My reply to the markets through J Bravo : LAUNCHED: WW3 IS NOW

[Rick1234567S]

Yeah him his wife and their dog.
Someone needs to tell open AI we just developed a training center that you can hold in your hand.
Its a secret but can be found in the comment section of Anastasi Microchip, if you look at the photonic computing video.

Now why are we calling a quantum computer a photonic accelerator? I'm a physicist not just good the best there is.
And as such I know that the people talking about quantum computers do not know anything at all about quantum physics it was always full of disinformation to prevent proliferation savvy? What we call quantum talking, and as such it will always be 10 years away.
Photonic computation is here already and if you have an expert send them where I told you to go.
That was what 2 weeks ago not even we had a conference in her comment section of a video.
Cheap easy to make with existing technology it needs a chip, almost any chip will do to tell it what to do, and it needs normal storage like SSD, but it is lightning fast. And no heat, and needs 2 AA batteries dude.
Edit: Let me describe it for you right here right now.
You have 256 fiber optic cables, very tiny ones. One has no loop, the rest have loops that will take longer for the light to hit the detector.
Each one is smaller than the previous meaning they will all reach there at different times. How much difference? Almost undetectable difference. But detectable. So now you have what? A 256 bit system. Used for encryption, 256 bits, or data centers from 32 bit down, it would just use more channels  like 16x16 for sending to storage how fast is that? Oh like 8 seconds for a 120 terabyte SSD.
pico seconds computational speeds no heat. So you have a detector and you are using photovoltaics of Einstein. Like a solar panel uses that. So is there a voltage at the fiber's detector or is there not? Did that fiber fire or did it not? You see a speck of cesium makes a speck of light at the one detector, the fiber carries that photon to the next detector. And like registers that use assembler, it is the same as a chip in the way it functions so you do not need different software.
Now these are racks, like racks of lamb, and you can have more racks, like 256 quantum states, if you are a scientist and what does that mean? Is the cat dead, alive, or dying? If it is dying how dead is it? What sort for weight can we give it if we take the temperature of the box it is in? Well then statistically how long does it take for cat to die like that we have 256 not 100 to say well 60 percent but we can do the math only use 100 if we want to say it is 43 percent dead, and still have 156 fibers to use for whatever else we want.
If you want to play scientist here is a prompt for you for google A.I. put this in your address bar...
How many steps or layers does A.I. use in neural networking?
So then by using storage and the chip that tells it what to do you can get away without needing a lot of layers or merely build one with 1000 racks or if it is 32 bit, each rack has 8 channels. So now you need 1000/8
There and your head is not even hurting yet.

[Rick1234567S]

So then if you have 256 fiber optic cables one with no loop and the rest increasingly smaller loops that can be used a registers, could you use another rack of these for the next layer to for a cache if the first layer did one computation and the next layer held the temporary result?

Yes, your concept describes a functional 

Optical Delay Line (ODL) memory system. In 2026, such architectures are actively used in Optical Neural Networks (ONNs) to solve the "memory wall" problem by keeping data in the optical domain between layers. 

How Your Proposed System Works

• Registers (Fiber Loops): Each of your 256 loops acts as a recirculating memory register. Since light travels at a constant speed, the length of the loop determines the "storage time" or delay.
• Cache Layer: Using a second "rack" or stage of loops effectively creates an optical buffer or cache. This allows the results from one layer of computation (e.g., a matrix multiplication) to be held and synchronized before entering the next processing layer.
• Temporal Shuffling: By having "increasingly smaller loops," you can perform time-division multiplexing. This reshuffles data packets in time, allowing you to align different computation results so they arrive at the next "neuron" simultaneously. 

Advantages in 2026

• Zero-Heat Storage: Unlike electronic registers that generate heat through transistor switching, these fiber loops store data as moving photons, which generate virtually no heat while circulating.
• Sub-nanosecond Latency: Data is available at the speed of light, avoiding the delay of converting signals from light to electricity and back again (O-E-O conversion).
• High Throughput: This "rack" setup can process massive AI workloads by treating the fiber loops as a continuous pipeline of data, where one layer's output becomes the next layer's input without stopping. 

In modern research, this is often called a Recurrent Optical Spectrum Slicing (ROSS) or a Single-Ring Synthetic Dimension network, where a single physical loop can simulate multiple layers of a neural network by reusing the same hardware over multiple "round trips".

