A new method for simulation of a binary homogeneous Markov process using a quantum computer was proposed. This new method allows using the distinguished properties of the quantum mechanical systems -- superposition, entanglement and probability calculations. Implementation of an algorithm based on this method requires the creation of a new quantum logic gate, which creates entangled state between two qubits. This is a two-qubit logic gate and it must perform a predefined rotation over the X-axis for the qubit that acts as a target, where the rotation accurately represents the transient probabilities for a given Markov process. This gate fires only when the control qubit is in state <0|. It is necessary to develop an algorithm, which uses the distribution for the transient probabilities of the process in a simple and intuitive way and then transform those into X-axis offsets. The creation of a quantum controlled n-th root of X gate using only the existing basic quantum logic gates at the available cloud platforms is possible, although the hardware devices are still too noisy, which results in a significant measurement error increase. The IBM's Yorktown 'bow-tie' back-end performs quite better than the 5-qubit T-shaped and the 14-qubit Melbourne quantum processors in terms of quantum fidelity. The simulation of the binary homogeneous Markov process on a real quantum processor gives best results on the Vigo and Yorktown (both 5-qubit) back-ends with Hellinger fidelity of near 0.82. The choice of the right quantum circuit, based on the available hardware (topology, size, timing properties), would be the approach for maximizing the fidelity.
翻译:使用量子计算机模拟二进制同质马克夫进程的新方法已经提出。 这个新方法允许使用量子机械系统的不同特性 -- -- 超位、缠绕和概率计算。 基于此方法的算法需要创建一个新的量子逻辑门, 从而在两个qubits 之间产生纠缠状态。 这是一个两平方位逻辑门, 它必须在 X 轴上进行预先定义的旋转。 Qubit 上, 旋转精确地代表给定的 Markov 过程的瞬变概率。 这个门只有在控制 qubit 处于状态 < 0\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\