Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even once trained, still require access to a quantum computer in order to be evaluated on new data. To solve this issue, we introduce a new class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. Specifically, the training phase of our models ends with the generation of a 'shadow model' from which the classical deployment becomes possible. We prove that: i) this class of models is universal for classically-deployed quantum machine learning; ii) it does have restricted learning capacities compared to 'fully quantum' models, but nonetheless iii) it achieves a provable learning advantage over fully classical learners, contingent on widely-believed assumptions in complexity theory. These results provide compelling evidence that quantum machine learning can confer learning advantages across a substantially broader range of scenarios, where quantum computers are exclusively employed during the training phase. By enabling classical deployment, our approach facilitates the implementation of quantum machine learning models in various practical contexts.

翻译：量子机器学习常被强调为量子计算机能够提供计算优势的最具前景的实际应用之一。然而，量子机器学习模型在实践中广泛使用的一个主要障碍是，即使这些模型经过训练，在评估新数据时仍然需要访问量子计算机。为解决这一问题，我们引入了一类新的量子模型，其中量子资源仅在训练阶段需要，而训练后模型的部署则是经典的。具体而言，我们模型的训练阶段以生成一个“阴影模型”结束，从而使得经典部署成为可能。我们证明：i) 此类模型对于经典部署的量子机器学习是通用的；ii) 与“完全量子”模型相比，其学习能力确实受到限制，但 iii) 在复杂性理论中广泛接受的假设下，它相对于完全经典的学习者实现了可证明的学习优势。这些结果为量子机器学习能够在更广泛场景中提供学习优势提供了有力证据，其中量子计算机仅在训练阶段被使用。通过实现经典部署，我们的方法促进了量子机器学习模型在各种实际环境中的实施。