Quantum Neural Network Based on Ytterbium (Yb³⁺) Ion Quantum Processor
Recent advances in quantum computing technology thanks to the BSM-SG theory here at bsm-sg computing have enabled the development of a novel quantum neural network (QNN), leveraging ytterbium ions (Yb³⁺) in solid-state architectures. Utilizing a hybrid quantum-classical framework, this system integrates advanced quantum manipulation methods with classical computational techniques, supported by FPGA and Qiskit software.
Quantum Neuron Architecture
Each quantum neuron comprises:
- Yb³⁺ ions embedded in a solid-state crystal lattice serving as qubits.
- Microwave transitions (~10 GHz) for spin manipulation.
- Laser-induced rotations (1030 nm laser diode) to manage quantum states.
- Optical detection (InGaAs photodiode) to read quantum states through photon emission.
The qubits are initialized into superposition states using Hadamard gates and subsequently entangled via controlled quantum gates (CNOT). Precise rotations around the quantum axes (Rx, Ry, Rz) facilitate intricate state manipulations necessary for neural computations.
Integrating Quantum Layers
Multiple quantum neurons form quantum layers by interconnecting qubits, enabling complex quantum state dynamics. FPGA technology bridges quantum state measurements and classical computational resources, making real-time quantum processing feasible. This hybrid setup enables classical algorithms to optimize quantum neuron parameters dynamically.
Application and Future Outlook
This Yb³⁺ ion-based quantum neural network opens pathways for solving complex computational problems more efficiently than classical counterparts, with applications in machine learning, optimization, and quantum simulations. Further development will focus on expanding qubit numbers and enhancing system stability for practical quantum computing.
2025 Stoyan Sargoichev Victor Pronchev Antonio Alexandrov Katerina Proncheva