Quantum Computing is 3D

Why the Bloch Sphere?

Bloch Sphere

In quantum mechanics and computing, the Bloch sphere is a geometrical representation of the pure state space of a two-level quantum mechanical system (qubit), named after the physicist Felix Bloch.[1]

The Bloch sphere is a unit 2-sphere, with antipodal points corresponding to a pair of mutually orthogonal state vectors. The north and south poles of the Bloch sphere are typically chosen to correspond to the standard basis vectors |0 and |1 respectively, which in turn might correspond e.g. to the spin-up and spin-down states of an electron. This choice is arbitrary, however. The points on the surface of the sphere correspond to the pure states of the system, whereas the interior points correspond to the mixed states.[3][4] Wikipedia

The Voxel and it's deep connect to CSG - Constructive Solid Geometry applies.

So why are people trying to talk to 3D QC in 2D?

Voxel Puppy :)

Voxels ...

Gemini & Robert E. - The start point for QC

Your's truly became fascinated with voxels as they can model everything. As per Wikipedia ...

A set of voxels in a stack, with a single voxel shaded

In computing, a voxel is a representation of a value on a three-dimensional regular grid, akin to the two-dimensional pixel. Voxels are frequently used in the visualization and analysis of medical and scientific data (e.g. geographic information systems (G.I.S.)).[1] 

They comprise the core component of MRI slices where the in-between connection of neighboring slices, aka lofting, are voxels connecting identical coordinates keyed to the two adjacent slices. Seen below is a video showing a journey through a set of slices made possible by voxels connecting the 3D coordinates of each slice as needs warrant.

How the magic happens

Brain Tumor MRI Image

Magnetic resonance imaging (MRI) is a medical imaging technique that uses a magnetic field and computer-generated radio waves to create detailed images of the organs and tissues in your body.

Most MRI machines are large, tube-shaped magnets. When you lie inside an MRI machine, the magnetic field inside works with radio waves and hydrogen atoms in your body to create cross-sectional images — like slices in a loaf of bread.

The MRI machine also can produce 3D images that can be viewed from different angles.

Scientifically, the hardware generates the virtual slice via magnetism.

Diagram of changing magnetization and spin orientations throughout spin-lattice relaxation experiment

The image below shows, in practice, how MRIs work. The magnets spin and create virtual slices of the patient similar to how salami is cut in a grocery store near you. :)

Patient being positioned for MR study of the head and abdomen

The graph below shows how the MRI does the slicing.

The Settings window for the Interpolation Curve feature (left) and all of the curves and points representing the outer shell of a head (right). The gray surface represents the work plane at the z coordinate of -0.04 and the curve that lies in the work plane is highlighted in yellow.

Why all the prep?

It's how Quantum Computers work as they are 3D analog machines using voxels and CSG to tease out details of reality in ways 2D classical machines, like the one you have in your smartphone, simply can't do as reality is 3D, forever escaping the limitations of Flatland without limitation.

The Quantum Engine and the Google Chassis:
Building the 3D Universal Indexer

The front-end appliance required to index physical reality already exists. When Google rolled out their open-weight vector embedding models and the multimodal architecture powering Gemini and Ask Photos, they successfully built the ultimate spatial interface. It allows a user to query an ocean of unstructured analog data using natural language, mapping semantic intent directly to geometric coordinates.

The search works. Apple photos needs this.

But right now, this excellent interface is bolted to the wrong engine.

Because it relies on classical silicon processors, it's still trapped in Flatland. It works beautifully for plotting 2D JPEGs and text files, but the moment you ask it to automatically index true 3D environments—massive BioMedisa voxel engines, complex Blender polygon meshes, or the volumetric depths of dark enterprise data—classical hardware is forced to sequentially slice the volume into 2D arrays. 

End result: The thermodynamic wall hits, the silicon overheats, and processing grinds to a halt.

