QFabric: 5-LayerSoftware Architecture

A comprehensive quantum-classical hybrid platform designed specifically for financial workloads, delivering sub-microsecond latency and 40x performance improvements over classical Monte Carlo methods.

Explore Layers

Full-Stack Quantum Integration

QFabric provides a complete abstraction from application interface to hardware control, enabling seamless execution across custom QPUs and third-party quantum backends.

Layer 1

Application Interface Layer

High-level abstraction providing financial institutions with pre-built algorithm templates and circuit builders optimized for risk, portfolio optimization, and options pricing workloads.

Circuit Builders

Intuitive tools for constructing quantum circuits

Algorithm Templates

Pre-optimized for financial use cases

Result Post-Processing

Automated data transformation and analysis

Target Users

Financial Institutions

Quantum Researchers

Primary Workloads

Risk Management

Portfolio Optimization

Options Pricing

DeFi Analytics

Core Technologies

QFabric IRProprietary

Extended OpenQASM 3.0Open Standard

Financial Primitives

Custom gate sets and operations optimized for correlation matrices, covariance calculations, and multi-asset portfolio analysis.

Layer 2

Intermediate Representation Layer

Proprietary IR extended from OpenQASM 3.0, featuring financial-specific primitives and real-time classical control for hybrid quantum-classical algorithms.

Financial Primitives

Domain-specific quantum operations

Classical Control

Real-time optimization loops

Extended OpenQASM

Industry-standard compatibility

Layer 3

Compilation & Optimization Engine

Advanced transpiler with intelligent pass management, automatically optimizing circuits for target hardware topology while implementing error mitigation strategies.

Transpiler

Auto-mapping to custom QPU topology

Gate Decomposition

Optimal gate set transformation

Pass Manager

Multi-stage optimization pipeline

Error Mitigation

Noise-aware circuit optimization

Optimization Features

Finance-Optimized Gates

Custom decompositions for correlation calculations

Topology Mapping

Automatic adaptation to hardware constraints

Error Mitigation

Zero-noise extrapolation and readout correction

Performance Impact

3-5x

Circuit depth reduction vs. naive compilation

Backend Support

QuantaBull Custom QPUPrimary

IBM Quantum

AWS Braket

IonQ

Rigetti

Latency Performance

Sub-Microsecond

Job scheduling and dispatch

Layer 4

Backend Abstraction Layer

Unified interface for heterogeneous quantum backends, providing seamless job scheduling, credential management, and sub-microsecond dispatch to both custom and third-party hardware.

Unified Backend Interface

Single API for all quantum providers

Job Scheduling

Intelligent workload distribution

Credential Management

Secure multi-provider authentication

Layer 5

Hardware Control Layer

Direct FPGA pulse control with RFSoC integration, enabling real-time waveform generation and feedback for QuantaBull's custom QPU and classical accelerators.

FPGA Pulse Control

Nanosecond-precision quantum gate operations

RFSoC Integration

Direct RF signal generation and acquisition

Real-Time Feedback

Adaptive circuit execution based on measurements

Hardware Capabilities

Custom QPU Control

Direct pulse-level quantum gate operations

FPGA Accelerators

Classical preprocessing and postprocessing

Waveform Generation

Real-time arbitrary waveform synthesis

Gate Fidelity

>99.5%

Single-qubit gate operations

Complete Risk Calculation Pipeline

End-to-end workflow demonstrating how QFabric processes financial risk calculations from data ingestion to result delivery.

Data Ingestion

Market data, portfolio positions, and risk parameters loaded via REST API or WebSocket

Format: JSON, Protobuf

Circuit Generation

Quantum circuits auto-generated from algorithm templates and compiled for hardware

Latency: <100μs

Quantum Execution

Circuit executed on custom QPU with real-time error mitigation and adaptive feedback

Duration: 50ms

Results & Analytics

VaR, CVaR, Greeks, and sensitivity metrics delivered via API with confidence intervals

Total: ~50ms end-to-end