We demonstrate quantum information processing of dense frequency-multiplexed high-dimensional time-bin entangled photons over 25 km of single-mode fiber. Our approach leverages off-the-shelf fiber components to enable high-bandwidth quantum networking for quantum cryptography and distributed quantum computing.

Integrated optics is primarily based on planar designs due to the availability of mature lithographic manufacturing and optical confinement constraints. These 2D designs with finite thickness in the third dimension are often referred to as 2.5D. Full 3D photonic architectures, with refractive-index variations along the three dimensions, hold the promise of higher integration density and novel, to the best of our knowledge, light control capabilities, but require advanced multi-layer stacking techniques with precise alignment and planarization. Nanoscale 3D printing techniques, such as multi-photon lithography, can address these challenges, and are gaining momentum thanks to their cost-effectiveness and rapid prototyping capabilities compared to silicon foundries. Despite this potential, the exploration of freeform polymer optics at the nanoscale remains limited due to challenges associated with low-index materials and a lack of design tools. Here, we address these limitations by applying a multi-layered inverse design approach for polymer-based integrated optics. We systematically compare 3D with 2.5D designs (all simulations are conducted in 3D), for the task of demultiplexing two wavelengths with spectral spacing from 100 nm to 20 nm. Our numerical results show that fully 3D polymer designs consistently outperform their 2.5D counterparts, achieving higher efficiencies at equal footprint. These findings propel the advancement of a next generation of miniaturized 3D devices for polymer-based integrated optics.

The rapidly increasing demands for computational throughput, bandwidth, and memory capacity fueled by breakthroughs in machine learning pose substantial challenges for conventional electronic computing platforms. For digital scaling to keep pace with the accelerating growth of artificial intelligence (AI) models beyond the trajectory of Moores law, computational power has to double roughly every three months. Historically, advancing compute performance relied on spatial scaling to increase the transistor count on a given chip area and, more recently, the development of parallel and multi-core architectures. Exponential scaling on trajectories much steeper than what can be achieved by such conventional strategies, and in line with the demands of AI, can be achieved with computing platforms that process data using multiple, orthogonal dimensions available to photons. Here we elucidate pivotal developments in the realization of multidimensional computing platforms based on photonic systems. Moving to such architectures holds enormous promise for low-latency, high-bandwidth information processing at reduced energy consumption.

High-brightness-integrated quantum light sources represent a fundamental element for large-scale photonic quantum technologies. Integrated microring resonator (MRR) in highly nonlinear and ultralow-loss material platforms enable entangled photon-pair generation, but device performance is affected by fabrication tolerances and can thus not be judiciously controlled. Redundant designs based on parameter variation are impractical for chip-scale circuits, as the sheer number of parameters would be too complex. To address this, passive photonic circuits must be combined with active control elements to optimize photon-pair emission and to match emission characteristics among sources. Here, we present a comprehensive study of an actively tunable, interferometrically coupled integrated MRR implemented in silicon nitride (SiN). By manipulating two thermally controlled phase shifters, the ring-bus coupling and the resonator phase can be dynamically adjusted, enabling post-fabrication control of its quality factor (Q-factor) between 105 and 106 to optimize the photon emission bandwidth and the pair generation rate. We directly demonstrate for the first time that the optimal coupling and emission characteristics do not occur at critical coupling and vary across frequency modes due to dispersion effects, in good agreement with our theoretical model. These findings highlight the role of adaptive integrated photon sources for single-batch fabrication of reconfigurable devices with application-specific spectral properties.