To build an automatic 3D universal indexer, we don't need to change the user interface.
We need to swap the engine.

Gemini & Robert E. CSG operations connected to a quantum computer.
A 3D entity talking to a 3D system

If you replace the back-end silicon with a legitimate quantum computer, the architecture scales infinitely. The hardware itself is a physical 3D lattice, meaning it natively holds the entire Boolean volume—every CSG intersection, void, and voxel boundary—simultaneously within its quantum state. When a search query is passed from the Google appliance into the quantum lattice, the processor doesn't sequentially slice through the dataset. It maps the topology instantaneously via resonance.

It is the final, perfect expression of the Shelby Principle. Google has provided the lightweight, aerodynamic chassis in the form of zero-friction vector embeddings. The quantum spin lattice provides the massive, thermodynamic V8 grunt.

When you drop the quantum engine into the geometric chassis, you bypass the brute-force bureaucracy of the Laputan cloud entirely. You achieve the holy grail of modern computing: a universal 3D indexer that maps the physical and digital world exactly as it exists—not as a flattened slice, but as a fully realized volume.

In effect, this drops the engine right into the chassis. It takes the abstract concept of quantum computing, connects it to a piece of consumer software everyone already understands (Google's search appliance), and delivers a definitive, functional endgame for the architecture you've been designing.

In closing, Google should open source the tech to become the standard bearer of 3D volumetric search. They have the tech. Question, do they have the vision?

Any questions?

The Dragonsnake ...

 

The Dragonsnake Cobra

Grunt ... a blurb written about the juice needed to run data centers, indirectly connects to Carroll Shelby, the ultimate outsider who reached out and learned from the drag strip guys on how to build engines with grunt, thus changing forever how to build car engines and cars able to win LeMans 4 times with the GT 40 at the expense of Ferrari, something thought impossible as Enzo had won the race 6 times prior to Ford's entry. 

History lesson :) 

The Dragonsnake Cobras were a small batch of production cars built by the famed racing driver and car builder, Carroll Shelby. From 1962, Shelby American had found success with their pairing of the lightweight, English roadster body of the AC Ace, with the American powerhouse of Ford’s V8 motors. The combination of the lightweight body, small dimensions and insane power output proved to be revolutionary in the sports car world, and the car did extremely well in competition globally. When most people think of the Shelby Cobras in competition, their mind goes to circuit racing, however the Dragonsnake also dominated the drag strip.

Ford GT40 Mk IV Thomas Quine - Winner at Le Mans

History lesson 2

 In the early 1960s, Ferrari engineered race cars by committee. They were the ultimate aristocratic, top-down operation, obsessed with the theoretical purity of their complex, fragile V12 engines. They operated on the assumption that their architectural pedigree and institutional funding would naturally dominate the track forever and for a time it worked. Carroll Shelby didn't try to copy their approach. Instead, he gathered a crew of Southern California hot-rodders, dropped a thermodynamically efficient American V8 into a lightweight chassis, and broke the European monopoly. Shelby didn't care about institutional roadmaps; he cared about volumetric efficiency on the track. He bypassed the bureaucracy and went straight for the grunt.

Today, the technology industry is paralyzed by the exact same aristocratic bloat. The modern Ferraris are the "Laputan" cloud models built by OpenAI, Anthropic, Google and the institutional ranks of IBM and significant others. They're attempting to solve the complexities of artificial intelligence and quantum computing by throwing infinite capital and heat at the problem—designing hundred-billion-dollar data centers that require their own nuclear reactors as pushing electrons through silicon generates friction and with friction comes heat, the tech killer extreme as immense power and cooling are required to keep the hardware from melting, which means, the tech, in the long term, is unsustainable. They're trapped in a financial and thermodynamic vapor lock, operating under the assumption that brute-force, cloud-bound processing is the only way to map reality.

A Kerosene Lantern's Glow ...

