Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to $16$ qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging.

翻译：量子机器学习有望对各个行业领域产生变革性影响，尤其在金融领域。本研究聚焦于对冲问题，深度强化学习为此类真实市场问题提供了强大框架。我们基于策略搜索和分布式演员-评论家算法开发了量子强化学习方法，该方法采用具有正交层与复合层的量子神经网络架构来构建策略函数与价值函数。我们证明了所用量子神经网络的可训练性，并通过大量仿真表明：量子模型在减少可训练参数数量的同时保持可比性能，而分布式方法相较于其他经典与量子标准方法可获得更优表现。我们成功在囚禁离子量子处理器上实现了所提出的模型，采用含多达16个量子比特的量子电路，观察到与无噪声仿真高度吻合的性能。我们的量子技术具有通用性，可应用于对冲之外的其他强化学习问题。