Miniature chips made from materials like silicon that guide and process photons, acting like a quantum CPU. A photon’s quantum state can be encoded in different ways:

Polarization: Horizontal (0) vs. vertical (1), with superpositions like diagonal.
Path: Which route a photon takes (e.g., through one of two waveguides), with superpositions of both paths.
Time: When a photon arrives (early vs. late), with superpositions of timing. These properties let photons carry quantum information.

Generating Photonic Qubits

To start, you need a source of single photons or entangled photon pairs. This is where solid-state photonics often comes in:

Spontaneous Parametric Down-Conversion (SPDC): A laser hits a nonlinear crystal (like beta-barium borate), splitting some photons into pairs that are entangled—meaning their states are linked, even if separated.
Quantum Dots: Tiny semiconductor structures that emit single photons when excited by electricity or light.
Defect Centers: Like nitrogen-vacancy centers in diamond, which can spit out photons with precise quantum properties.

These sources are integrated into photonic chips—think miniature circuits etched into materials like silicon or silicon nitride—to keep everything compact and scalable.

Manipulating Photons

Once you’ve got your photonic qubits, you need to perform operations on them—quantum gates, the equivalent of logic gates in classical computing. This is tricky because photons don’t naturally interact with each other. Here’s how it’s done:

Beam Splitters: These partially reflecting mirrors mix photon paths, creating superpositions or entangling qubits when combined with phase shifters (which tweak the timing of light waves).
Phase Shifters: Adjust the relative timing (phase) of photon states, enabling operations like flipping a qubit from 0 to 1 or putting it into superposition.
Detectors: Measuring a photon collapses its state (e.g., 0 or 1), which is key for certain algorithms. Detectors also enable "feed-forward" logic, where measurement outcomes guide later steps.

A big challenge is that standard quantum gates—like the CNOT gate (controlled NOT)—require qubit interaction. Photons don’t play nice that way, so photonic systems often rely on a workaround called linear optical quantum computing (LOQC). Here, you use extra photons (ancilla qubits) and clever measurement tricks to simulate interactions indirectly.

Measurement-Based Quantum Computing

One popular approach in photonic quantum processing is measurement-based (or one-way) quantum computing. Instead of applying gates step-by-step like a classical CPU, you: Start with a big entangled state (a "cluster state") made of many photons. Measure photons one by one in specific ways. Use the measurement results to steer the computation. It’s like sculpting a result out of a pre-entangled block of light. The catch is preparing that initial cluster state, but once you’ve got it, the process is robust and suits photons’ strengths.

The Photonic Processor

A photonic quantum processor looks like a tiny chip with waveguides (light channels), beam splitters, and detectors all etched into it. Here’s the flow: Generate single photons or entangled pairs. Send them through waveguides, where beam splitters and phase shifters manipulate their states. Measure some photons along the way, adjusting the setup dynamically based on results (using fast electronics). Collect the final output from the remaining photons’ measurements.

Photonic Systems are the evolution of optical or electro-optical circuit boards, which represent an emerging technology that integrates photonic (light-based) components with traditional electronic circuits. Unlike conventional electronic circuit boards that rely solely on copper traces to transmit electrical signals, photonic circuit boards use light—carried through waveguides, optical fibers, or other photonic structures—to transmit data. This approach leverages the unique properties of photons, such as high speed, low energy loss, and immunity to electromagnetic interference, to overcome limitations in traditional electronic systems.

The core idea behind photonic circuit boards is to combine optical and electrical functionalities on a single substrate. Photonic components, such as waveguides (which act like optical "wires"), lasers (for generating light), modulators (to encode data onto light), and photodetectors (to convert light back into electrical signals), are integrated alongside electronic elements like transistors and resistors. This hybrid design allows for faster data transfer rates, higher bandwidth, and reduced power consumption compared to purely electronic systems. For instance, while copper-based interconnects in electronic boards are limited to bandwidths of around 40-100 Gb/s per line due to signal degradation and heat generation, photonic links can achieve terabit-scale speeds (e.g., 2 Tb/s or more on a single channel) with minimal heat.

One prominent application of this technology is in photonic integrated circuits (PICs), which are often embedded within photonic circuit boards. PICs are microchips that contain multiple photonic components, typically fabricated on materials like silicon, indium phosphide (InP), or silicon nitride (SiN). These chips can perform tasks such as signal processing, amplification, and data transmission using light. For example, arrayed waveguide gratings (AWGs) in PICs are used in fiber-optic communication systems to multiplex and demultiplex optical signals, while externally modulated lasers (EMLs) combine a laser diode with a modulator for high-speed data transmission.

The development of photonic circuit boards is driven by the growing demand for high-performance computing, AI, and telecommunications. As electronic systems approach physical limits—such as those imposed by Moore’s Law, where transistor scaling becomes increasingly challenging—photonics offers a way to "change lanes" and push performance further. In data centers, for instance, photonic circuit boards could enable faster interconnects between servers, reducing latency and power usage. Beyond computing, they hold promise for applications like LiDAR in autonomous vehicles, biomedical sensing, and even quantum computing, where precise control of light is essential.

