- MicroCloud Hologram Inc. (HOLO, Financial) develops noise-resistant Deep Quantum Neural Network (DQNN).
- The architecture optimizes quantum learning tasks while minimizing resource demands.
- HOLO's DQNN proves robust on Noisy Intermediate-Scale Quantum (NISQ) computers.
MicroCloud Hologram Inc. (HOLO), a prominent technology service provider, has announced the development of a groundbreaking noise-resistant Deep Quantum Neural Network (DQNN) architecture. This innovation is designed to optimize quantum computing and learning tasks by effectively using qubits as neurons and unitary operations as perceptrons.
One of the key features of this DQNN is its fidelity-based optimization strategy. By focusing on quantum fidelity rather than traditional loss functions, the architecture minimizes the number of training steps required and reduces the quantum resource needs while significantly maintaining stability even in noisy environments. This positions HOLO at an advantageous point in the quantum AI development landscape.
The design of the DQNN ensures that the qubit requirements are scalable with network width rather than depth. This unique approach makes it suitable for use with current Noisy Intermediate-Scale Quantum (NISQ) computers, thus addressing a common scalability challenge in quantum neural networks.
Benchmark tests conducted by MicroCloud Hologram have demonstrated the network's ability to learn unknown quantum operations accurately and maintain robust performance, even when the data is noisy. This positions the DQNN as a practically viable solution on existing quantum processors, marking a significant step forward in practical applications of quantum AI.
While specific commercial applications of this technology remain under exploration, the development of this innovative architecture highlights HOLO's leading role in the confluence of artificial intelligence and quantum computing. This is set to open new possibilities across various industries as quantum technology continues to evolve.
