Abstract Realization of integrated quantum photonics is a key step toward scalable quantum applications such as quantum computing, sensing, information processing, and quantum material metrology. To enable practical quantum photonic systems, several challenges should be addressed, including (i) the realization of deterministic, bright, and stable single-photon emission operating at THz rates and at room temperatures, (ii) on-chip integration of efficient single-photon sources, and (iii) the development of deterministic and scalable nanoassembly of quantum circuitry elements. In this Perspective, we focus on the emerging field of physics-informed machine learning (ML) quantum photonics that is envisioned to play a decisive role in addressing the above challenges. Specifically, three directions of ML-assisted quantum research are discussed: (i) rapid preselection of single single-photon sources via ML-assisted quantum measurements, (ii) hybrid ML-optimization approach for developing efficient quantum circuits elements, and (iii) ML-based frameworks for developing novel deterministic assembly of on-chip quantum emitters.
Abstract Realization of integrated quantum photonics is a key step toward scalable quantum applications such as quantum computing, sensing, information processing, and quantum material metrology. To enable practical quantum photonic systems, several challenges should be addressed, including (i) the realization of deterministic, bright, and stable single-photon emission operating at THz rates and at room temperatures, (ii) on-chip integration of efficient single-photon sources, and (iii) the development of deterministic and scalable nanoassembly of quantum circuitry elements. In this Perspective, we focus on the emerging field of physics-informed machine learning (ML) quantum photonics that is envisioned to play a decisive role in addressing the above challenges. Specifically, three directions of ML-assisted quantum research are discussed: (i) rapid preselection of single single-photon sources via ML-assisted quantum measurements, (ii) hybrid ML-optimization approach for developing efficient quantum circuits elements, and (iii) ML-based frameworks for developing novel deterministic assembly of on-chip quantum emitters.
The development of on-chip, CMOS-compatible quantum photonics is critical for future scalable quantum communications, quantum computing, and quantum sensing. Integrated photonic waveguides, photonic resonators, and single-photon emitters are essential building blocks for such a purpose. In this talk, I will present how machine learning (ML) can enhance the quantum properties of these building blocks, specifically the indistinguishability (I) of the generated single photons, with a further decrease in quantum decoherence.
Single photon emitters represent a critical component for enabling several quantum technologies such as quantum communication, quantum photonic computing, sensing, and imaging. One example of quantum imaging that leverages the non-classical nature of light radiated by quantum emitters is the so-called antibunching super-resolution microscopy. We present a machine learning (ML)-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum on-chip devices hosting various types of quantum emitters as well as quantum material metrology. Additionally, for security applications, we develop a robust, ML-assisted on-chip device testing method that can be used to ensure the integrity of a chip avoiding risks of tampering and failure. Both ML approaches could pave the way to the realization of large-scale, secure quantum on-chip devices.
In recent years, research on integrated quantum circuits has developed rapidly and exciting results have been achieved. The overarching goal of this emerging research direction in the field of modern quantum technology is the scalable integration of quantum functionality on robust chips. Such chips can work independently of one another, but it is even more interesting to develop them modularly for integration into larger quantum networks, thereby linking quantum computation and quantum communication in the same framework. In this context, the ongoing development and further optimization of integrated quantum circuits aim, inter alia, to achieve a quantum advantage in the area of quantum computing and to pave the way for multipartite quantum networks. The functionality of such chips is essentially based on single-photon operations, such as interference at beam splitters in combination with phase shifters in the field of linear optical quantum computing and Bell-state measurements for entanglement swapping in long-distance quantum networks. While individual functionalities such as CNOT gates and more complex quantum computing operations such as boson sampling in a combination of waveguide chips and external photon sources and detectors were successfully demonstrated, the field is currently facing the major challenge of integrating all necessary components monolithically on chip in order to exploit the full potential of integrated quantum nanophotonics. The present Perspective discusses the status and the present challenges of integrated quantum nanophotonics based on on-demand single-photon emitters and gives an outlook on required developments to enter the next level of fully functional quantum circuits for photonic quantum technology.
Abstract Integrated photonics will play a key role in quantum systems as they grow from few-qubit prototypes to tens of thousands of qubits. The underlying optical quantum technologies can only be realized through the integration of these components onto quantum photonic integrated circuits (QPICs) with accompanying electronics. In the last decade, remarkable advances in quantum photonic integration have enabled table-top experiments to be scaled down to prototype chips with improvements in efficiency, robustness, and key performance metrics. These advances have enabled integrated quantum photonic technologies combining up to 650 optical and electrical components onto a single chip that are capable of programmable quantum information processing, chip-to-chip networking, hybrid quantum system integration, and high-speed communications. In this roadmap article, we highlight the status, current and future challenges, and emerging technologies in several key research areas in integrated quantum photonics, including photonic platforms, quantum and classical light sources, quantum frequency conversion, integrated detectors, and applications in computing, communications, and sensing. With advances in materials, photonic design architectures, fabrication and integration processes, packaging, and testing and benchmarking, in the next decade we can expect a transition from single- and few-function prototypes to large-scale integration of multi-functional and reconfigurable devices that will have a transformative impact on quantum information science and engineering.
The goal of integrated quantum photonics is to combine components for the generation, manipulation, and detection of nonclassical light in a phase-stable and efficient platform. Solid-state quantum emitters have recently reached outstanding performance as single-photon sources. In parallel, photonic integrated circuits have been advanced to the point that thousands of components can be controlled on a chip with high efficiency and phase stability. Consequently, researchers are now beginning to combine these leading quantum emitters and photonic integrated circuit platforms to realize the best properties of each technology. In this paper, we review recent advances in integrated quantum photonics based on such hybrid systems. Although hybrid integration solves many limitations of individual platforms, it also introduces new challenges that arise from interfacing different materials. We review various issues in solid-state quantum emitters and photonic integrated circuits, the hybrid integration techniques that bridge these two systems, and methods for chip-based manipulation of photons and emitters. Finally, we discuss the remaining challenges and future prospects of on-chip quantum photonics with integrated quantum emitters.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.