the core, computational AIs are nothing but algorithms that generates a large number of statistical heuristics from enormous data sets.

There is an old anecdote about a classification AI that was supposed to distinguish between friendly and enemy tanks. Although the AI performed well with respect to the database, it failed miserably in field tests. Finely, the developers figured out the source of the problem. Most of the friendly tanks' images in the database were taken during good weather and with fine lighting conditions. The enemy tanks were mostly photographed in cloudy, darker weather. The AI simply learned to identify the environmental condition.

Though this specific anecdote is probably an urban legend, it illustrates the fact that AIs don't really know what they're doing. Therefore, attributing intelligence to Arificial Intelligence algorithms is a misconception. Intelligence is not the application of a complicated recipe to data. Rather, it is a self-critical analysis that generates meaning from input. Moreover, because intelligence requires not only understanding of the data and its internal structure, but also inner-understanding of the thought processes that generate this understanding, as well as an inner-understanding of this inner-understanding (and so forth), it can never be implemented using a finite set of rules. There is something of the infinite in true intelligence and in any type of conscious thought.

But, if not computation, "what else can it be?". The substantial progress made in quantum theory and quantum computation revived the old hypothesis by Penrose that the working of the mind is tightly coupled to the quantum nature of the brain. What had been previously regarded as esoteric and outlandish suddenly became, in light of recent advancements, a relevant option.

During the last thirty years, quantum computation has been transformed from a rather abstract idea made by the physicist Richard Feynman into an operational technology. Several quantum algorithms were shown to have a fundamental advantage over any corresponding classical algorithm. Some tasks that are extremely hard to fulfil through standard computation (for example, factorization of integers to primes) are easy to achieve quantum mechanically. Note that this difference between hard and easy is qualitative rather than quantitative. It's independent of which hardware and how much resources we dedicate to such tasks.

Along with the advancements in quantum computation came a surging realization that quantum theory is still an incomplete description of nature, and that many quantum effects cannot be really resolved form a conventional materialistic viewpoint. This understanding was first formalized by John Stewart Bell in the 1960s and later on expanded by many other physicists. It is now clear that by accepting quantum mechanics, we have to abandon at least some deep-rooted philosophical perceptions. And it became even more conceivable that any comprehensive understanding of the physical world should incorporate a theory of the mind that experiences it. It's only stands to reason that, if the human mind is an essential component of a complete quantum theory, then the quantum is an essential component of the workings of the mind. If that's the case, then it's clear that a classical algorithm, sophisticated as it may be, can never achieve true intelligence. It lacks an essential physical ingredient that is vital for conscious, intelligent thinking. Trying to simulate such thinking computationally is like trying to build a Perpetuum Mobile or chemically transmute lead into gold. You might discover all sorts of useful things along the way, but you would never reach your intended goal. Computational AIs shall never gain true intelligence. In that respect, this technology is a dead end.

[cslearninghub]

Quantum Computing 101: What are Qubits?

Curious about quantum computing? Let's break it down!

🔍 What’s a Qubit? A qubit is the basic unit of quantum information. Unlike classical bits (0 or 1), qubits can be 0, 1, or both at the same time thanks to a phenomenon called superposition.

✨ Why Is This Cool?

Superposition: Allows qubits to explore many possibilities simultaneously.

Entanglement: Qubits can be linked, so the state of one affects the state of another, no matter the distance.

⚙️ In Action: This means quantum computers can tackle complex problems faster by processing a huge number of possibilities at once!

Follow for more insights on the future of tech! 🚀✨

Instagram: cs_learninghub YT: CS Learning Hub

[diablo1776]

Fucking Skynet once they load AI on it if they haven't already.

[youare-number6]

How to Make - Operate -- Quantum Computers Pt 1

Futuristic Quantum Computing with instructions!

[comparativetarot]

The Star. Art by Suzanne Treister, from HEXEN 2.0.

Quantum Computing - AI

[89hitokiri]

黒影 (Kurokage)

"In the depths of silence, we find the power to reshape the world. We are KuroKage. We will prevail."

[t-jfh]

(Illustration: Nicholas Law)

(Illustration: Nicholas Law)

(Illustration: Nicholas Law)

The Quantum Apocalypse Is Coming. Be Very Afraid

What happens when quantum computers can finally crack encryption and break into the world’s best-kept secrets? It’s called Q-Day — the worst holiday maybe ever.

ONE DAY SOON, at a research lab near Santa Barbara or Seattle or a secret facility in the Chinese mountains, it will begin: the sudden unlocking of the world’s secrets. Your secrets.

Cybersecurity analysts call this Q-Day — the day someone builds a quantum computer that can crack the most widely used forms of encryption. These math problems have kept humanity’s intimate data safe for decades, but on Q-Day, everything could become vulnerable, for everyone: emails, text messages, anonymous posts, location histories, bitcoin wallets, police reports, hospital records, power stations, the entire global financial system.

By Amit Katwala

WIRED magazine May/June 2025 - Level Up

The Frontiers of Computing Issue

Shared from Apple News - March 24, 2025

Post-quantum algorithms. thermodynamic hardware, open source architectures. apocalypse-proof programming, and more: WIRED journeys to the freaky frontiers of modern computing.

WIRED The Frontiers of Computing Issue

• The Quantum Apocalypse Is Coming. Be Very Afraid

• Hot New Thermodynamic Chips Could Trump Classical Computers

• The Weight of the Internet Will Shock You

• How Software Engineers Actually Use AI

• Quantum Computing Is Dead. Long Live Quantum Computing!

•

[frank-olivier]

The Topological Advantage: How Anyons Are Changing Quantum Computing

The field of quantum computing has experienced a significant paradigm shift in recent years, with the emergence of topological quantum computing as a promising approach to building practical quantum computers. At the heart of this new paradigm is the concept of anyons, quasiparticles that exhibit non-Abelian statistics in two-dimensional spaces. First proposed by physicist Frank Wilczek in 1982, anyons have been extensively studied and experimentally confirmed in various systems.

