Abstract Orbital angular momentum (OAM) detection underpins almost all aspects of vortex beams’ advances such as communication and quantum analogy. Conventional schemes are frustrated by low speed, complicated system, limited detection range. Here, we devise an intelligent processor composed of photonic and electronic neurons for OAM spectrum measurement in a fast, accurate and direct manner. Specifically, optical layers extract invisible topological charge information from incoming light and a shallow electronic layer predicts the exact spectrum. The integration of optical-computing promises us a compact single-shot system with high speed and energy efficiency (optical operations / electronic operations ~ $${10}^{3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mn>3</mml:mn> </mml:msup> </mml:math> ), neither necessitating reference wave nor repetitive steps. Importantly, our processor is endowed with salient generalization ability and robustness against diverse structured light and adverse effects (mean squared error ~ $$10^{(-5)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>(</mml:mo> <mml:mo>-</mml:mo> <mml:mn>5</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:msup> </mml:math> ). We further raise a universal model interpretation paradigm to reveal the underlying physical mechanisms in the hybrid processor, as distinct from conventional ‘black-box’ networks. Such interpretation algorithm can improve the detection efficiency up to 25-fold. We also complete the theory of optoelectronic network enabling its efficient training. This work not only contributes to the explorations on OAM physics and applications, and also broadly inspires the advanced links between intelligent computing and physical effects. 

/pdf/intelligent-optoelectronic-processor-for-orbital-angular-3gc3aj4q.pdf

Intelligent optoelectronic processor for orbital angular momentum spectrum measurement

Quantum photonic neural networks are variational photonic circuits that can be trained to implement high‐fidelity quantum operations. However, work‐to‐date has assumed idealized components, including a perfect π Kerr nonlinearity. This work investigates the limitations of non‐ideal quantum photonic neural networks that suffer from fabrication imperfections leading to unbalanced photon loss and imperfect routing, and weak nonlinearities, showing that they can learn to overcome most of these errors. Using the example of a Bell‐state analyzer, the results demonstrate that there is an optimal network size, which balances imperfections versus the ability to compensate for lacking nonlinearities. With a sub‐optimal π/10$\pi /10$ effective Kerr nonlinearity, it is shown that a network fabricated with current state‐of‐the‐art processes can achieve an unconditional fidelity of 0.905 that increases to 0.999999 if it is possible to precondition success on the detection of a photon in each logical photonic qubit. These results provide a guide to the construction of viable, brain‐inspired quantum photonic devices for emerging quantum technologies.

Imperfect Quantum Photonic Neural Networks

Software-defined nanophotonic devices and systems empowered by machine learning

Abstract. Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the “imaging” technique in quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. We build and demonstrate an optical neural network (ONN) for photonic polarization qubit QST. The ONN is equipped with built-in optical nonlinear activation functions based on electromagnetically induced transparency. The experimental results show that our ONN can determine the phase parameter of the qubit state accurately. As optics are highly desired for quantum interconnections, our ONN-QST may contribute to the realization of optical quantum networks and inspire the ideas combining artificial optical intelligence with quantum information studies.

https://www.spiedigitallibrary.org/journals/advanced-photonics/volume-4/issue-2/026004/Optical-neural-network-quantum-state-tomography/10.1117/1.AP.4.2.026004.pdf

Optical neural network quantum state tomography

Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the "imaging" technique in the quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. In this work, we build an integrated all-optical setup for neural network QST, based on an all-optical neural network (AONN). Our AONN is equipped with built-in nonlinear activation function, which is based on electromagnetically induced transparency. Experiment results demonstrate the validity and efficiency of the all-optical setup, indicating that AONN can mitigate the state-preparation-and-measurement error and predict the phase parameter in the quantum state accurately. Given that optical setups are highly desired for future quantum networks, our all-optical setup of integrated AONN-QST may shed light on replenishing the all-optical quantum network with the last brick.

All-optical neural network quantum state tomography.

Back Cover: Distributed Quantum Computing with Photons and Atomic Memories (Adv. Quantum Technol. 6/2023)

The promise of universal quantum computing requires scalable single‐ and inter‐qubit control interactions. Currently, three of the leading candidate platforms for quantum computing are based on superconducting circuits, trapped ions, and neutral atom arrays. However, these systems have strong interaction with environmental and control noises that introduce decoherence of qubit states and gate operations. Alternatively, photons are well decoupled from the environment and have advantages of speed and timing for quantum computing. Photonic systems have already demonstrated capability for solving specific intractable problems like Boson sampling, but face challenges for practically scalable universal quantum computing solutions because it is extremely difficult for a single photon to “talk” to another deterministically. Here, a universal distributed quantum computing scheme based on photons and atomic‐ensemble‐based quantum memories is proposed. Taking the established photonic advantages, two‐qubit nonlinear interaction is mediated by converting photonic qubits into quantum memory states and employing Rydberg blockade for the controlled gate operation. Spatial and temporal scalability of this scheme is demonstrated further. These results show photon‐atom network hybrid approach can be a potential solution to universal distributed quantum computing.

/pdf/distributed-quantum-computing-with-photons-and-atomic-214p80kp.pdf

Xuanying Lai

Papers

Optical neural network quantum state tomography

All-optical neural network quantum state tomography.

Back Cover: Distributed Quantum Computing with Photons and Atomic Memories (Adv. Quantum Technol. 6/2023)

Distributed Quantum Computing with Photons and Atomic Memories