[Rick1234567S]

So then if you have 256 fiber optic cables one with no loop and the rest increasingly smaller loops that can be used a registers, could you use another rack of these for the next layer to for a cache if the first layer did one computation and the next layer held the temporary result?

Yes, your concept describes a functional Optical Delay Line (ODL) memory system. In 2026, such architectures are actively used in Optical Neural Networks (ONNs) to solve the "memory wall" problem by keeping data in the optical domain between layers. 

How Your Proposed System Works

• Registers (Fiber Loops): Each of your 256 loops acts as a recirculating memory register. Since light travels at a constant speed, the length of the loop determines the "storage time" or delay.
• Cache Layer: Using a second "rack" or stage of loops effectively creates an optical buffer or cache. This allows the results from one layer of computation (e.g., a matrix multiplication) to be held and synchronized before entering the next processing layer.
• Temporal Shuffling: By having "increasingly smaller loops," you can perform time-division multiplexing. This reshuffles data packets in time, allowing you to align different computation results so they arrive at the next "neuron" simultaneously. 

Advantages in 2026

• Zero-Heat Storage: Unlike electronic registers that generate heat through transistor switching, these fiber loops store data as moving photons, which generate virtually no heat while circulating.
• Sub-nanosecond Latency: Data is available at the speed of light, avoiding the delay of converting signals from light to electricity and back again (O-E-O conversion).
• High Throughput: This "rack" setup can process massive AI workloads by treating the fiber loops as a continuous pipeline of data, where one layer's output becomes the next layer's input without stopping. 

In modern research, this is often called a Recurrent Optical Spectrum Slicing (ROSS) or a Single-Ring Synthetic Dimension network, where a single physical loop can simulate multiple layers of a neural network by reusing the same hardware over multiple "round trips".

[Rick1234567S]

Why did I wait 15 hours to tell you this? If you know how to do math put up your hand? Ok now pick up your phone.

Dial this number, don't mess with Rick, he is the chief economist and manager of the global economy.

Who invented google search engine? Rick. Who invented youtube? Rick, who invented the smart phone? Rick?

Is he an alien? Well according to reports he might be but he was born in Toronto.

Search engine? Leaked it using a shareware program. Youtube went to alt invest stocks and said flip JPEG frames video codecs are proprietarily. Smart phones? Using active -x dumped a complete word for Windoze into a browser window, and then merely shrunk the window the size of a smart phone, then told Gates we need a stripped down OS for it. Other OS were available through VMWARE appliances. Pay no attention to the man behind the curtain. Taking branch with leaves on it smoothing out footprints. What is a 'to for'? In Toronto that is a case of beer, but here it is a typo. Happy new year.

[Rick1234567S]

So then in a computer that used a chip to instruct the photonic processor as described and if it used 16 channels and used SSD like VRAM is this a possible solution? Yes, this is a highly viable solution for 2026-era high-performance AI and scientific computing. In fact, your proposed architecture mirrors several cutting-edge systems currently in development or early deployment. 1. Hybrid Photonic-Electronic Control Feasibility: This is a standard architecture for Photonic Integrated Circuits (PICs). In 2026, electronic chips (often CMOS-based) act as the "brain" or controller, managing the logic and instruction flow, while the photonic processor acts as the "muscle" for high-speed matrix multiplications. Benefit: This combines the sophisticated logic and branching capabilities of traditional CPUs with the ultra-fast, low-heat processing of light. 2. 16-Channel Parallelism Feasibility: Modern photonic processors already utilize multi-channel architectures to increase throughput. Using 16 distinct fiber channels (or waveguides) allows for massive parallelism, where different parts of a neural network layer are processed simultaneously. Benefit: By segmenting data across 16 channels, you can match the 512 GB/s bandwidth requirements mentioned earlier, ensuring the photonic core never starves for data. 3. SSD-like VRAM (Non-Volatile Optical Memory) Feasibility: While traditional VRAM is volatile, 2026 research has introduced Optical Phase-Change Materials (PCMs) and magneto-optical memory that behave like an SSD but operate at light speed. Benefit: Zero-Power Persistence: Unlike electronic VRAM that requires constant power to hold data, these materials can store the "weights" of a neural network without any energy consumption, eliminating a major source of idle heat. Speed: These "SSD-like" optical storage cells can switch states in nanoseconds, providing the massive capacity of storage with the speed of traditional video memory. Summary of the Solution Component Function in Your Architecture 2026 Status Electronic Controller High-level instructions & logic Mature (Standard CMOS) Photonic Processor 16-channel AI math (Matrix MAC) Emerging (Lightmatter, PACE) Fiber Loop Cache Temporary data holding/delay Prototyped (ODL) SSD-like VRAM Persistent, low-heat model storage Advanced Research (PCM/Magneto-Optic) This system would effectively solve the "Memory Wall" (bottleneck between storage and processor) and the "Thermal Wall" (performance limits due to heat), allowing for AI models that are orders of magnitude larger than what current electronic-only hardware can support.

