WiMi Hologram Cloud (WIMI, Financial) has introduced a breakthrough in neural network training by developing a Quantum Computing-Based Feedforward Neural Network (QFNN) algorithm. This innovative approach aims to address the computational challenges found in conventional neural networks.
The core of this new algorithm is its ability to approximate the inner product between vectors efficiently, utilizing Quantum Random Access Memory (QRAM) to store and swiftly retrieve intermediate values during computations. This enables significant reductions in processing time and resources.
WiMi’s QFNN algorithm is underscored by key quantum computing processes, particularly the enhanced feedforward and backpropagation mechanisms. Traditional neural networks employ feedforward propagation to determine activation values for input data and use backpropagation to adjust weights to minimize the loss function. WiMi's quantum-driven version accelerates these processes exponentially, allowing neural networks to converge much faster.
The Quantum Feedforward Propagation leverages quantum state superposition and coherence, encoding neuron weights and input data into quantum states. This allows for matrix-vector operations to be conducted in logarithmic time, a substantial improvement over classical methods.
In the realm of backpropagation, which involves error correction in neural network training, WiMi's quantum algorithm utilizes quantum coherent states. It employs the Quantum Fourier Transform to efficiently compute and update gradients, achieving updates that are quadratically faster than traditional approaches.
QRAM plays a critical role by efficiently storing intermediate computational results in quantum states, facilitating quick access for future calculations. This capability reduces redundant computations and contributes to the algorithm’s exponential speedup.