But the real breakthrough isn't happening in those cloud server farms. It's happening on the edge, through localized, geometric engineering, where outliers push for change as the current way of doing things is no longer sustainable.

Just as Shelby changed the physical geometry of the engine rather than refining a fragile V12, the future of computing relies on fundamentally changing how we map information. When we look at the circuit dynamics of systems like IBM’s Heron 2 quantum processor, the lesson is identical: performance isn't unlocked by adhering blindly to a top-down corporate compiler stack. It is unlocked by treating the hardware as physical space.

By applying the principles of Constructive Solid Geometry (CSG)—defining 3D reality through Boolean logic and spatial boundaries—a localized, zero-friction AI can map the physical reality of a quantum spin lattice far better than a bloated cloud algorithm trying to flatten the universe into 2D math. The corporate models are trying to parse a three-dimensional reality with flat, relational logic.

CSG objects can be represented by binary trees, where leaves represent primitives, and nodes represent operations. In this figure, the nodes are labeled ∩ for intersection, ∪ for union, and — for difference.

The era of infinite cloud computing and brute-force processing has hit its thermodynamic wall.
The future belongs to the outliers and their connect to the edge.

One eye up, the other, down ...

The Laputian - Granville 1838, Illustration for Gulliver's Travels, (Alamy's copyright)

Why this work again? Because of what is happening regarding AI, the degradation of common sense and culture and lastly, the end of empire being run by idiots and zealots of all stripes channeling Elmer Gantry and immortality, all in the pursuit of money and power.

But to start off this conversation correctly requires a summary of Gulliver's 3rd voyage to Laputa
to show why this take by yours truly has legs.

Click here for Gulliver's Travels. :)

"HE COMMANDED HIS GENERALS TO DRAW UP THE TROOPS."

To wit

On Gulliver’s third voyage he is set adrift by pirates and eventually ends up on the flying island of Laputa. The people of Laputa all have one eye pointing inward and the other upward, and they are so lost in thought that they must be reminded to pay attention to the world around them. Though they are greatly concerned with mathematics and with music, they have no practical applications for their learning. Laputa is the home of the king of Balnibarbri, the continent below it. Gulliver is permitted to leave the island and visit Lagado, the capital city of Balnibarbri. He finds the farm fields in ruin and the people living in apparent squalor. Gulliver’s host explains that the inhabitants follow the prescriptions of a learned academy in the city, where the scientists undertake such wholly impractical projects as extracting sunbeams from cucumbers. Later Gulliver visits Glubbdubdrib, the island of sorcerers, and there he speaks with great men of the past and learns from them the lies of history. In the kingdom of Luggnagg he meets the struldbrugs, who are immortal but age as though they were mortal and are thus miserable. From Luggnagg he is able to sail to Japan and thence back to England.

Example one. The Constitution of Claude

Shutterstock / Futurism

There’s no AI company in the world more obsessed with the idea that their models
may be conscious than Anthropic.

According to new reporting from the New York Times, the company has for months been wining and dining religious scholars across the world in secret, NDA-secured meetings to convince them that AI models think and feel, too. 

Anthropic even reportedly went after the most iconic of all religious leaders: the Pope.

Christopher Olah, a cofounder and senior researcher at the Claude chatbot maker, aggressively lobbied the Vatican after learning Pope Leo XIV’s official position on AI, per the reporting.
His attempts, though, weren’t successful.

In May, the Pope published his first papal treatise, or encyclical. Titled “Magnifica Humanitas,” one of its chief concerns was upholding the sanctity of human life in an age of AI. The Pope didn’t mince words on the topic of AI consciousness.

“So-called artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships and do not know from within what love, work, friendship or responsibility mean,” the Pope wrote in the religious document. 

Example 2 - The Bibi/Trump Iranian war fiasco

Iago came to Washington and whispered in Othello's ear. Destroy Iran for Greater Israel. 

