Goto Hayato
Frontier Research Laboratory, Corporate Research &Development Center, Toshiba Corporation, 1, Komukai Toshiba-cho, Saiwai-ku, Kawasaki-shi, 212-8582, Japan.
Sci Rep. 2016 Feb 22;6:21686. doi: 10.1038/srep21686.
The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence.
非线性系统的动力学特性会根据其参数发生定性变化，这被称为分岔。一个量子力学非线性振荡器可以通过其分岔点进行量子绝热演化，产生两个振荡状态的量子叠加，即所谓的薛定谔猫态。在这里，我们提出一种由这种量子非线性振荡器而非量子比特组成的量子计算机，用于解决困难的组合优化问题。与传统绝热量子计算或量子退火（其中量子涨落项缓慢减小）不同，非线性振荡器网络通过量子绝热演化找到最优解，在这个过程中非线性项缓慢增加。数值模拟结果表明，量子叠加和量子涨落在寻找最优解方面能有效发挥作用。同样值得注意的是，当前的计算机类似于神经计算机，神经计算机也是由非线性组件构成的网络。因此，本方案将为量子计算、非线性科学和人工智能开辟新的可能性。
