Waveguides potentially offer an effective medium for interconnecting quantum processors within a modular framework, facilitating the coherent quantum state transfer between the qubits across separate chips. In this work, we analyze a quantum communication scenario where two qubits are connected to a shared waveguide, whose resonance frequency may match or not match that of the qubits. Both configurations are simulated from the perspective of quantum electrodynamics (QED) to assess the system behavior and key factors that influence reliable interchip communication. The primary performance metrics analyzed are quantum state transfer fidelity and latency, considering the impact of key system parameters such as the qubit-waveguide detuning, coupling strength, waveguide decay rate, and qubit decay rate. We present the system design requirements that yield enhanced state transmission fidelity rates and lowered latency, and discuss the scalability of waveguide-mediated interconnects considering various configurations of the system.

This paper presents a single stack structure integrating a Single Photon Avalanche Diode (SPAD) and a memristor, for rapid quenching and recharge using the short term memory of a memristor in its threshold switching behaviour. The fabricated device is compatible with typical CMOS process. It uses a circuit similar to passive quenching, typically used in single photon imaging devices. However, by using a memristor, it experimentally achieves performance similar to active reset operation without using additional circuitry and hence, at same pixel size and fill factor.

Wireless communications in the terahertz band have been recently proposed as complement to conventional wired interconnects within computing packages. Such environments are typically highly reverberant, hence showing long channel impulse responses and severely limiting the achievable rates. Fortunately, this communications scenario is static and can be pre-characterized, which opens the door to techniques such as time reversal. Time reversal acts a spatial matched filter and has a spatiotemporal focusing effect, which allows not only to increase the achievable symbol rates, but also to create multiple spatial channels. In this paper, the multi-user capability of time reversal is explored in the context of wireless communications in the terahertz band within a computing package. Full-wave simulations are carried out to validate the approach, whereas modulation streams are simulated to evaluate the error rate as a function of the transmitted power, symbol rate, and number of simultaneous transmissions.

The grand challenge of scaling up quantum computers requires a full-stack architectural standpoint. In this position paper, we will present the vision of a new generation of scalable quantum computing architectures featuring distributed quantum cores (Qcores) interconnected via quantum-coherent qubit state transfer links and orchestrated via an integrated wireless interconnect.

In this paper, we propose a deep optimization prior approach with application to the estimation of material-related model parameters from terahertz (THz) data that is acquired using a Frequency Modulated Continuous Wave (FMCW) THz scanning system. A stable estimation of the THz model parameters for low SNR and shot noise configurations is essential to achieve acquisition times required for applications in, e.g., quality control. Conceptually, our deep optimization prior approach estimates the desired THz model parameters by optimizing for the weights of a neural network. While such a technique was shown to improve the reconstruction quality for convex objectives in the seminal work of Ulyanov et al., our paper demonstrates that deep priors also allow to find better local optima in the non-convex energy landscape of the nonlinear inverse problem arising from THz imaging. We verify this claim numerically on various THz parameter estimation problems for synthetic and real data under low SNR and shot noise conditions. While the low SNR scenario not even requires regularization, the impact of shot noise is significantly reduced by total variation (TV) regularization. We compare our approach with existing optimization techniques that require sophisticated physically motivated initialization, and with a 1D single-pixel reparametrization method.

This paper presents the research directions pursued by the WiPLASH European project, pioneering on-chip wireless communications as a disruptive enabler towards next-generation computing systems for artificial intelligence (AI). We illustrate the holistic approach driving our research efforts, which encompass expertises and abstraction levels ranging from physical design of embedded graphene antennas to system-level evaluation of wirelessly-communicating heterogeneous systems.

Graphene is enabling a plethora of applications in a wide range of fields due to its unique electrical, mechanical, and optical properties. In the realm of wireless communications, graphene shows great promise for the implementation of miniaturized and tunable antennas in the terahertz band. These unique advantages open the door to new reconfigurable antenna structures which, in turn, enable novel communication protocols at different levels of the stack. This paper explores both aspects by, first, presenting a terahertz Yagi-Uda-like antenna concept that achieves reconfiguration both in frequency and beam direction simultaneously. Then, a programmable antenna controller design is proposed to expose the reconfigurability to the PHY and MAC layers, and several examples of its applicability are given. The performance and cost of the proposed scheme is evaluated through full-wave simulations and comparative analysis, demonstrating reconfigurability at nanosecond granularity with overheads below 0.02 mm2and 0.2 mW.

Abstract 2D Confocal Raman Microscopy (CRM) data consist of high dimensional per‐pixel spectral data of 1000 bands and allows for complex spectral and spatial‐spectral analysis tasks, i.e., in material discrimination, material thickness, and spatial material distributions. Currently, simple integral methods are commonly applied as visual analysis solutions to CRM data which exhibit restricted discrimination power in various regards. In this paper we present a novel approach for the visual analysis of 2D multispectral CRM data using multi‐variate visualization techniques. Due to the large amount of data and the demand of an explorative approach without a‐priori restriction, our system allows for arbitrary interactive (de)selection of varaibles w/o limitation and an unrestricted online definition/construction of new, combined properties. Our approach integrates CRM specific quantitative measures and handles material‐related features for mixed materials in a quantitative manner. Technically, we realize the online definition/construction of new, combined properties as semi‐automatic, cascaded, 1D and 2D multidimensional transfer functions (MD‐TFs). By interactively incorporating new (raw or derived) properties, the dimensionality of the MD‐TF space grows during the exploration procedure and is virtually unlimited. The final visualization is achieved by an enhanced color mixing step which improves saturation and contrast.