基于遗传优化算法的SU(2)k（k=5,6,7）任意子模型通用量子门集构建

Optimized Dynamical Decoupling via Genetic Algorithms

We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlie…

Genetic optimization of quantum annealing

The study of optimal control of quantum annealing by modulating the pace of evolution and by introducing a counterdiabatic potential has gained significant attention in recent times. In this work, we present a numerical approach based on genetic algorithms to improve the performance of quantum annealing, which evades the Landau-Zener transitions to navigate to the ground state of the final Hamiltonian with high probability. We optimize the annealing schedules starting from polynomial ansatz by treating their coefficients as chromosomes of the genetic algorithm. We also explore shortcuts to adi…

弱束缚分子的振动梯级下降光稳定化：采用遗传算法的量子最优控制

Vibrational ladder-descending photostabilization of a weakly bound molecule: Quantum optimal control with a genetic algorithm

本研究提出一种基于遗传算法优化参数的红外啁啾激光脉冲光学控制方案，可在同一电子态内将极性分子从高位振动能级驱动至低位能级，并通过KRb Feshbach分子的计算验证了方法有效性。

Optimized Entanglement Purification

We investigate novel protocols for entanglement purification of qubit Bell pairs. Employing genetic algorithms for the design of the purification circuit, we obtain shorter circuits achieving higher success rates and better final fidelities than what is currently available in the literature. We provide a software tool for analytical and numerical study of the generated purification circuits, under customizable error models. These new purification protocols pave the way to practical implementations of modular quantum computers and quantum repeaters. Our approach is particularly attentive to the…

Genetic algorithm for searching bipolar Single-Flux-Quantum pulse sequences for qubit control

Nowadays most of superconducting quantum processors use charge qubits of a transmon type. They require implementation of energy efficient qubit state control scheme. A promising approach is the use of superconducting digital circuits operating with single-flux-quantum (SFQ) pulses. The duration of SFQ pulse control sequence is typically larger than that of conventional microwave drive pulses but its length can be optimized for the system with known parameters. Here we introduce a genetic algorithm for unipolar or bipolar SFQ control sequence search that minimize qubit state leakage from the co…

Optimal Qubit Control Using Single-Flux Quantum Pulses

Single flux quantum pulses are a natural candidate for on-chip control of superconducting qubits. We show that they can drive high-fidelity single-qubit rotations---even in leaky transmon qubits---if the pulse sequence is suitably optimized. We achieve this objective by showing that, for these restricted all-digital pulses, genetic algorithms can be made to converge to arbitrarily low error, verified up to a reduction in gate error by 2 orders of magnitude compared to an evenly spaced pulse train. Timing jitter of the pulses is taken into account, exploring the robustness of our optimized sequ…

Quantum autoencoders via quantum adders with genetic algorithms

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimi…

MoG-VQE: Multiobjective genetic variational quantum eigensolver

Variational quantum eigensolver (VQE) emerged as a first practical algorithm for near-term quantum computers. Its success largely relies on the chosen variational ansatz, corresponding to a quantum circuit that prepares an approximate ground state of a Hamiltonian. Typically, it either aims to achieve high representation accuracy (at the expense of circuit depth), or uses a shallow circuit sacrificing the convergence to the exact ground state energy. Here, we propose the approach which can combine both low depth and improved precision, capitalizing on a genetically-improved ansatz for hardware…