A versatile single-photon-based quantum computing platform

A versatile single-photon-based quantum computing platform

26 March 2024 | Nicolas Maring, Andreas Fyrillas, Mathias Pont, Edouard Ivanov, Petr Stepanov, Nico Margaria, William Hease, Anton Pishchagin, Aristide Lemaitre, Isabelle Sagnes, Thi Huong Au, Sebastien Boissier, Eric Bertasi, Aurelien Baert, Mario Valdivia, Marie Billard, Ozan Acar, Alexandre Brieussel, Rawad Mezher, Stephen C. Wein, Alexia Salavrakos, Patrick Sinnott, Dario A. Fioretto, Pierre-Emmanuel Emeriau, Nadia Belabas, Shane Mansfield, Pascale Senellart, Jean Senellart, Niccolò Somaschi
The article presents a versatile single-photon-based quantum computing platform, named Ascella, which is accessible via a cloud service. Ascella consists of a high-efficiency quantum-dot single-photon source, a universal linear optical network on a reconfigurable chip, and a machine-learned transpilation process to correct hardware errors. The platform supports gate-based and photonic computation frameworks, achieving state-of-the-art fidelities for one-, two-, and three-qubit gates. It also implements a variational quantum eigensolver for calculating the energy levels of the hydrogen molecule with chemical accuracy and a photon-native quantum neural network for supervised learning classification tasks. Additionally, Ascella demonstrates heralded three-photon entanglement generation, a key step towards measurement-based quantum computing. The platform's performance and stability are validated through various benchmarks and applications, showcasing its potential for noisy near-term quantum computing tasks and large-scale fault-tolerant quantum computing.The article presents a versatile single-photon-based quantum computing platform, named Ascella, which is accessible via a cloud service. Ascella consists of a high-efficiency quantum-dot single-photon source, a universal linear optical network on a reconfigurable chip, and a machine-learned transpilation process to correct hardware errors. The platform supports gate-based and photonic computation frameworks, achieving state-of-the-art fidelities for one-, two-, and three-qubit gates. It also implements a variational quantum eigensolver for calculating the energy levels of the hydrogen molecule with chemical accuracy and a photon-native quantum neural network for supervised learning classification tasks. Additionally, Ascella demonstrates heralded three-photon entanglement generation, a key step towards measurement-based quantum computing. The platform's performance and stability are validated through various benchmarks and applications, showcasing its potential for noisy near-term quantum computing tasks and large-scale fault-tolerant quantum computing.
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Understanding A versatile single-photon-based quantum computing platform