Manufacturing these boards, however, presents challenges. Integrating photonic and electronic components requires high-precision fabrication techniques, such as lithography, to create waveguides and align them with optical sources and detectors. Materials like glass, polymers, or thin-film lithium niobate (TFLN) are often used for their optical properties, but combining them with silicon-based electronics can introduce compatibility issues. Additive manufacturing (e.g., 3D printing) is being explored as a solution to fabricate complex hybrid structures more efficiently, potentially reducing costs and enabling smaller, lighter designs.

Despite these hurdles, the technology is advancing. Companies like NVIDIA are exploring silicon photonics for next-generation AI systems, with plans to release integrated photonic solutions by late 2026. Research institutions, such as Fraunhofer IZM, are also developing glass-based waveguides for scalable photonic boards. As the field matures, photonic circuit boards could redefine how we build and connect electronic devices, offering a path to faster, more efficient, and sustainable technology.

Photonic storage refers to data storage technologies that utilize light (photons) rather than traditional electronic (electrons) or magnetic methods to store and retrieve information. These technologies leverage the properties of light—such as its speed, bandwidth, and energy efficiency—to address the growing demands for faster, more efficient, and higher-capacity data storage systems.

Photonic storage systems typically involve optical materials and devices, such as photonic integrated circuits (PICs), lasers, and phase-change materials, to encode and read data. Unlike conventional magnetic hard drives or solid-state drives (SSDs), which rely on electrons, photonic storage uses light to write and retrieve data, offering advantages like higher speeds (potentially in the picosecond range) and lower energy consumption. This makes it particularly appealing for applications in data centers, artificial intelligence (AI), and other data-intensive fields where traditional storage is hitting performance and efficiency bottlenecks.

One notable development in photonic storage is the use of hybrid technologies that combine light-based writing with magnetic storage. For instance, researchers have demonstrated systems where ultra-short femtosecond laser pulses write data directly into magnetic memory. This approach achieves write speeds 100 to 1,000 times faster than current magnetic storage, while maintaining energy efficiency by eliminating the need for power-hungry electronic intermediaries. Such systems could integrate with "racetrack memory," where magnetic bits are transported along a wire, allowing continuous data writing and retrieval with light.

Another exciting advancement is in multi-layer optical storage technologies that utilize Multi-layer nanophotonic discs that use advanced materials to stack data layers, aiming for capacities starting at 1 terabyte per disc and scaling to much higher levels. This technology promises to reduce costs, enhance cybersecurity (through immutable storage), and improve sustainability compared to traditional archival methods.

High-density photonic memory is also progressing with innovations like 5D optical storage, demonstrated by researchers at the University of Southampton. This technique uses nanostructured glass to encode data in three spatial dimensions plus two optical dimensions (polarization and intensity), achieving potential capacities of hundreds of terabytes per disc. While currently limited by slow read speeds and the need for high-power lasers, its long-term stability (billions of years) makes it a candidate for archival purposes.

On the computing front, photonic memory arrays integrated into chips are gaining traction. These systems use tunable nanophotonic devices to store data optically on-chip, offering ultrahigh bandwidth and analog processing capabilities ideal for unconventional computing strategies like AI and quantum computing. Although their storage density lags behind electronic memory (by a factor of about 100), techniques like wavelength multiplexing and 3D layering could close this gap to within a factor of 3, with further improvements possible through advanced nanostructures.

Advantages of Photonic Storage

Speed and Bandwidth: Photonic systems can process information and transfer data at extremely high speeds, potentially exceeding the capabilities of current electronic systems.

Energy Efficiency: Light-based systems can be more energy-efficient than their electronic counterparts, reducing energy consumption and environmental impact.

Scalability: Photonic technologies, particularly those based on silicon photonics, offer the potential for creating compact and scalable storage devices.

On-Chip Integration: Photonic integrated circuits (PICs) enable the integration of optical components on a single chip, leading to more compact and efficient storage solutions.

Quantum-Photonic Memories: Quantum-photonic memories, which can exist in multiple states simultaneously, could theoretically store much more information than traditional binary systems, leading to higher storage densities.

Examples of Photonic Storage Technologies

Programmable Photonic Latch: A new type of optical memory that utilizes silicon photonics for high-speed, temporary data storage in optical processing systems.

Photonic Memory Arrays: Large-scale architectures for photonic memory arrays, enabling high-bandwidth data processing and unconventional computing strategies.

Nanophotonic Devices: Emerging nanophotonic devices with memory capabilities, including those based on phase change materials (PCMs).

Light-Written Magnetic Memories: A hybrid technology that combines the speed and efficiency of light with the storage capacity of magnetic hard drives.

Photonic Stochastic Emergent Storage (SES): A method for storing and classifying information at the speed of light by leveraging random transmission matrices in a turbid medium.

Photonic Neural Networks: Using photonic memory for fast training and weight storage in optical neural networks.

Challenges and Future Directions

Challenges remain, including scalability, manufacturing complexity, and integration with existing infrastructure. For example, while photonic memories excel in speed and bandwidth, their physical footprint is larger than electronic counterparts, and exotic materials or advanced packaging techniques are often required. However, ongoing research into silicon photonics and heterogeneous integration (combining photonic and electronic components) is addressing these hurdles, with commercial applications inching closer.