Bremner Michael J, Montanaro Ashley, Shepherd Dan J
Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
School of Mathematics, University of Bristol, Bristol BS8 1TW, United Kingdom.
Phys Rev Lett. 2016 Aug 19;117(8):080501. doi: 10.1103/PhysRevLett.117.080501. Epub 2016 Aug 18.
We use the class of commuting quantum computations known as IQP (instantaneous quantum polynomial time) to strengthen the conjecture that quantum computers are hard to simulate classically. We show that, if either of two plausible average-case hardness conjectures holds, then IQP computations are hard to simulate classically up to constant additive error. One conjecture relates to the hardness of estimating the complex-temperature partition function for random instances of the Ising model; the other concerns approximating the number of zeroes of random low-degree polynomials. We observe that both conjectures can be shown to be valid in the setting of worst-case complexity. We arrive at these conjectures by deriving spin-based generalizations of the boson sampling problem that avoid the so-called permanent anticoncentration conjecture.
我们使用一类被称为IQP(瞬时量子多项式时间)的可交换量子计算来强化量子计算机难以被经典模拟这一猜想。我们证明,如果两个看似合理的平均情况硬度猜想中的任何一个成立,那么IQP计算在常数加法误差范围内难以被经典模拟。一个猜想涉及估计伊辛模型随机实例的复温度配分函数的硬度;另一个则关乎逼近随机低次多项式的零点数量。我们观察到,在最坏情况复杂度的设定下,这两个猜想都能被证明是有效的。我们通过推导玻色子采样问题基于自旋的推广来得出这些猜想,该推广避免了所谓的行列式反集中猜想。