The big theme.
Work that connects silicon photonics, quantum computing, and artificial intelligence.
Experimental quantum speed-up in reinforcement learning agents.
Nature 591, 229-233 (2021).
Here we present a reinforcement learning experiment in which the learning process of an agent is sped up by using a quantum communication channel with the environment. We further show that combining this scenario with classical communication enables the evaluation of this improvement and allows optimal control of the learning progress. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor. The device interfaces with telecommunication-wavelength photons and features a fast active-feedback mechanism, demonstrating the agent’s systematic quantum advantage in a setup that could readily be integrated within future large-scale quantum communication networks.
Variational quantum unsampling on a quantum photonic processor.
Nature Physics 16, 322–327 (2020).
Here we introduce the variational quantum unsampling protocol, a nonlinear quantum neural network approach for verification and inference of near-term quantum circuit outputs. In our approach, one can variationally train a quantum operation to unravel the action of an unknown unitary on a known input state, essentially learning the inverse of the black-box quantum dynamics. While the principle of our approach is platform independent, its implementation will depend on the unique architecture of a specific quantum processor. We experimentally demonstrate the variational quantum unsampling protocol on a quantum photonic processor. Alongside quantum verification, our protocol has broad applications, including optimal quantum measurement and tomography, quantum sensing and imaging, and ansatz validation.
Quantum transport simulations in a programmable nanophotonic processor.
Nature Photonics 11, 447–452 (2017).
Here, we fully map the role of disorder in quantum transport using a nanophotonic processor: a mesh of 88 generalized beamsplitters programmable on microsecond timescales. Over 64,400 experiments we observe distinct transport regimes, including environment-assisted quantum transport and the ‘quantum Goldilocks’ regime in statically disordered discrete-time systems. Low-loss and high-fidelity programmable transformations make this nanophotonic processor a promising platform for many-boson quantum simulation experiments.
Deep learning with coherent nanophotonic circuits.
Nature Photonics 11, 441–446 (2017).
Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach–Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.
On-chip detection of non-classical light by scalable integration of single-photon detectors.
Nature Comms. 6, 5873 (2015).
Here we introduce a micrometer-scale flip-chip process that enables scalable integration of SNSPDs on a range of photonic circuits. Ten low-jitter detectors are integrated on one circuit with 100% device yield. With an average system detection efficiency beyond 10%, and estimated on-chip detection efficiency of 14–52% for four detectors operated simultaneously, we demonstrate, to the best of our knowledge, the first on-chip photon correlation measurements of non-classical light.
Integrated Source of Spectrally Filtered Correlated Photons for Large-Scale Quantum Photonic Systems.
Phys. Rev. X 4, 041047 (2014).
We demonstrate the generation of quantum-correlated photon pairs combined with the spectral filtering of the pump field by more than 95 dB on a single silicon chip using electrically tunable ring resonators and passive Bragg reflectors. Moreover, we perform the demultiplexing and routing of signal and idler photons after transferring them via an optical fiber to a second identical chip. Nonclassical two-photon temporal correlations with a coincidence-to-accidental ratio of 50 are measured without further off-chip filtering. Our system, fabricated with high yield and reproducibility in a CMOS-compatible process, paves the way toward large-scale quantum photonic circuits by allowing sources and detectors of single photons to be integrated on the same chip.