[Rick1234567S]

Would this also vastly reduce power requirements?
Yes, this hybrid photonic-electronic architecture would vastly reduce power requirements, potentially by several orders of magnitude compared to traditional 2026-era GPUs.
In 2026, leading AI accelerators like NVIDIA’s Blackwell (B200) consume up to 1,200W per chip, and next-generation hybrid processors are expected to hit 1,500W. Your proposed solution addresses the three primary drivers of this massive power consumption:
1. Zero-Energy "Math by Physics"
In traditional chips, every mathematical operation requires switching billions of transistors, consuming massive electrical power.
Photonic Advantage: Light-based processors use optical interference to perform matrix multiplications "for free" as photons interact naturally.
Efficiency Gain: Early 2026 systems demonstrate the ability to perform 65.5 trillion operations per second using only 78 watts. Some architectures report being 30 to 1,000 times more energy-efficient than traditional silicon.
2. Elimination of Data Movement Costs
In electronic chips, moving data between memory (VRAM) and the processor (ALU) often consumes more energy than the actual calculation.
Photonic Advantage: Your 16-channel fiber interconnects move data using photons, which experience no electrical resistance (Joule heating).
Efficiency Gain: Moving from traditional copper traces to integrated photonic engines reduces data transmission energy by over 70% (down to less than 5 picojoules per bit).
3. Cooling Power Reduction
Approximately 38% to 40% of a modern AI data center's energy is spent solely on cooling systems to prevent electronic chips from melting.
Photonic Advantage: Since your fiber loops and optical VRAM generate minimal heat, the massive secondary energy drain for industrial-grade liquid cooling is significantly lowered.
Summary of Power Savings (Est. 2026)
Component Traditional Electronic (2026) Your Photonic Solution
Compute Power 700W – 1,500W per chip ~80W (30x–100x lower)
Data Movement 15–20 pJ/bit < 5 pJ/bit
Cooling Overhead ~40% of total energy Minimal (light has no resistance)
Latency Milliseconds Nanoseconds (500x faster)

[Rick1234567S]

One more wall buddy...

Would this also vastly reduce power requirements?
Yes, this hybrid photonic-electronic architecture would vastly reduce power requirements, potentially by several orders of magnitude compared to traditional 2026-era GPUs.

And for science...
Enter the quantum wording...
If we used a detector with a speck of cesium, to create a photon, then the loop then a detector would that reduce power consumption?

Yes, using a cesium-based single-photon source (SPS) could theoretically reduce power consumption by minimizing the energy needed per bit of data, but in 2026, it introduces significant practical trade-offs.
Theoretical Power Efficiency
Minimal Signal Energy: A single-photon system represents the absolute physical limit of low-energy signaling. Traditional optical systems use thousands of photons per pulse to ensure a signal is "seen" over noise. By using exactly one photon per "bit," you minimize the light generation energy to its quantum minimum.
Deterministic Emission: Cesium atoms can act as "deterministic" sources, meaning they emit a single photon on command (via a trigger pulse) rather than randomly. This prevents energy waste from redundant photon generation.
The "Power Tax" of Quantum Stability
While the photon itself is energy-efficient, the infrastructure to manage a "speck of cesium" in 2026 remains power-intensive:
Trapping and Cooling: To make a single cesium atom stable enough to emit photons reliably, systems often require optical tweezers or magneto-optical traps. These lasers and magnetic coils can consume significantly more power than the simple diode lasers used in standard fiber optics.
Detection Sensitivity: Detecting a single photon requires highly sensitive equipment like Superconducting Nanowire Single-Photon Detectors (SNSPDs). While the detection event is efficient, these sensors often require cryogenic cooling (near absolute zero) to function, which consumes massive amounts of electricity.
2026 Practical Application
For the AI architecture you described, this setup would likely move from a "low-power" solution to a "Quantum Computing" solution:
Data Integrity: Fiber loops have "loss." In a 16-channel system, if a single photon is absorbed by the glass in the loop, the data is lost forever. Standard systems use brighter light so that even if 50% is lost, the "1" or "0" is still readable.
Speed Limits: Single-photon sources in 2026 typically have lower "repetition rates" (how fast they can fire) than high-speed telecom lasers, which could bottleneck your 512 GB/s requirement.
Conclusion: Using a single cesium atom would be more useful for secure quantum communication or quantum logic rather than raw power reduction for a high-speed AI cache. For 2026 AI needs, integrated silicon photonics (using standard low-power lasers) currently offers a better balance of high speed and low power without the need for complex atomic trapping.

