Photonic integrated circuits provide a scalable platform for photonics-based quantum technologies. However, integrating quantum emitters and electro-optic cavities within this platform remains an open challenge proving to be a major hurdle from implementing key functionalities for quantum photonics, such as single photon sources and nonlinearities. Here, we address this shortcoming with the hybrid integration of InAs/InP quantum dot emitters on foundry silicon photonics and the implementation of photonic crystal cavities in thin-film lithium niobate. Co-integrated on-chip electronics allow us to tune the emission properties of the quantum dots while enabling GHz-rate coherent modulation over photons trapped in the cavities, thus providing a new level of programmability over interactions between optical fields and atom-like systems in integrated circuits. Our results open the door to a new generation of quantum information processors that can be manufactured in leading semiconductor foundries.
We route the single photons from a trapped barium ion in a nanophotonic circuit. For this routing, we first generate C-band telecom single photons from barium ion which makes them compatible with the silicon-nitride photonic foundry. Then using the thermo-optic property of silicon-nitride, we switch the single photons in a Mach-Zehnder interferometer controlling the current of the phase-shifter. These results could enable a new generation of compact and reconfigurable integrated photonic devices that can serve as efficient quantum interconnects for quantum computers and sensors.
The generation and manipulation of quantum states of light has historically played a critical role in the development of quantum information science: from the first violation of Bell’s inequality to the more recent development of near-term quantum algorithms such as the variational quantum eigensolver. In this talk, I present a new frontier for photons at the intersection of quantum mechanics and machine learning. I will first provide a short introduction to the field of quantum photonics, then demonstrate how quantum photonic processors can accelerate both quantum and classical machine learning. Finally, I show how optimization techniques can enhance large-scale quantum control and provide a new path towards efficient verification of near-term quantum processors.
Photons play a central role in many areas of quantum information science, either as qubit themselves or to mediate interactions between long-lived matter based qubits. Techniques for (1) high-fidelity generation, (2) precise manipulation and (3) ultra-efficient detection of quantum states of light are therefore a prerequisite for virtually all quantum technologies. A quantum photonics processor is the union of these three core technologies into a single system, and, bolstered by advances in integrated photonics, promises to be a versatile platform for quantum information science. In this talk we present recent progress towards large-scale quantum photonic processors, leveraging the platform of silicon photonics. We demonstrate how quantum photonic processors can accelerate both quantum and classical machine learning, and how optimization techniques can enhance large-scale quantum control.
Photons play a central role in many areas of quantum information science, either as qubit themselves or to mediate interactions between long-lived matter based qubits. Techniques for (1) high-fidelity generation, (2) precise manipulation and (3) ultra-efficient detection of quantum states of light are therefore a prerequisite for virtually all quantum technologies. A quantum photonics processor is the union of these three core technologies into a single system, and, bolstered by advances in integrated photonics, promises to be a versatile platform for quantum information science. In this talk we present recent progress towards large-scale quantum photonic processors, leveraging the platform of silicon photonics. We demonstrate how quantum photonic processors can accelerate both quantum and classical machine learning, and how optimization techniques can enhance large-scale quantum control and provide a new path towards efficient verification of near-term quantum processors.
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