Abstract The complexity of experimental quantum information processing devices is increasing rapidly, requiring new approaches to control them. In this paper, we address the problems of practically modeling and controlling an integrated optical waveguide array chip—a technology expected to have many applications in telecommunications and optical quantum information processing. This photonic circuit can be electrically reconfigured, but only the output optical signal can be monitored. As a result, the conventional control methods cannot be naively applied. Characterizing such a chip is challenging for three reasons. First, there are uncertainties associated with the Hamiltonian model describing the chip. Second, we expect distortions of the control voltages caused by the chip’s electrical response, which cannot be directly observed. And third, there are imperfections in the measurements caused by losses from coupling the chip externally to optical fibers. We have developed a deep neural network (NN) approach to solve these problems. The architecture is designed specifically to overcome the aforementioned challenges using a gated recurrent unit -based network as the central component. The Hamiltonian is estimated as a blackbox, while the rules of quantum mechanics such as state evolution is embedded in the structure as a whitebox. The resulting overall graybox model of the chip shows good performance both quantitatively in terms of the mean square error and qualitatively in terms of the shape of the predicted waveforms We use this NN to solve a classical and a quantum control problem. In the classical application we find a control sequence to approximately realize a time-dependent output power distribution. For the quantum application we obtain the control voltages to realize a target set of quantum gates. The method we propose is generic and can be applied to other systems that can only be probed indirectly.
Abstract The complexity of experimental quantum information processing devices is increasing rapidly, requiring new approaches to control them. In this paper, we address the problems of practically modeling and controlling an integrated optical waveguide array chip—a technology expected to have many applications in telecommunications and optical quantum information processing. This photonic circuit can be electrically reconfigured, but only the output optical signal can be monitored. As a result, the conventional control methods cannot be naively applied. Characterizing such a chip is challenging for three reasons. First, there are uncertainties associated with the Hamiltonian model describing the chip. Second, we expect distortions of the control voltages caused by the chip’s electrical response, which cannot be directly observed. And third, there are imperfections in the measurements caused by losses from coupling the chip externally to optical fibers. We have developed a deep neural network (NN) approach to solve these problems. The architecture is designed specifically to overcome the aforementioned challenges using a gated recurrent unit -based network as the central component. The Hamiltonian is estimated as a blackbox, while the rules of quantum mechanics such as state evolution is embedded in the structure as a whitebox. The resulting overall graybox model of the chip shows good performance both quantitatively in terms of the mean square error and qualitatively in terms of the shape of the predicted waveforms We use this NN to solve a classical and a quantum control problem. In the classical application we find a control sequence to approximately realize a time-dependent output power distribution. For the quantum application we obtain the control voltages to realize a target set of quantum gates. The method we propose is generic and can be applied to other systems that can only be probed indirectly.
Abstract Quantum devices need precise control to achieve their full capability. In this work, we address the problem of controlling closed quantum systems, tackling two main issues. First, in practice the control signals are usually subject to unknown classical distortions that could arise from the device fabrication, material properties and/or instruments generating those signals. Second, in most cases modeling the system is very difficult or not even viable due to uncertainties in the relations between some variables and inaccessibility to some measurements inside the system. In this paper, we introduce a general model-free control approach based on deep reinforcement learning (DRL), that can work for any controllable closed quantum system. We train a deep neural network (NN), using the REINFORCE policy gradient algorithm to control the state probability distribution of a controllable closed quantum system as it evolves, and drive it to different target distributions. We present a novel controller architecture that comprises multiple NNs. This enables accommodating as many different target state distributions as desired, without increasing the complexity of the NN or its training process. The used DRL algorithm works whether the control problem can be modeled as a Markov decision process (MDP) or a partially observed MDP. Our method is valid whether the control signals are discrete- or continuous-valued. We verified our method through numerical simulations based on a photonic waveguide array chip. We trained a controller to generate sequences of different target output distributions of the chip with fidelity higher than 99%, where the controller showed superior performance in canceling the classical signal distortions.
Integrated photonic technologies have recently shown significant advances, enabling the possibility to implement reconfigurable interferometers with increasing size. One of the main tasks to fully exploit the capabilities of reconfigurable integrated interferometers is the possibility to precisely program their operation to perform a desired target unitary. While recipes are known for circuit layouts based on a cascade of beam-splitter and phase-shifter operations, a methodology applicable for reconfigurable continuously-coupled waveguide arrays is currently missing. Here, we devise a machine learning based approach for this task, using a black box methodology that does not rely on precise a-priori modeling of the circuit internal architectures. We verify the effectiveness and the robustness of this approach via numerical simulations on different continuously-coupled waveguides layouts, either with planar or 3D structures. The proposed method makes use of a limited number of single- and two-photon measurements, making it suitable for optical quantum information processing. The obtained results open the perspective of employing this methodology as an effective tool to program the operation of integrated interferometers designed via different architectures.