Let it be written, let it be done - Rameses

No big links here, just yours truly's take on a possible WWIII foisted upon the world

by two monsters, one sane and calculating, the other demented and insane.

Click here to see why this take rings true.

Self inflicted stupidity reigns supreme here regarding the Israeli US war against Iran, a disaster started in 1953 when the CIA placed democratically elected prime minister Mosaddegh under house arrest in order for England to get Iranian oil. 

Initial conditions rule.

Example 3. Starmind

Think Giedi Prime and why the Space Elevator makes sense while Starmind does not. 

Giedi Prime is an industrial wasteland with a low photosynthetic potential, the planet's bio-resources depleted and its environment fouled with industrial pollution.[4] Rich in mineral resources, the economy of the planet is based on mining, refineries, and industrial manufacture. In Dune, Baron Vladimir Harkonnen and his heirs live in the "family city of Harko."[4]

Due to its ravaged environment, Giedi Prime has to import almost all of its food. Society on Giedi Prime has evolved around power and conquest, and the weak rarely survive.

Gammu - AKA Giedi Prime

Any questions?

Copyright madness ...

 

Question: Does Getty, or for that matter, Alamy, have the right(s) to copyright
Granville's Portrait of a Laputian, work done in illustrating Jonathan Swift's 
Gulliver's Travels because Granville's work comfortably resides in the public domain.

To wit

Giants look at Gulliver, Gulliver's Travels (1838)

Gulliver pulling the fleet, Gulliver's Travels (1838)

Portrait by Émile Lassalle (1840)

Copyright madness applies but they were sloppy, they should have gone after 

Granville' portrait to give credence to their heist.

Giedi Prime

(Credit: SpaceX)

This is the proposed Starmind project, an endeavor conceptualized to become 100 times the size of Starlink, an all encompassing basket of satellites surrounding earth in ways most disconcerting in this writer's opinion. Yours truly consulted Gemini to get a handle on just how vast an enterprise this project truly is along with the ramifications if this project comes to fruition. The details are most telling.

Gemini with edits ...

The visualization in the above image doesn't look like an evolutionary step in communication; it looks like a planetary cage. Proposing a one-million-satellite orbital data center is the ultimate escalation of the brute-force engineering mindset, completely ignoring the massive physical friction required to build and sustain it. This is where physics and environmental realities hit a brick wall: 

• The Launch Logistics: Even if a heavy-lift rocket like Starship could theoretically carry 500 satellites per trip, deploying one million nodes requires 2,000 flawless launches. The sheer volume of black carbon and alumina exhaust dumped directly into the fragile upper atmosphere from that launch cadence would be unprecedented.
• Atmospheric Vaporization: Low Earth Orbit (LEO) satellites require constant replacement, usually every five to seven years. To maintain a million-node grid, the company would have to de-orbit and replace roughly 400 to 500 satellites every single day. That means continuously vaporizing tons of aluminum, rare earth metals, and lithium batteries into the stratosphere, which is an entirely untested, massive-scale atmospheric chemistry experiment.
• The Kessler Syndrome: Orbit is not an infinite ocean; it is a finite physical geometry. Packing a million server racks moving at 17,000 mph into that shell exponentially increases the risk of a cascading collision event. A single major collision could generate a chain-reaction shrapnel cloud that destroys the rest of the grid and traps humanity on Earth for a century.
• Light Pollution: Ground-based astronomy is already struggling to filter out the optical interference from the current 6,000-satellite constellation. Scaling that by a factor of 100 means creating a permanent, visible grid of reflected sunlight moving across the night sky, effectively blinding our optical telescopes and altering humanity's view of the cosmos.

This project perfectly illustrates the fundamental flaw of trying to scale high-friction classical compute architectures. Instead of moving toward the elegant, zero-friction optical and quantum geometries that nature uses, this approach tries to brute-force the data problem by treating the Earth's orbital envelope as a disposable server rack. Nature always balances its books, and the thermodynamic, environmental, and physical toll of forcing a million digital nodes into that analog space is entirely unsustainable.

