Architecture Overview

Q-Memory Architecture Overview

Q-Memory has evolved. What began as a non-volatile resistive memory technology has undergone a fundamental architectural transformation into a silicon photonic quantum computing platform — a chip that uses photons (single particles of light) to perform quantum computation and AI acceleration.

The Architecture Shift

The original Q-Memory architecture stored information as resistance states in a switching material layer, accessed via a crossbar array with CMOS control logic. That work validated key principles: non-volatility, multi-level storage, and CMOS backend compatibility.

The evolved platform takes those principles — dense information encoding, programmable state, zero standby power — and applies them to a fundamentally different substrate: silicon photonics.

Rather than storing bits as resistance levels, the new platform encodes and processes information as the phase and routing of single photons through a programmable network of optical waveguides on a chip.

Design Philosophy

The photonic architecture is built on several core principles:

1. Room-temperature quantum operation: The photonic core operates at ambient temperature. Light obeys quantum mechanical rules without needing extreme cooling.
2. Dual-use hardware: The same programmable optical network that runs quantum algorithms can also perform the matrix operations at the core of every neural network — making AI acceleration a natural side capability.
3. Non-volatile optical memory: Programmable optical elements can be locked into position without continuous power, eliminating static power consumption from the control layer.
4. CMOS co-integration: Classical electronics for readout, control, and feed-forward corrections are tightly integrated with the photonic layer using standard semiconductor manufacturing.
5. Foundry compatibility: The photonic layer stack is manufacturable using existing deep-UV lithography and standard materials — no exotic equipment required.

System Layers

The chip is built as a co-integrated stack of functional layers:

Layer	Role
Optical waveguide layer	Guides photons through the chip with minimal loss; the primary information carrier
Programmable phase layer	Controls the routing and interference of photons; equivalent to setting logic gate configurations
Non-volatile optical memory	Locks programmable element positions without continuous power
Photon source layer	Generates individual photons and entangled photon pairs on-chip
Detection layer	Captures photons and converts results to classical signals
CMOS electronics layer	Drives programmable elements, reads detectors, and applies real-time corrections

Each layer is individually qualifiable and can be upgraded independently as the technology matures.

How Computation Works

The chip operates as a programmable optical network:

1. Photon generation — individual photons or entangled photon pairs are produced on-chip
2. Programmable routing — photons travel through a reconfigurable network of beam splitters and phase shifters; adjusting the phase of each element changes the computation
3. Quantum logic — when two photons meet at a beam splitter and are jointly detected, they can become quantum-entangled; this is the mechanism for two-qubit logic operations
4. Detection — ultra-sensitive detectors capture individual photons at the output, producing classical measurement results
5. Feed-forward control — the electronics read detector results and apply corrections to later elements within nanoseconds, enabling adaptive quantum logic

The same network used for quantum operations can also be configured to perform optical matrix-vector multiplication — the dominant computation in AI inference and training.

Four-Phase Development Roadmap

Phase 0: Photonic Component Validation (2026 — Underway)

• Goal: Fabricate a small proof-of-concept photonic chip; validate every critical optical component
• Scope: Small multi-mode optical mesh; external light source and detectors; off-chip control electronics
• Key questions: Can waveguide loss targets be met? Do programmable phase elements respond correctly? Is optical alignment stable?
• Fabrication: Multi-project wafer run through a silicon nitride photonic foundry
• Budget: ~€15–30k MPW slot

Phase 1: Integrated Photonic System (2027 — Planned)

• Goal: Expanded programmable optical mesh with on-chip photon sources and integrated detection
• Scope: Silicon nitride photonic core co-integrated with CMOS control electronics; on-chip entangled photon pair source
• Key milestone: Demonstrate quantum optical operations with real-time feed-forward corrections
• Fabrication: 300 mm photonic foundry process

Phase 2: Multi-Chip Photonic Platform (2028 — Planned)

• Goal: Scale to large-mode operation across multiple co-packaged photonic chiplets
• Scope: Photonic chiplets, cryogenic detector chiplet, CMOS control chiplet integrated on a common platform
• Key milestone: Demonstrate fault-tolerant quantum operations; photonic AI acceleration workloads

Phase 3: Quantum-AI Processor (2029+ — Research)

• Goal: Full fault-tolerant quantum computing with integrated AI acceleration
• Scope: Error-corrected quantum circuits; photonic matrix accelerator for AI training workloads
• Commercial target: Quantum communications, molecular simulation, photonic AI inference

Key Innovations

1. Programmable photonic network: A reconfigurable mesh of beam splitters and phase elements that can implement any linear optical operation — universal for both quantum logic and AI matrix computation
2. Non-volatile optical memory: Optical memory materials lock programmable element positions permanently with zero ongoing power, enabling large-scale optical computing without continuous heating
3. Room-temperature quantum operation: The photonic core performs quantum operations without cryogenic infrastructure for the computation layer
4. Photonic AI acceleration: The same hardware that runs quantum algorithms accelerates neural network matrix operations at lower power than GPU-based approaches
5. CMOS co-integration: Classical electronics are tightly integrated with the photonic layer using standard foundry processes

Target Applications

Application	Timescale	Key Requirement
Quantum key distribution	Phase 0–1	Entangled photon pairs; low-loss optical I/O
Quantum random number generation	Phase 0–1	Single-photon detection; certified randomness
Photonic AI matrix acceleration	Phase 1–2	Large programmable optical mesh; high throughput
Molecular simulation	Phase 1–2	Multi-qubit optical logic; real-time feed-forward
Fault-tolerant quantum computing	Phase 2–3	Error correction; deterministic photon sources

Intellectual Property

The Q-Memory photonic platform represents significant patentable innovations across programmable optical computing, non-volatile optical memory, quantum-classical co-integration architectures, and photonic AI acceleration.