NEURAL-Q-FORGE (NQF) is an advanced quantum computing framework designed for the post-VQE era. Developed in Rust, it offers a unique capability to dynamically forge quantum circuits that adapt to the geometry of complex problems. This innovative approach allows users to tackle intricate quantum challenges efficiently.

Key Features

• Dynamic Topology Forging: Quantum circuits evolve during optimization, avoiding common pitfalls such as Barren Plateaus.
• Rust-Native Core: Leverage the performance of Rust with direct processing and memory management, eliminating Python overhead while providing support for AVX-512 vector instructions.
• Asynchronous Gradient Estimation: Facilitates simultaneous calculation of quantum gradients through massive parallelism, enabling rapid evaluations.

Benchmark Validation

In robust testing involving the simulation of a water molecule (H₂O), NQF demonstrated exceptional performance. It achieved convergence within 3.7 seconds using standard hardware, generating a circuit with just 17 logic gates. This performance is significantly faster than traditional Python-based frameworks, establishing NQF as a powerful tool for research in fields like chemistry and material science.

Architecture Overview

NQF comprises several integral components:

• nq-core: A high-speed open-source quantum linear algebra engine.
• nq-compiler: Translates dynamic graphs into OpenQASM 3.0 format (open source).
• nq-neural: Incorporates artificial intelligence strategies for circuit evolution (proprietary / source available).
• nq-py: Provides high-performance Python bindings (open source).

Usage Example (Python)

To utilize NQF for simulations, users can leverage straightforward code snippets:

import nqf

# Load the molecule's Hamiltonian from a JSON file
h2_hamiltonian = nqf.PauliSum.from_json("h2_hamiltonian.json")

# Initialize the QuantumForge with the desired number of qubits
forge = nqf.QuantumForge(2)

# Run the simulation
gole := forge.run(h2_hamiltonian, target_energy=-1.136, max_iters=1000, hf_state_idx=1)

print(f"Final Energy: {result['final_energy']} Ha")
print(f"QASM Circuit:\n{result['qasm']}")

This framework serves as a vital resource for researchers engaged in the field of quantum computing, providing them the tools to build and optimize quantum circuits effectively. For citation, please refer to the work of Cleiton Augusto Correa Bezerra published on Zenodo.