实现知识反向迁移的量子数据量子持续学习
Quantum continual learning of quantum data realizing knowledge backward transfer
AI总结：
针对量子机器学习中的灾难性遗忘问题，本文引入梯度情景记忆方法设计面向量子数据的量子持续学习方案，通过变分量子分类器实现序列量子态分类任务的持续学习，可克服遗忘并实现知识反向迁移。
摘要：
为了实现可媲美人类水平智能的强人工智能目标，人工智能系统需要具备适应不断变化场景、持续学习新知识且不遗忘已习得知识的能力。当机器学习模型按顺序在多个连续到来的任务上训练时，在新任务的学习过程中，其在已学习任务上的性能可能会大幅下降。为了避免这种被称为灾难性遗忘的现象，持续学习（也称为终身学习）被提出，成为机器学习最前沿的研究领域之一。随着近年来量子机器学习的蓬勃发展，开展量子持续学习研究具备重要价值。本文聚焦于计算模型与待处理数据均为量子形式的量子数据量子模型场景，引入梯度情景记忆方法设计了一套量子持续学习方案，该方案可克服灾难性遗忘并实现知识反向迁移。具体而言，由经典基于梯度的优化器优化参数的变分量子分类器，对一系列量子态分类任务开展持续学习，将当前任务的梯度投影至最近的梯度，避免先前任务的损失上升，同时允许其损失下降。数值模拟结果表明，该方案不仅克服了灾难性遗忘，还实现了知识反向迁移，即分类器在学习新任务时，其在先前任务上的性能会得到提升而非受损。
英文摘要：
For the goal of strong artificial intelligence that can mimic human-level intelligence, AI systems would have the ability to adapt to ever-changing scenarios and learn new knowledge continuously without forgetting previously acquired knowledge. When a machine learning model is consecutively trained on multiple tasks that come in sequence, its performance on previously learned tasks may drop dramatically during the learning process of the newly seen task. To avoid this phenomenon termed catastrophic forgetting, continual learning, also known as lifelong learning, has been proposed and become one of the most up-to-date research areas of machine learning. As quantum machine learning blossoms in recent years, it is interesting to develop quantum continual learning. This paper focuses on the case of quantum models for quantum data where the computation model and the data to be processed are both quantum. The gradient episodic memory method is incorporated to design a quantum continual learning scheme that overcomes catastrophic forgetting and realizes knowledge backward transfer. Specifically, a sequence of quantum state classification tasks is continually learned by a variational quantum classifier whose parameters are optimized by a classical gradient-based optimizer. The gradient of the current task is projected to the closest gradient, avoiding the increase of the loss at previous tasks, but allowing the decrease. Numerical simulation results show that our scheme not only overcomes catastrophic forgetting, but also realize knowledge backward transfer, which means the classifier's performance on previous tasks is enhanced rather than compromised while learning a new task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
