Method for noise-induced regularization in quantum neural networks
This addresses the challenge of noise in quantum computing for quantum machine learning applications, offering a novel approach to enhance performance in specific domains like medical regression.
The paper tackles the problem of quantum decoherence in quantum neural networks by showing that controlled noise can act as regularization, improving generalization; on a medical regression task, tuning noise reduced the mean squared error loss by 8%.
In the current quantum computing paradigm, significant focus is placed on the reduction or mitigation of quantum decoherence. When designing new quantum processing units, the general objective is to reduce the amount of noise qubits are subject to, and in algorithm design, a large effort is underway to provide scalable error correction or mitigation techniques. Yet some previous work has indicated that certain classes of quantum algorithms, such as quantum machine learning, may, in fact, be intrinsically robust to or even benefit from the presence of a small amount of noise. Here, we demonstrate that noise levels in quantum hardware can be effectively tuned to enhance the ability of quantum neural networks to generalize data, acting akin to regularisation in classical neural networks. As an example, we consider a medical regression task, where, by tuning the noise level in the circuit, we improved the mean squared error loss by 8%.