Functional Complete

DigitalSoul 1.0.0

This marks a major milestone release of DigitalSoul, designed to create a bridge between classical, quantum, and potentially hardware-accelerated computation.

Key Features

Customizable Data Types: Define data types (Boolean, integer, floating-point, quantum states, gates, tensors) tailored to your computational needs.
Node-Based Computation: Express computations as flexible graphs, where nodes represent operations and edges manage data flow.
Multi-Backend Execution: Execute computations seamlessly across CPUs (NumPy), GPUs (Cupy), TensorFlow, and internal quantum simulators.
VHDL Transpilation: Translate computational graphs into VHDL, enabling potential synthesis on FPGAs for hardware acceleration.

New in Version 1.0.0

Core computational framework, node types, and multi-backend execution.
VHDL transpilation for hardware synthesis flow exploration.
Initial set of quantum gates (e.g., X, Y, Z, H, CX, CCX).
Basic example in the README showcasing core capabilities.

Getting Started

Installation: pip install DigitalSoul
Documentation: Explore the documentation on the project's GitHub repository: [DigitalSoul GitHub Link]
Contribute: We welcome your contributions to help shape the future of DigitalSoul!

Why DigitalSoul?

DigitalSoul aims to empower researchers, developers, and enthusiasts by enabling:

Hybrid Algorithm Exploration: Design and test algorithms that seamlessly blend classical, quantum, and hardware-accelerated elements.
Hardware-Aware Development: Craft software with the potential for direct hardware acceleration through FPGAs.
Quantum Readiness: Simulate and experiment with quantum algorithms in a flexible, hardware-agnostic environment.

We Want Your Feedback!

DigitalSoul is an open-source project driven by community input. Please share your experiences, feature requests, and bug reports. Let's shape the future of unified computation together!

Let me know if you'd like any modifications or have specific features to highlight in the release notes!