标题： 将量子比特噪声转化为优势：在量子计算机上杂质模型的自动状态制备和长时间动力学 显示英文标题

标题： Turning qubit noise into an advantage: Automatic state preparation and long-time dynamics for impurity models on quantum computers

摘要： 噪声通常被视为量子计算机的限制。 在本工作中，我们表明，在强关联体系的动力学平均场理论（DMFT）方法中，它实际上可以被利用来获得优势。 确实，DMFT将晶格模型映射到一个杂质模型，即一个与耗散浴耦合的有限系统。 虽然标准方法需要大量高质量的量子比特在单位制背景下，我们提出了一种电路，利用幅度阻尼来用噪声和无噪声量子比特的混合再现该模型的动力学。 我们发现这种方法有明显的优点：量子比特数量大幅减少，能够达到更长的时间动力学，并且不需要进行基态搜索和制备。 这种方法自然适合部分量子纠错框架。 显示英文摘要

摘要： Noise is often regarded as a limitation of quantum computers. In this work, we show that in the dynamical mean field theory (DMFT) approach to strongly-correlated systems, it can actually be harnessed to our advantage. Indeed, DMFT maps a lattice model onto an impurity model, namely a finite system coupled to a dissipative bath. While standard approaches require a large number of high-quality qubits in a unitary context, we propose a circuit that harvests amplitude damping to reproduce the dynamics of this model with a blend of noisy and noiseless qubits. We find compelling advantages with this approach: a substantial reduction in the number of qubits, the ability to reach longer time dynamics, and no need for ground state search and preparation. This method would naturally fit in a partial quantum error correction framework.