The discovery of anyons and their unique properties has opened up new avenues for quantum computing, enabling the development of fault-tolerant quantum gates and scalable quantum systems. The topological properties of anyons make them well-suited for creating stable qubits, the fundamental units of quantum information. The robustness of these qubits stems from their topological characteristics, which are less susceptible to errors caused by environmental disturbances.

One of the most significant advantages of topological quantum computing is its inherent error resistance. The robust nature of anyonic systems minimizes sensitivity to local perturbations, reducing the need for complex error correction codes and facilitating scalability. Michael Freedman and colleagues first demonstrated this concept in 2003, and it has since been extensively studied.

The manipulation of anyons through braiding, where anyons are moved around each other in specific patterns, implements quantum gates that are inherently fault-tolerant. This concept was first introduced by Alexei Kitaev in 1997, and has since been extensively studied. The topological nature of braiding ensures that operations are resistant to errors, as they rely only on the topology of the braiding path, not its precise details.

Topological quantum computing has far-reaching potential applications, with significant implications for cryptography, material science, and quantum simulations. Topological quantum computing enables enhanced security protocols, insights into novel states of matter, and more efficient simulations of complex quantum systems.

Prof. Steve Simon: Topological Quantum Computing (University of Waterloo, June 2012)

Part 1

Part 2

Tuesday, October 8, 2024

[nunuslab24]

Understanding The World of Quantum Computers

Imagine a computer so powerful that it could solve problems in seconds that would take our current machines millions of years. No, it's not science fiction—it's the exciting world of quantum computing, where bits become qubits and the impossible becomes possible. Let's dive into this technological marvel that might one day be as common as your smartphone!

A quantum computer is a supercomputer that exploits quantum mechanical phenomena or in other words, a quantum computer uses tiny particles to perform complex calculations. Unlike regular computers, quantum computers use qubits instead of bits!

A qubit means that it is either neither 0 or 1, think of it as a wave; it can go up and down at any given moment! This ability to be in multiple states simultaneously is known as superposition. At the same time, a bit in a classical computer is like a simple switch that can be either off (0) or on (1), a qubit can be both off and on simultaneously, providing an incredible amount of computational power. But how do they really work?

How Quantum Computers Actually Work

Superposition: As mentioned, qubits can exist in multiple states at once. This allows quantum computers to process a vast amount of information simultaneously.

Entanglement: This is a phenomenon where qubits become intertwined, so the state of one qubit can depend on another, no matter how far apart they are. This can massively increase computational power.

Quantum Gates: Similarto logic gates (a device that acts as a building block for digital circuits) in classical computers, quantum gates manipulate qubits. but because of superposition and entanglement, quantum gates can perform complex operations much faster than classical gates (smartphones, tablets, etc).

What Do Quantum Computers Look Like?

Unlike the sleek laptops and smartphones we use today, quantum computers look very different. They are usually large (5ft wide & 20ft long), complex machines housed in specialized laboratories. A typical quantum computer setup includes:

Cryogenic Systems: Quantum computers need extremely low temperatures to function, often close to absolute zero (kelvin or -273.15 degrees Celsius or -460 degrees Fahrenheit). This requires sophisticated cooling systems.

Quantum Processor: The heart of a quantum computer, where qubits are manipulated.

Control Systems: These are used to manage and operate the quantum processor, often involving complex electronics and software.

In other words, quantum computers are not something you can slip into your pocket or place on your desk. They currently require a highly controlled environment and are far from being household items.

Why Does This Matter?

The potential of quantum computers is amazing. Here are a few areas where they could make a significant impact:

Cryptography: Quantum computers could break current encryption methods, making our data vulnerable. However, they could also create unbreakable encryption.

Drug (Health) Discovery: They can simulate molecular structures much more efficiently than classical computers, speeding up the process of drug discovery and development.

Optimization: Quantum computers can solve complex optimization problems that are currently unsolvable, impacting industries from logistics to finance.

Pros and Cons of Quantum Computers:

Pros:

Speed: Quantum computers can solve problems in seconds that would take classical computers millions of years.

Power: Their ability to handle complex calculations could revolutionize fields like cryptography, material science, and artificial intelligence (AI).

Innovation: They could lead to new discoveries and advancements in technology that we can’t even imagine yet.

Cons:

Complexity: Quantum computers are incredibly complex and difficult to build and maintain.

Cost: The technology is expensive and currently out of reach for most organizations.

Security Risks: The potential to break current encryption methods poses a significant security threat.

Will We Ever Have Quantum Computers in Our Homes?

Given their current state, quantum computers are unlikely to become household items anytime soon. The technology is still in its infancy, and the machines are expensive and complex. However, as research progresses and technology advances, it’s possible that we could see more accessible forms of quantum computing in the future.

For now, the most practical application for everyday users will likely come through cloud-based quantum computing services provided by tech companies. This means you could potentially access the power of a quantum computer over the internet, without having to own one.

Quantum computers represent a leap forward in computing technology, with the potential to transform numerous fields and solve problems that are currently intractable. However, they also come with significant challenges and risks. As this technology develops, it will be crucial to balance its immense potential with the necessary safeguards to ensure it benefits humanity as a whole.

[aikoto-otokia]

4D Hypercube Created when I was 16 Lost Jedi Productions https://www.youtube.com/channel/UCJjuggclDEHRQ09vXG61ulw

The Resurrection

[gesundheitszentrum]

Paradise

[youare-number6]

How to Make - Operate -- Quantum Computers Pt 2

Image prompt driven AI images of futuristic Quantum computers