Abstract As photonic systems grow more complex, it becomes increasingly difficult to capture their behaviour within the conventional four dimensions of space and time, particularly for systems operating at the nanometer scale, where strong confinement effects, near-field interactions, and subwavelength structuring introduce additional layers of complexity. The concept of 5D photonics reflects this shift by incorporating additional physical, material, computational, adaptive, and quantum degrees of freedom as active components in design, control, and function. Rather than defining a single extra coordinate, higher-dimensional photonics is about operating photonic systems within expanded, dynamically accessible state spaces where multiple dimensions can interact and evolve together. This roadmap brings together perspectives ranging from modeling and design concepts to experimental platforms, materials, components, and system-level implementations. It covers a wide spectrum of synthetic and structured dimensions, nonlinear and strong-field regimes, adaptive and reconfigurable architectures, cyber-physical and engineering approaches, as well as inherently high-dimensional quantum and excitonic systems. Across all these areas, higher-dimensional thinking emerges not as an abstract construct but as a practical tool for enabling new functionalities, overcoming conventional design limitations, and bridging physical systems with digital and AI-driven layers. By framing these diverse developments within a shared higher-dimensional perspective, the roadmap aims to provide orientation in a rapidly expanding field, reveal conceptual connections between traditionally separate areas of photonics, and highlight common challenges and opportunities. In doing so, it positions higher-dimensional photonics as a central paradigm for developing future photonic technologies that are increasingly adaptive, intelligent, and integrated across physical and virtual domains.

An optimized quantum network design is demonstrated by realizing a state-multiplexing quantum light source via a dual-excitation configuration technique. This approach optimizes the usage of the finite wavelength spectrum, facilitating the efficient expansion of entanglement-based fully-connected quantum networks across multiple users.

Large-scale quantum networks require dynamic and resource-efficient solutions to reduce system complexity with maintained security and performance to support growing number of users over large distances. Current encoding schemes including time-bin, polarization, and orbital angular momentum, suffer from the lack of reconfigurability and thus scalability issues. Here, we demonstrate the first-time implementation of frequency-bin-encoded entanglement-based quantum key distribution and a reconfigurable distribution of entanglement using frequency-bin encoding. Specifically, we demonstrate a novel scalable frequency-bin basis analyzer module that allows for a passive random basis selection as a crucial step in quantum protocols, and importantly equips each user with a single detector rather than four detectors. This minimizes massively the resource overhead, reduces the dark count contribution, vulnerability to detector side-channel attacks, and the detector imbalance, hence providing an enhanced security. Our approach offers an adaptive frequency-multiplexing capability to increase the number of channels without hardware overhead, enabling increased secret key rate and reconfigurable multi-user operations. In perspective, our approach enables dynamic resource-minimized quantum key distribution among multiple users across diverse network topologies, and facilitates scalability to large-scale quantum networks.

The next generation of telecommunication networks will rely on the transmission of complex quantum states to enable secure and transformative information processing, utilizing entanglement and superposition. Cluster states - multipartite entangled states that retain entanglement under local measurements - are a vital resource for quantum networking applications such as blind photonic quantum computing, quantum state teleportation and all-photonic quantum repeaters. However, the transmission of cluster states over optical fiber has remained elusive with previous approaches. Here, we demonstrate the first transmission of a four-qubit cluster state over 25 km of single-mode fiber by using a two-photon multi-level time-bin encoding. We directly generate the state by exploiting coherent control of a parametric generation process, rendering a resource-intensive controlled-phase gate obsolete. To enable efficient and reconfigurable projective measurements on the multi-level time-bin encoded state, we introduce chirped-pulse modulation and implement the first time-bin beam splitter, allowing us to certify genuine multipartite entanglement and to demonstrate one-way computing operations. Our approach enables the transmission of complex quantum states over long-distance fibers, permitting the implementation of multipartite protocols and laying the foundation for large-scale quantum resource networks.

