Quantum computing aims at exploiting quantum phenomena to efficiently perform computations that are unfeasible even for the most powerful classical supercomputers. Among the promising technological approaches, photonic quantum computing offers the advantages of low decoherence, information processing with modest cryogenic requirements, and native integration with classical and quantum networks. So far, quantum computing demonstrations with light have implemented specific tasks with specialized hardware, notably Gaussian boson sampling, which permits the quantum computational advantage to be realized. Here we report a cloud-accessible versatile quantum computing prototype based on single photons. The device comprises a high-efficiency quantum-dot single-photon source feeding a universal linear optical network on a reconfigurable chip for which hardware errors are compensated by a machine-learned transpilation process. Our full software stack allows remote control of the device to perform computations via logic gates or direct photonic operations. For gate-based computation, we benchmark one-, two- and three-qubit gates with state-of-the art fidelities of 99.6 ± 0.1%, 93.8 ± 0.6% and 86 ± 1.2%, respectively. We also implement a variational quantum eigensolver, which we use to calculate the energy levels of the hydrogen molecule with chemical accuracy. For photon native computation, we implement a classifier algorithm using a three-photon-based quantum neural network and report a six-photon boson sampling demonstration on a universal reconfigurable integrated circuit. Finally, we report on a heralded three-photon entanglement generation, a key milestone toward measurement-based quantum computing.

量子计算旨在利用量子现象来有效地执行即使对于最强大的经典超级计算机也是不可行的计算。在有前途的技术方法中，光子量子计算具有低退相干、具有适度低温要求的信息处理以及与经典和量子网络的本机集成等优点。到目前为止，光量子计算演示已经使用专用硬件实现了特定任务，特别是高斯玻色子采样，这使得量子计算优势得以实现。在这里，我们报告了一种基于单光子的可云访问的多功能量子计算原型。该设备包括一个高效量子点单光子源，为可重构芯片上的通用线性光网络提供信号，该芯片的硬件错误通过机器学习的转译过程进行补偿。我们完整的软件堆栈允许远程控制设备，通过逻辑门或直接光子操作执行计算。对于基于门的计算，我们对一量子位门、二量子位门和三量子位门进行基准测试，其最先进的保真度分别为 99.6 ± 0.1%、93.8 ± 0.6% 和 86 ± 1.2%。我们还实现了一个变分量子本征求解器，用于以化学精度计算氢分子的能级。对于光子本机计算，我们使用基于三光子的量子神经网络实现分类器算法，并报告在通用可重构集成电路上的六光子玻色子采样演示。最后，我们报告了三光子纠缠的产生，这是基于测量的量子计算的一个重要里程碑。