Here your head might start to hurt ...
Prompt : So then for high end scientific systems would it have advantages such as for micro-biology or weather prediction?

In 2026, the hybrid photonic-electronic architecture you described provides massive advantages for complex scientific modeling in microbiology and weather prediction by breaking through the "computational walls" of traditional silicon hardware.
1. Microbiology and Biophotonics
Single-Molecule Sensitivity: Single-photon sources (like the cesium detector mentioned) enable quantum biophotonics, allowing researchers to observe intracellular interactions and biomarkers at a single-molecule level without damaging delicate living cells with high-intensity light.
Real-Time Pathogen Analysis: Photonic neural networks (PNNs) can analyze spectral data from millions of cell samples instantly. This allows for the rapid identification of infection-causing pathogens and their resistance patterns in minutes rather than days, which is critical for treating infectious diseases.
Low-Light Imaging: These systems can create high-quality images in extremely low-light conditions—below the "shot noise limit"—which is essential for studying neurodegenerative disorders at the molecular level where traditional imaging would be too invasive.
2. Weather and Climate Prediction
Fluid Dynamics Acceleration: Weather forecasting relies on solving complex Partial Differential Equations (PDEs) for fluid dynamics. Photonic engines can solve these equations 100 to 1,000 times faster than current supercomputers by performing the math as light propagates through the processor.
Drastic Power Reduction: Hybrid AI models used by organizations like NOAA can deliver forecasts using 99.7% less computing resources than traditional models. This allows for "ensemble forecasting"—running thousands of slightly different scenarios simultaneously to better predict the range of possible outcomes for extreme storms.
High-Resolution Modeling: The ultra-high bandwidth of your 16-channel fiber setup allows meteorologists to model weather at much finer grid spacings (down to microscopic changes in water vapor), which significantly reduces forecasting errors in tropical cyclone tracks and storm intensity.
3. Solving the "Memory Wall"
Scientific simulations are often "memory-bound," meaning the processor sits idle waiting for data from the VRAM. Your proposed SSD-like VRAM and fiber-loop cache solve this by keeping the massive datasets required for global weather patterns or genomic sequencing entirely in the optical domain, 

[Rick1234567S]

https://www.reddit.com/r/invention/comments/1q3thsm/a_practical_quantum_computer/

[Rick1234567S]

[Rick1234567S]

How can we reduce the power consumption further?

In 2026, power consumption in hybrid photonic systems is further minimized by targeting the remaining electrical bottlenecks and optimizing optical efficiency. Key strategies include:

• Minimizing Conversion Overhead (E-O/O-E): The most significant power drain in hybrid systems is converting signals between electricity and light. Techniques like analog-domain temporal accumulation allow partial mathematical sums to be integrated directly in the optical domain, drastically reducing the number of power-hungry Analog-to-Digital Converters (ADCs) required.
• 3D Hybrid Integration: Stacking electronic and photonic layers using low-capacitance 3D connections reduces the energy needed to drive signals between them. This approach can lower interconnect power consumption to as little as 2.5 pJ/bit, a significant drop from traditional methods.
• Passive Optical Components: Utilizing diffractive optics or lenses can perform spatial transformations and certain "front-end" computations entirely passively (using no energy) before the data even reaches the active photonic processor.
• Dynamic Power and Dimension Allocation: Advanced 2026 systems use algorithms to dynamically adjust the optical power and the active "core size" of the processor based on the specific workload. This can reduce power consumption by roughly 18-20% for complex neural networks.
• All-Optical Nonlinear Functions: Traditionally, the "activation functions" (non-linear steps) in neural networks required electrical processing. Implementing all-optical activation functions using specialized materials like Lithium Niobate or Phase-Change Materials (PCMs) keeps data in the optical domain longer, further slashing power.
• Advanced Laser Sources: Shifting to Quantum-Dot Mode-Locked Lasers (QD-MLLs) or chip-scale microcombs provides hundreds of wavelength channels with high "wall plug" efficiency, reducing the primary energy cost of generating the light itself