论文标题
提高基于学习的错误缓解效率
Improving the efficiency of learning-based error mitigation
论文作者
论文摘要
缓解错误将在近期嘈杂量子计算机的实际应用中发挥重要作用。当前的缓解方法通常集中在校正质量上，以节俭为代价（如量子硬件的其他调用数量衡量）。为了满足高度准确但廉价技术的需求，我们引入了基于Clifford数据回归（CDR）的错误缓解方案。该方案通过仔细选择训练数据并利用问题的对称性来改善节俭。我们通过在IBM多伦多量子计算机上纠正XY Hamiltonian基态的远距离相关器来测试我们的方法。我们发现我们的方法是便宜的数量级，同时保持与原始CDR方法相同的准确性。效率的增长使我们能够获得$ 10 $提高的$ 10 $提高，而总预算的总预算小至$ 2 \ cdot10^5 $ hots。此外，我们证明了通过IBM的源自源性噪声模型模拟LIH基态能量的节俭的数量级改善。
Error mitigation will play an important role in practical applications of near-term noisy quantum computers. Current error mitigation methods typically concentrate on correction quality at the expense of frugality (as measured by the number of additional calls to quantum hardware). To fill the need for highly accurate, yet inexpensive techniques, we introduce an error mitigation scheme that builds on Clifford data regression (CDR). The scheme improves the frugality by carefully choosing the training data and exploiting the symmetries of the problem. We test our approach by correcting long range correlators of the ground state of XY Hamiltonian on IBM Toronto quantum computer. We find that our method is an order of magnitude cheaper while maintaining the same accuracy as the original CDR approach. The efficiency gain enables us to obtain a factor of $10$ improvement on the unmitigated results with the total budget as small as $2\cdot10^5$ shots. Furthermore, we demonstrate orders of magnitude improvements in frugality for mitigation of energy of the LiH ground state simulated with IBM's Ourense-derived noise model.
