Quafu-Qcover:Explore combinatorial optimization problems on cloud-based quantum computers

We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.

海水温度对北海渔业经营策略的影响

北海海水温度在不断上升,海水水温的变化使得鱼群向更高纬度进行迁移,导致原先北海区域的小型捕鱼产业遭受到了影响。为了清楚地了解该现象在未来的五十年内会对北海海域捕鱼业的经营策略产生什么影响,建立了回归分析预测模型、模糊综合评价模型。首先得到了时间和鱼群位置的关系,研究小组认为对苏格兰小型捕捞公司的最坏情况是在2046年时,鲱鱼与鲭鱼迁徙超出捕鱼范围,最好情况是在2053年鲱鱼与鲭鱼鱼群迁徙超出捕鱼范围,最可能发生的情况是2050年,鱼群迁徙超出捕鱼范围。接着研究小组在考察小渔业公司经营计划时,通过建立模糊综合评价模型分析得出发生着变化苏格兰海域的小型捕捞公司一定需要进行经营计划的改变,接着又通过模糊评价模型进一步确定减少人工成本为最合适的经营策略。

共渔区对苏格兰捕鱼业影响

由于全球气候变暖导致的全球海水温度的逐年上升,2019年的海水温度打破了以往的纪录,到达了一个新的高值。海水表面温度的升高使得鱼群可向更高纬度进行迁移,这样的迁徙会导致原先区域的小型捕鱼业遭受到致命影响。为了更加清楚地了解在未来五十年内全球海水温度上升,鱼群向高纬度迁徙会对苏格兰地区的捕鱼业造成什么影响,当鱼群迁徙到别的国家时,共渔区会对小型捕捞公司造成什么影响。据此我们小组查阅相关文献后使用模拟退火模型进行优化共渔区对公司的影响。根据运算结果得出,共渔区会对小型捕捞公司造成影响,造成捕鱼产量受限、员工成本增加,但影响效果并不严重。

鲱鱼鲭鱼迁徙对北海渔业的影响

由于全球海水温度的持续上涨,2019年海水温度达到了新的高度,海水温度的升高使得鱼群向高纬度进行了迁移,导致原先区域的捕鱼业受到影响。为了清楚地了解这种现象在未来的五十年内会对苏格兰地区的捕鱼业造成什么影响,我们小组进行了建模分析。我们首先通过获取英国气象局哈德莱中心提供的全球海水表面温度和阅读文献明确了鱼群分布位置及生活区域的最适温度,从而进行区域分割,将研究海域定在挪威海、格陵兰海及北海。