Profitmost Wealth Project: June 2026

Releasing a hybrid quantum-classical convolutional neural network (QCNN) technology.

In the broader tech landscape, light (photonics and optics) is used to bypass the power and speed bottlenecks of traditional silicon chips.

1. Companies Using Light & How They Use It

WiMi Hologram Cloud Inc.

The Tech: Quantum-classical hybrid neural networks.
How They Use It: They use light-based components (like spatial light modulators and head-mounted light-field holographic devices) alongside quantum simulation algorithms to process image patches into quantum states. This extracts complex features from multi-channel visual data for image and text recognition.

Nvidia

The Tech: Optical Interconnects & Photonic AI Infrastructure.
How They Use It: Nvidia invests heavily in photonics companies (such as Coherent and Lumentum) to replace copper wires with light-based data transmission. This allows them to link millions of GPUs inside AI data centers, maximizing bandwidth while slashing power consumption.

Quantum Computing Inc. (QCi)

The Tech: Thin-Film Lithium Niobate (TFLN) Photonic Chips.
How They Use It: QCi builds room-temperature quantum and optical computers. They map data directly onto single photons of light, executing complex matrix calculations instantly and securely at low power levels.

Nokia Bell Labs

The Tech: Silicon Photonics & Heterogeneous Lasers.
How They Use It: They micro-print ultra-miniature lasers directly onto silicon computer chips. This blends traditional computing architectures with laser optics to run lightning-fast AI algorithms.

2. A Better Method: The "Electro-Optic Hybrid Memory" Design

While using light for Matrix-Vector Multiplication (MVM) is incredibly fast, current systems suffer from massive energy loss during Digital-to-Analog (DAC) and Analog-to-Digital (ADC) conversions when light data translates back into electrical chip data.

To do this better, we design a Direct Optical Phase-Change Memory (O-PCM) Architecture:

[Digital Input] ──> [Laser Pulse Array] ──> [O-PCM Weight Grid] ──> [Diffractive Optical Elements] ──> [Direct Photodiode Output]
                                                 │                                                      │
                                                 └─── (Weights updated in light phase, no DAC needed) ──┘

How It Optimizes Current Technology:

Eliminate Electrical Conversion In-Flight: Use non-volatile Phase-Change Materials (like GST alloy) directly on the photonic tracks. Instead of continuously changing electricity into light using power-hungry modulators, send laser pulses to alter the material state of the chip, instantly shifting how it refracts light. This stores neural network weights directly inside the light pathway.
All-Optical Activation Functions: Instead of sending light signals back to a classical computer chip to process mathematical non-linear steps, direct the light beams through an ultra-thin Non-linear Metasurface layer. This allows the light to transform itself purely through optical physics, bypassing the CPU entirely.
Adaptive Photonic State Injection: Integrate an automated measuring step during the neural network pooling phase. By split-testing a tiny fraction of the light beam mid-calculation, the chip can automatically adjust downstream light intensities, making the chip self-correcting without requiring an external processor.

The exact materials list needed for the O-PCM chip layers.
The mathematical quantum parameter-shift rules used to train these systems.
A comparative breakdown of Free-space vs. Integrated on-chip optics.

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