Programmable waveguide circuits are crucial building blocks for integrated spectrometric applications and quantum photonic information processing. Amongst the dielectric material platforms in integrated photonics, silicon nitride stands out with highly attractive properties such as a large bandgap energy and a moderately high index contrast. This allows low propagation losses in a wide spectral range while simultaneously allowing a dense packing of components. The aforementioned properties, together with the inherent phase stability and phase programmability achievable in silicon nitride, enable the creation of complex photonic circuits. In this thesis we describe and demonstrate densely integrated programmable photonic circuits based on silicon nitride waveguides for wavelength metrology and quantum information processing. We concentrate on reconfigurable photonic integrated circuits based on silicon nitride waveguides with low-loss propagation, to explore interference in the spectral and temporal domain for advanced applications. We investigated two types of integrated interferometric devices featuring low loss in combination with programmability for classical and quantum photonic processing. The first is simple tunable microring resonator circuits in combination with neural network data processing for the analysis of classical light in the spectral domain as wavelength meter. The second is a complex tunable network of waveguide interferometers for controlling quantum correlations (coincidences) between single photons. Exploiting the long-term interferometric stability, low propagation loss and tight optical confinement of integrated silicon nitride waveguides, we have shown complex reconfigurable optical circuits both for classical and quantum photonic processing. For future development of integrated programmable photonic processors, various challenges need to be addressed such as compact and low-power phase shifters, a further increase of the component density and lower the propagation losses.
In recent years, optical computing becomes a powerful task-specific platform, under the increasing demand for computing power (especially for neural networks). This doctoral thesis is devoted to the design, simulation, and testing of integrated silicon photonic circuits for optical neural networks and machine-learning-assisted quantum computing tasks. In Chapter 3, we demonstrate an optical neural chip that implements complex-valued neural networks and benchmark its performance under practical settings, proving strong learning capabilities. In Chapter 4, we demonstrate an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip. In Chapter 5, we demonstrate a silicon photonic chip that achieves chip-to-chip teleportation of high-dimensional quantum states, using the trainable quantum autoencoders. High fidelities are achieved between the input qutrit and the qutrit decoded from the teleported state. Our results present a promising avenue towards realizing deep optical neural networks and challenging quantum computing tasks with dedicated integrated silicon photonic circuits.
Quantum photonics is a rapidly developing platform for future quantum network applications. Waveguide-based architectures, in which embedded quantum emitters act as both nonlinear elements to mediate photon–photon interactions and as highly coherent single-photon sources, offer a highly promising route to realize such networks. A key requirement for the scale-up of the waveguide architecture is local control and tunability of individual quantum emitters. Here, we demonstrate electrical control, tuning, and switching of the nonlinear photon–photon interaction arising due to a quantum dot embedded in a single-mode nano-photonic waveguide. A power-dependent waveguide transmission extinction as large as 40±2% is observed on resonance. Photon statistics measurements show clear, voltage-controlled bunching of the transmitted light and antibunching of the reflected light, demonstrating the single-photon, quantum character of the nonlinearity. Importantly, the same architecture is also shown to act as a source of highly coherent, electrically tunable single photons. Overall, the platform presented addresses the essential requirements for the implementation of photonic gates for scalable nano-photonic-based quantum information processing.
In recent years, quantum information has rapidly evolved as a field with transformative potential in science and technology. It provides secure encryption for communication, accurate emulation of practical physical systems, streamlined and accelerated machine learning models, and advanced computational powers that outperform classical supercomputers in solving complex problems. These advancements are all reliant on the superposition and entanglement properties of qubits. While various candidates such as trapped ions or superconducting qubits have been proposed to realize quantum technologies, photons have demonstrated a prominent advantage due to their robustness against decoherence and noise, high clock cycle rate, fast and easy measurement, and room temperature working environment. In addition, photonic integrated chip (PIC) technology has developed rapidly since the beginning of the 21st century. The number of devices integrated on a single chip has increased rapidly, and its complexity has also increased. Quantum computing combined with integrated photonics is expected to solve bottleneck problems in practical situations. In this thesis, based on the silicon-on-insulator (SOI) platform, an optical computing chip and a large-scale quantum computing chip with various practical applications are specifically studied. In the first part of the thesis, a novel integrated diffractive optical network is studied, and a scheme is designed to realize parallel Fourier transform, convolution operations, and application-specific optical computing. Due to the utilization of on-chip compact diffractive cells (slab waveguides), the footprint and power consumption of the proposed architecture is reduced from quadratic scaling in the input data dimensions required for Mach-Zehnder interferometer (MZI)-based optical neural network (ONN) architectures to linear scaling for the improved deep neural networks. A 10-fold reduction in both footprint and energy consumption and equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets. This reduction in the resource scaling from quadratic to linear has a profound impact on the realization of large-scale silicon-photonics computing circuits with current fabrication technologies. A quantum photonic microprocessor based on Gaussian Boson sampling (GBS) that offers dynamic programmability to solve various graph-related NP-complete problems is then demonstrated. The system with optical, electrical, and thermal packaging implements a GBS with 16 modes of single-mode squeezed vacuum states, a universal programmable 16-mode interferometer, and a single photon readout on all outputs with high accuracy, generality, and controllability. The developed system is applied to demonstrate applications in solving NP-complete problems, manifesting the ability of photonic quantum computing to realize practical applications for conventionally intractable computations. The GBS-based quantum photonic microprocessor is applied to solve task assignment, Boolean satisfiability, graph clique, max cut, and vertex cover. These demonstrations suggest an excellent benchmarking platform, paving the way toward large-scale combinatorial optimization. Finally, this thesis explores the application possibility of large-scale PICs in molecular simulation. A new algorithm utilizing states with zero displacements (vacuum-squeezed states) coupling to a linear network is proposed to calculate molecular vibronic spectra. With a large-scale PIC, molecular vibronic spectra are accurately simulated for formic acid and thymine with high reconstructed fidelity (>91%) and for naphthalene, phenanthrene, and benzene under the non-Condon regime. Our experimental demonstration will open pathways toward conducting large-scale molecular quantum simulations, a task surpassing classical computers’ capabilities.