Controlling nonlinear pulse propagation in optical fibers is paramount for applications spanning spectroscopy and optical communication networks. However, the inherent complexity of laser pulse evolution in matter, shaped by the interplay of nonlinearity and dispersion, poses significant challenges in experimental situations. Modulation instability, a fundamental process in nonlinear fiber optics, illustrates such experimental issues due to its noise-driven nature, leading to unpredictable dynamics and thus requiring advanced control strategies. Here, we investigate noise-driven modulation instability during nonlinear fiber propagation, underlining the potential of coherent optical seeding and machine learning to jointly control incoherent spectral broadening dynamics. By introducing weak coherent seeds into an initial laser pulse, we demonstrate the ability to tailor noise-driven MI properties through fine adjustments of the seed parameters driven by evolutionary algorithms. In particular, real-time spectral characterization is achieved via time-stretch dispersive Fourier transform, enabling optimized control of spectral intensity correlations. Our experimental results highlight the effectiveness of combining coherent optical seeding with optimization techniques such as genetic algorithms, to tailor incoherent spectral fluctuations arising from the competition between coherent and incoherent nonlinear frequency conversion processes. Specifically, we show that the proposed approach can be leveraged on-demand, to shape specific correlation features in the output spectrum. The implications of our research extend beyond the sheer process of modulation instability, offering promising applications in advanced optical information processing. By demonstrating simple yet robust and flexible management strategies, this work paves the way for next-generation nonlinear photonic technologies, exploiting incoherent processes in practical optical fiber architectures.

A recent research reports on chip-fiber-chip quantum teleportation of time-bin-encoded qubits over a 12.3 km optical fiber link within a star-topology quantum network, composed of an on-chip accommodated user node, relay node and a central node. An active feedback optimization scheme is embedded to ensure highly stable Bell state measurements.

Machine learning is bringing revolutionary approaches into many fields of physics. Among those, photonics enables fast and scalable information processing. Photonics platforms further possess rich nonlinear dynamics that drive fundamental interest but also prove powerful for applications in computation, imaging, frequency conversion, source development and advanced signal processing. However, incoherent processes of nonlinear optics are hardly exploited in practice as the control of noise-driven dynamics remains challenging. Here, we exploit deep learning strategies and demonstrate that coherent optical seeding can effectively shape incoherent spectral broadening. We focus on the intricate interplay between weak coherent pulses and broadband noise, competing during nonlinear fibre propagation within an amplification process known as modulation instability. We demonstrate artificial neural networks' capability to efficiently predict these complex incoherent dynamics, both numerically and experimentally. Our results show that input seed properties can be inferred from the incoherent output signal. Furthermore, our approach enables reliable prediction of output spectral fluctuations, paving the way to tailoring complex photonic signals with specific correlation features.

The rapidly increasing demands on computational throughput, bandwidth and memory capacity fuelled by breakthroughs in machine learning pose substantial challenges for conventional electronic computing platforms. Historically, advancing compute performance relied on miniaturization to increase the transistor count on a given chip area and, more recently, on the development of parallel and multicore architectures. Computing platforms that process data using multiple, orthogonal dimensions can achieve exponential scaling on trajectories much steeper than what is possible with conventional strategies. One promising analog platform is photonics, which makes use of the physics of light, such as sensitivity to material properties and ability to encode information across multiple degrees of freedom. With recent breakthroughs in integrated photonic hardware and control, large-scale photonic systems have become a practical and timely solution for data-intensive, real-time computational tasks. Here, we explain developments in the realization of multidimensional computing platforms based on photonic systems. Moving to such architectures holds promise for low-latency, high-bandwidth information processing at reduced energy consumption. Multidimensional photonic computing is a framework that combines classical and quantum approaches, leveraging the properties of light. This Perspective explores its potential to enable scalable, neuromorphic photonic quantum systems suited to data-intensive and complex computational tasks.

