A versatile single-photon-based quantum computing platform

TLDR

Quantum computing seeks to harness quantum phenomena for tasks beyond classical reach, and photonic approaches offer low decoherence, modest cryogenic needs, and easy integration with classical and quantum networks, though demonstrations have largely been limited to specialized tasks such as Gaussian boson sampling. The authors present a cloud‑accessible, versatile quantum‑computing prototype built around single photons. The platform couples a high‑efficiency quantum‑dot single‑photon source to a reconfigurable linear‑optical network, uses machine‑learned transpilation to correct hardware errors, and provides a remote software stack that supports gate‑based, variational, and native photonic computations. Benchmarks reveal one‑, two‑ and three‑qubit gate fidelities of 99.6 %, 93.8 % and 86 %, while the device achieves chemical‑accuracy variational quantum eigensolver results for H₂, a three‑photon quantum neural‑network classifier, six‑photon boson sampling, and heralded three‑photon entanglement generation.

Abstract

Abstract 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.