Custom Quantum +FPGA Hardware Platform

Purpose-built hardware combining custom FPGA accelerators with quantum processing units for ultra-low latency financial computations.

Explore Hardware

Quantum Tensor Network Processor (QTNP)

A custom-designed quantum processor optimized for financial modeling, integrating cutting-edge quantum processing with classical high-performance computing infrastructure for exponential speedup in complex financial calculations.

<1μs

Quantum search latency

√N

Quantum speedup advantage

>99.5%

Single-qubit gate fidelity

Classical Infrastructure

Classical Processing & Control Layer

High-performance classical infrastructure manages data ingestion, preprocessing, and trade execution, while seamlessly interfacing with the quantum core for computationally intensive tasks.

High-Volume Data Streaming

Manages real-time market data ingestion and preprocessing with ultra-low latency

Quantum-Classical Interface

Seamless data transfer and control signals between classical and quantum components

Trade Execution & Validation

Validates trading signals, manages risk, and executes trades on financial exchanges

Output Analysis

Extracts and analyzes quantum computation results for market insights

FPGA Specifications

PlatformXilinx Versal ACAP

Logic Cells2M+ LUTs

RF Channels16 RFSoC DAC/ADC

Memory32GB DDR4

Network100GbE

Processing Throughput

10M+ ops/sec

Financial calculations per second

QTNP Core Specifications

Qubit TechnologySuperconducting/Fluxonium

Layout2D Grid Topology

Entanglement MappingCustom EMU Architecture

HierarchyMulti-Scale Temporal

Gate Fidelity>99.5% (1-qubit)

Two-Qubit Gate Fidelity

>98%

CNOT gate operations

Quantum Core

Quantum Tensor Network Processor

Custom quantum processor utilizing 2D grid layout with vendor-agnostic design, supporting superconducting transmon and fluxonium qubits for optimal performance across financial algorithms.

Entanglement Mapping Units (EMU)

Pre-compiled quantum gate sequences encoding correlation data into entangled qubit states

Multi-Scale Entanglement Hierarchy

Simultaneous analysis of correlations at multiple time scales (intraday, daily, weekly)

Vendor-Agnostic Architecture

Supports multiple qubit technologies including superconducting, fluxonium, and trapped-ion

Ultra-Fast Gate Operations

Single-qubit gates in <20ns, two-qubit gates in <100ns for minimal latency

Quantum Memory

Sliding Window Quantum RAM (qRAM)

Specialized quantum random-access memory for storing and retrieving time-series financial data, enabling efficient quantum queries on large datasets with automatic windowing for real-time updates.

Tree-Based Address System

Hierarchical addressing for efficient superposition-based memory access

Automatic Windowing Mechanism

Classical co-processor manages sliding window of market data with continuous updates

Hybrid Quantum-Classical Model

Superior scaling in both space and time for time-series financial data

Real-Time Data Access

Operates on the most relevant market activity with automatic data lifecycle management

qRAM Architecture

ArchitectureTree-Based Addressing

Access ModeSuperposition Queries

Window ManagementAutomated Sliding

Co-ProcessorClassical Data Manager

Update RateReal-Time Streaming

Memory Capacity

High-Dimensional

Time-series financial datasets

Search Engine Components

AlgorithmGrover's Search

Oracle TypeStreaming Quantum Oracle

Processing ModeParallel Universe Branches

Latency<1μs

Data ModeContinuous Streaming

Speedup

Quadratic (√N)

Arbitrage pattern detection

High-Frequency Search

Quantum Pattern Matching Engine

Specialized quantum search architecture based on Grover's algorithm for high-frequency arbitrage detection, providing quadratic speedup for finding complex opportunities in vast search spaces.

Streaming Quantum Oracle

Continuous processing of real-time market data without discrete start-stop operations

Parallel Universe Branches

Multiple parallel quantum lanes searching for different arbitrage patterns simultaneously

Sub-Microsecond Latency

Ultra-fast pattern detection for high-frequency trading applications

Reservoir Computing Inspired

Quantum reservoir computing techniques for optimal real-time processing

Multi-Layer Hardware Stack

Comprehensive hardware architecture abstracting quantum complexity with a programmable interface for financial applications.

Layer 1-2

Physical & Control

Qubit technology, cryogenic systems, and microwave/RF pulse generation

Hardware: Superconducting/Fluxonium qubits

Layer 3-4

Gate & Circuit

Single/two-qubit gates, error mitigation, and quantum circuit construction

Gates: Variational circuits, oracles

Layer 5-6

Algorithm & System

Quantum algorithms (Grover, TEBD) and system resource management

Resources: qRAM, classical co-processor

Layer 7

Application

High-level interface for financial applications and use cases

Apps: Correlation, arbitrage detection

Hardware Design Principles

Core principles guiding the quantum-hybrid architecture for financial computing excellence.

Latency Optimization

Minimizing time from data input to actionable insight for high-frequency trading applications

Scalability

Handling growing number of assets and increasingly complex financial models

Reliability

Error correction and noise mitigation ensuring accuracy of quantum computations

Adaptability

Vendor-agnostic design incorporating new qubit technologies and quantum algorithms

Integration

Seamless connection with existing classical trading infrastructure and data feeds

Modularity

Modular architecture enabling independent upgrades and technology evolution