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 two regression tasks, where, by tuning the noise level in the circuit, we demonstrated improvement of the validation mean squared error loss. Moreover, we demonstrate the method's effectiveness by numerically simulating QNN training on a realistic model of a noisy superconducting quantum computer.
{"title":"Method for Noise-Induced Regularization in Quantum Neural Networks","authors":"Viacheslav Kuzmin, Wilfrid Somogyi, Ekaterina Pankovets, Alexey Melnikov","doi":"10.1002/qute.202400603","DOIUrl":"https://doi.org/10.1002/qute.202400603","url":null,"abstract":"<p>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 two regression tasks, where, by tuning the noise level in the circuit, we demonstrated improvement of the validation mean squared error loss. Moreover, we demonstrate the method's effectiveness by numerically simulating QNN training on a realistic model of a noisy superconducting quantum computer.</p>","PeriodicalId":72073,"journal":{"name":"Advanced quantum technologies","volume":"8 12","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/qute.202400603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145751157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bose-Einstein condensates (BECs) provide an ideal platform for exploring novel quantum states (QTs) in synthetic spin-orbit coupling (SOC). In article number 2500431, Yun Liu and Zu-Jian Ying analyse the interplay of SOC with other interactions and potential geometry by BECs in concentric annular traps. Various exotic QTs emerge, including facial-makeup states, fissure states, stripe states, half-disk states, half-skyrmion fence, etc. Peculiar density-phase separation is noticed. The study illustrates manipulations of exotic QTs and supplies abundant quantum resources for potential applications.