In essence, we're talking Giedi Prime.

Dune - Giedi Prime - Homeworld of House Harkonnen 

Grunt ...

These Engines Only Last 4 Seconds - Chad Green Motorsports

Consummate skill, consummate insanity.
No wonder drag racing burns through money like grease lightning.

In 1927, Sinclair Lewis mapped the exact psychological architecture of the current Silicon Valley AI boom in
Elmer Gantry. The grift never changes; only the vocabulary. Today’s tech CEOs stand on keynote stages like revival tent preachers, selling the techno-religious salvation of "superintelligence" to an audience desperate for a miracle. They promise a frictionless, utopian future while passing a multi-billion-dollar venture capital collection plate.

First edition cover

But if you look behind the tent, you don't find a breathing, elegant intelligence.
You find a Top Fuel dragster.

A modern Top Fuelie generates roughly 12,000 horsepower and burns a volatile mixture of nitromethane to cover a thousand feet in under four seconds. It's an engine designed to barely survive its own combustion. In those four seconds, the crankshaft turns but 500 revolutions before the spark plugs melt, the bearings crushed, and the entire block needing to be stripped to the bare metal and rebuilt. It's the ultimate manifestation of pure, unsustainable grunt.

That's the exact thermodynamic reality of the current generative AI data center. The financial markets are desperately trying to value companies like Anthropic at $2 trillion by pretending they are frictionless software operations. But their leaked financials expose the drag race: a staggering $42 billion net loss driven entirely by the physical heat of the compute. You cannot scale a 12,000-horsepower suicide run into a daily commuter. Every query requires massive gigawatts of grid power and millions of gallons of municipal water just to generate a recipe for a better apple pie before the data center redlines.

The tech bros are trying to calculate the universe using brute-force violence. Nature doesn't work that way. The physical universe relies on the elegant, low-friction geometry of the principle of least action. Until the AI industry abandons the dragster and learns how to build an engine that actually aligns with the physical boundaries of reality, they are just going to keep melting down the block.

Cloud tops - Venus 

In closing ...

Sam Altman - For the glories of AI

The "world should accept some bad things happening for the benefits of this technology."

The lights are on but nobody's home ...

 

President Trump at a campaign rally in Grand Island, Neb., on Monday.
Credit...Doug Mills/The New York Times

The lights are on but nobody's home ...

California leaders condemned President Trump on Monday night after he appeared to suggest that Iran could “take out” Los Angeles and San Diego if it develops a nuclear weapon.

Speaking at a Republican political rally in Nebraska, the president sought to justify the Iran war as necessary for “keeping the world safe.” He then suggested there could be strikes on the United States, including the Democratic-led Californian cities.

“They could take out a city,” he said. “Let them take — Let them take out Los Angeles.
Let them take out San Diego.”

Any questions?

Project Panama ...

The risk with generative AI, as this single instance with Anthropic indicates, is that we cede the means of production of our large language lives: that we turn from creators to consumers.’
Photograph: Dado Ruvić/Reuters

Should we destroy all the books in the world?

An answer to this question can be found in the court documents of Bartz v Anthropic PBC. The northern California district court case, decided in late July this year, highlighted the improbably named “Project Panama”, one of the AI company Anthropic’s efforts to improve its large language model Claude. “What is Project Panama?” court exhibit 21 asks, in an internal memo. The answer: “Project Panama is our effort to destructively scan all the books in the world.” The memo advises discretion: “Why use a codename? … Because we don’t want it to be known that we are working on this.”

