绝热优化的大多数实验和理论研究使用随机哈密顿量，其基态仅用实非负振幅表示。这就提出了一个问题，经典的蒙特卡罗方法是否可以模拟随机绝热算法与多项式开销。在这里，我们分析扩散蒙特卡罗算法。我们认为，L1和L2归一化状态之间的差异的基础上，这些算法遭受一定的障碍，防止他们有效地模拟随机绝热演化的一般性。然而，在实践中，我们通过引入一种方法，我们称之为Substochastic Monte Carlo获得良好的性能。事实上，我们的模拟是很好的经典优化算法在自己的权利，竞争力与最好的以前已知的启发式求解器MAX-k-SAT在k= 2，3，4。
英文摘要：
Most experimental and theoretical studies of adiabatic optimization use stoquastic Hamiltonians, whose ground states are expressible using only real nonnegative amplitudes. This raises a question as to whether classical Monte Carlo methods can simulate stoquastic adiabatic algorithms with polynomial overhead. Here, we analyze diffusion Monte Carlo algorithms. We argue that, based on differences between L1 and L2 normalized states, these algorithms suffer from certain obstructions preventing them from efficiently simulating stoquastic adiabatic evolution in generality. In practice however, we obtain good performance by introducing a method that we call Substochastic Monte Carlo. In fact, our simulations are good classical optimization algorithms in their own right, competitive with the best previously known heuristic solvers for MAX-k-SAT at k=2,3,4.
Adiabatic quantum algorithms as quantum phase transitions: first versus second order
在连续极限（大量的量子比特），绝热量子算法显示出一个显着的相似性，通过量子相变扫描。我们发现，第二或更高的顺序相比，那些一阶的过渡是有利的。有了这个洞察力，我们提出了一种新的绝热量子算法来解决3-SAT问题（精确覆盖），根据数值模拟（高达20个量子比特），它比以前的建议要快得多。这些发现表明，绝热量子算法可以比Grover搜索程序（产生二次增强）更快地解决NP完全问题，例如3-SAT，甚至可能具有指数加速。PACS：03.67。- a，03.67.Lx，73.43.Nq，64.70.- p.
The measurement of the tunneling time in attosecond experiments, termed attoclock, triggered a hot debate about the tunneling time, the role of time in quantum mechanics, where the interaction with the laser pulse involves two regimes of a different character, the multiphoton and the tunneling (field-) ionization. In the adiabatic field calibration, one of us (O. K.) developed in earlier works a real tunneling time model and showed that the model fits well to the experimental data of Landsmann et al. (Optica {\bf 1}, 343 2014). In the present work, it is shown that the model explains the exper…
Quantum annealing is a practical approach to approximately implement the adiabatic quantum computational model under a real-world setting. The goal of an adiabatic algorithm is to prepare the ground state of a problem-encoded Hamiltonian at the end of an annealing path. This is typically achieved by driving the dynamical evolution of a quantum system slowly to enforce adiabaticity. Properly optimized annealing schedules often significantly accelerate the computational process. Inspired by the recent success of deep reinforcement learning such as DeepMind's AlphaZero, we propose a Monte Carlo T…