We demonstrate the first-time frequency-bin-encoded entanglement-based quantum key distribution (EBQKD) between two users, and a flexible entanglement distribution over long optical fiber links. Dynamic and resource-efficient solutions are crucial to realizing large-scale photonic quantum networks capable of accommodating a growing number of users over large distances and at the same time facilitating a reliable performance at a maintained security level. Here, we adopt the frequency-bin encoding approach for defining three frequency channels, namely, CH1, CH2, and CH3, from highly correlated signal and idler photon pairs from pulsed-excited spontaneous parametric down-conversion (SPDC) process. The adaptive frequency de/multiplexing capability of our approach facilitates dynamic allocation of multiple frequency channels to additional users without increasing the hardware overhead. We developed a frequency-bin-basis analyzer module (see Figure. 1 a & b) composed of a programmable filter (PF), a frequency mixer (FM) based on electro-optic phase modulation (EOPM), a fiber Bragg grating (FBG) for the frequency-to-time mapping (FTM), and a superconducting nanowire single photon detector (SNSPD) of high timing resolution. Through a fine-tuned frequency mixing, passive random projection measurements are realized in two mutually unbiased basis (MUB; here $\mathrm{Z}=\{\vert 0\rangle, \vert 1\rangle\}$ and $\mathrm{X}=\{\vert +\rangle, \vert -\rangle\})$. The FTM technique applied on the frequency-mixed spectrum of each frequency channel allows for a time-resolved access to the projection measurement results in four basis states using only a single SNSPD. Remarkably, we showcase the capability of our approach for time-resolved detection of the four projection states corresponding to CH1, CH2, and CH3 using a single SNSPD per user. The performance metrics of the BBM92 protocol are shown in Figure. 1 c-e under different optical attenuations, at maximum 15.6 dB (equivalent to a 73 km fiber link). The qubit error ratio (QBER) was obtained for all channels well below the 11% upper threshold [1]. The maximum asymptotic secret key length $l^{A}$ of 310-bits, 283-bits, and 149-bits were obtained for channels CH1, CH2, and CH3, respectively. From the finite key analysis, a positive non-zero secret key rate was obtained for less than 15.6 dB attenuation. The capability of the system was also assessed in preserving high quality frequency-insenstive entanglement for distribution across remote fiber links (see Figure.1 f-h). Overall, the presented approach enables passive random projection measurements in two MUB by each user and benefits from frequency-multiplexing capability for adaptive multi-user operations. A single SNSPD per user renders this approach resource-efficient compared to conventional implementation schemes requiring 4×N detectors corresponding to N channels [2]. A single SNSPD means a reduced vulnerability to detector side channel attacks, a minimized dark count contribution, and a diminished imbalance arising from detection probability mismatch. Our approach thus enables a resource-efficient and a scalable implementation of EBQKD required for the future large-scale quantum networks.

Optical artificial neural networks (OANNs) leverage the advantages of photonic technologies including high processing speeds, low energy consumption, and mass production to establish a competitive and scalable platform for machine learning applications. While recent advancements have focused on harnessing spatial or temporal modes of light, the frequency domain attracts a lot of attention, with current implementations including spectral multiplexing, neural networks in nonlinear optical systems and extreme learning machines. Here, we present an experimental realization of a programmable photonic frequency circuit, realized with fiber-optical components, and implement the in-situ training with optical weight control of an OANN operating in the frequency domain. Input data is encoded into phases of frequency comb modes, and programmable phase and amplitude manipulations of the spectral modes enable in-situ training of the OANN, without employing a digital model of the device. The trained OANN achieves multiclass classification accuracies exceeding 90 %, comparable to conventional machine learning approaches. This proof-of-concept demonstrates the feasibility of a multilayer OANN in the frequency domain and can be extended to a scalable, integrated photonic platform with ultrafast weights updates, with potential applications to single-shot classification in spectroscopy.

Optical beam splitters are essential for classical and quantum photonic on-chip systems. In integrated optical technology, a beam splitter can be implemented as a beam coupler with two input and two output ports. The output phases are constrained by the conservation of energy. In lossless beam splitters, the phase shift between the output fields is π and zero for excitation from the first and second input ports, respectively. Therefore, for excitation from both inputs, the phase between the output fields, defined as beam splitter phase (BSP), is π. The BSP leads to several phenomena, such as the quantum interference between two photons, known as the Hong–Ou–Mandel effect. By introducing losses, BSP values different than π become theoretically possible, but the design of 2 × 2 beam couplers with an arbitrary phase is elusive in integrated optics. Inspired by the growing interest on fundamental limits in electromagnetics and inverse design, here we explore the theoretical limits of symmetrical integrated beam splitters with an arbitrary BSP via adjoint-based topology optimization. Optimized 2D designs accounting for fabrication constraints are obtained for several combinations of loss and phase within the theoretical design space. Interestingly, the algorithm does not converge for objectives outside of the theoretical limits. Designs of beam splitters with arbitrary phase may find use in integrated optics for quantum information processing.