Optical technology is a highly promising platform for quantum computing due to its enormous potential for large-scale, ultrafast computation. However, realizing a programmable and scalable system remains a significant challenge. Here, we present a high-speed programmable Gaussian quantum computing platform with one hundred inputs based on a continuous-variable full-stack architecture. Our system features a 100 MHz clock frequency and integrates a cloud-based interface with an open-source Python software development kit, mqc3, significantly enhancing accessibility and operational flexibility. We provide a comprehensive characterization of our system and its capabilities through multi-input and multi-step teleportation, as well as the programmable routing of quantum states across 101 input modes. This platform represents a critical milestone in scalable analog quantum information processing, offering a robust testbed for the future integration of non-Gaussian resources and the development of large-scale optical neural networks.
Optical technology is a highly promising platform for quantum computing due to its enormous potential for large-scale, ultrafast computation. However, realizing a programmable and scalable system remains a significant challenge. Here, we present a high-speed programmable Gaussian quantum computing platform with one hundred inputs based on a continuous-variable full-stack architecture. Our system features a 100 MHz clock frequency and integrates a cloud-based interface with an open-source Python software development kit, mqc3, significantly enhancing accessibility and operational flexibility. We provide a comprehensive characterization of our system and its capabilities through multi-input and multi-step teleportation, as well as the programmable routing of quantum states across 101 input modes. This platform represents a critical milestone in scalable analog quantum information processing, offering a robust testbed for the future integration of non-Gaussian resources and the development of large-scale optical neural networks.
Quantum computing holds the potential to revolutionize fields such as cryptography, optimization, and quantum simulations. Among various quantum computing approaches, optical quantum computers are particularly promising due to their high-speed operations, scalability, and compatibility with communication technologies [1]. Recent advances in time-domain multiplexing have demonstrated the proof of principle of large-scale analog quantum computation in optical setups [2], [3]. However, previous systems have been limited by the number of inputs, clock frequency, and flexibility.
Continuous-variables (CV) quantum optics is a natural formalism for neural networks (NNs) due to its ability to reproduce the information processing of such trainable interconnected systems. In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes while non-Gaussian resources (the challenging piece for physical implementation) originate the nonlinear effects, unlocking quantum analogs of an artificial neuron. This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks (QONNs) developed with available photonic components: coherent states as input encoding, a general Gaussian transformation followed by multi-mode photon subtractions as the processing layer, and homodyne detection as outputs readout. The closed-form expressions of such architecture are derived demonstrating the family of adaptive activations and the quantum-optical neurons that emerge from the amount of photon-subtracted modes, proving that the proposed design satisfies the Universal Approximation Theorem within a single layer. To classically simulate the QONN training, the high-performance QuaNNTO library has been developed based on Wick-Isserlis expansion and Bogoliubov transformations, allowing multi-layer exact expectation values of non-Gaussian states without truncating the infinite-dimensional Hilbert space. Experiments on supervised learning and state-preparation tasks show balanced-resource efficiency with strong expressivity and generalization capabilities, illustrating the potential of the architecture for scalable photonic quantum machine learning and for quantum applications such as complex non-Gaussian gate synthesis.
Quantum computing has been pursued with various hardware platforms and an optical system is one of the most reasonable choices for large-scale computation. In the optical continuous-variable computation scheme, the incorporation of Gaussian gates and a highly nonclassical non-Gaussian state enables universal quantum computation. Although basic technologies for Gaussian gates and non-Gaussian state generation have long been developed, these building blocks have not yet been integrated in a scalable fashion. Here, we integrate them to develop a scalable and programmable optical quantum computing platform that can sequentially perform an essential Gaussian gate, the squeezing gate, on a non-Gaussian input state. The key enablers are a loop-based optical circuit with dynamical and programmable controllability and its time synchronization with the probabilistic non-Gaussian state generation. We verify the deterministic, programmable, and repeatable quantum gates on a typical non-Gaussian state by implementing up to three-step gates. The gates implemented are of such high quality that strong evidence of the nonclassicalities of the states, the negativities of the Wigner functions, are preserved even after multistep gates. This platform is compatible with other non-Gaussian states and can in principle realize large-scale universal quantum computing by incorporating other existing processing technologies.
Integrated photonic quantum chips have emerged as a promising platform for realizing fault-tolerant photonic quantum computers. Compared with bulk-optics systems, integrated photonics offer compact device footprints, enhanced programmability, and exceptional stability, attributes that are essential for executing large-scale and computationally demanding quantum tasks. In this review, we provide a comprehensive overview of recent advances in both discrete-variable and continuous-variable universal photonic quantum computation. We survey progress in fault-tolerant architectures and state-of-the-art integrated platforms and highlight the key challenges that must be addressed to scale photonic systems toward full fault tolerance. Finally, we outline future opportunities and research directions aimed at bridging the gap between current proof-of-principle demonstrations and fully scalable quantum microprocessors.
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.
In a world of various competing quantum computing architectures, hardware-agnostic, full-stack platforms are necessary to bring the full power of quantum computing hardware to domain experts via the cloud. QCI Connect and its Software Development Kit provide a reference architecture for a full-stack platform with a modular design and open-source interface definitions, built to facilitate a community-driven application ecosystem. Here, we present its overall design and features, central interfaces, and lessons learned, both for users of the platform and as a reference guide for future developments.