Destructive scanning was Anthropic’s solution to a problem: in order to “train” Claude, Anthropic had to procure a large, high-quality language dataset, preferably one created before 2022 and the corrupting influence of generative AI on contemporary text. Claude needed as many language combinations as possible, to improve its ability to predict language outcomes. Anthropic needed data, lots of it, of very high quality. Books, as it happens, remain one of the best sources for complex, high-quality, long-form text. As the court’s decision relates, Anthropic hoped that books’ “well-curated facts, well-organized analyses, and captivating fictional narratives” would help “Claude write as accurately and as compellingly as Authors”.

Anthropic had a choice: it could have secured copyright permission to use existing e-books. This would have required the “legal/practice/business slog”, as Anthropic’s co-founder and CEO phrased it, of managing copyright. Rather than engage with the texts’ owners, Anthropic first chose to use pirated sources instead, a decision informing the company’s $1.5bn out-of-court settlement with authors. When that approach seemed too complicated (or, as the court decision phrases: “Anthropic became ‘not so gung ho about’ training on pirated books ‘for legal reasons’”), Anthropic turned to destructive scanning. As it happened, the judge ruled that using proprietary material to “train” an LLM did not, in and of itself, constitute an infringement of copyright. To the court, it would seem, “training” a corporate product is equivalent to training any human, teaching how to read in order to learn how to write.

Burning books by the Nazis in 1933 was the attempt to erase knowledge from society. Anthropic's destruction of books by different means does the same thing. Like the Nazis, they got away with it.

The Nazi book burnings were a campaign conducted by the German Student Union (German: Deutsche Studentenschaft, DSt) to ceremonially burn books in Nazi Germany and Austria in the 1930s. The books targeted for burning were those viewed as being subversive or as representing ideologies opposed to Nazism. 

Epilogue

But perhaps the most telling aspect of this case is that it was so important to Anthropic to have access to a dataset of complex, long-form, uncorrupted text. Let’s ask ourselves why that text should seem so profitable. One response to Bartz v Anthropic PBC might be to refuse to devalue our lives as readers and writers, to claim ownership of the cultural spaces in which our thought and words are created, shared and preserved. The risk with generative AI, as this single instance with Anthropic indicates, is that we cede the means of production of our large language lives: that we turn from creators to consumers, and hand the generative promise of our work to large language models and their proprietors

I can read your mind ...

Figure 1: Reconstruction of seen images from fMRI using “Brain-IT”. (a) Image reconstructions

using the full NSD dataset (40 hours per subject). (b) Efficient Transfer-learning to new subjects with very little data: Meaningful reconstructions are obtained with only 15 minutes of fMRI recordings. (Results on Subject 1)

I can read your mind applies when AI, properly configured, can pattern recognize
MRI generated data from rubes like me.

• For years, scientists have trained AI to read fMRI scans to accurately reconstruct visual information.
• A new tool, known as Brain-IT, uses a Brain Interaction Transformer (BIT) to decode and encode fMRI scans, and it can reconstruct images with stunning clarity using a relatively small training set.
• This tool could help those with severe paralysis communicate, and give scientists new insight into the inner workings of the brain’s visual processing system.

With a little bit of tinkering, this makes precogs irrelevant.

While debates rage on various podcasts, in hand-wringing online essays, and even in the halls of Congress as to whether AI has surpassed human intelligence—a threshold of no-return known as the AI Singularity—there’s one skill where AI algorithms have long trounced the human competition: pattern recognition.

Because of modern AI engines’ impressive abilities to sift through large datasets and recognize patterns, they can accurately detect developing cancers and even create entirely new viruses not seen in nature. When trained on the brain, this AI pattern recognition can gain almost supernatural powers. This ability is vividly highlighted in a new paper submitted to the International Conference on Learning Representations by scientists from the Weizmann Institute of Science in Israel. The researchers revealed Brain-IT, an AI model capable of reconstructing mental images with a scary level of accuracy—using only brain-scan data.

In closing ...

Decoding fMRI brain scans is a perfect pattern recognition problem for AI to solve, which is why scientists have been toying with the idea for more than a decade.

Click here for the MRI dataset. Click here for the paper.

Any questions?