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The interaction mechanism between a single microscopic object like a cell, a particle, a molecule, or an atom and its interacting electromagnetic field is fundamental in single-object manipulation such as optical trap and magnetic trap. Function-on-demand, single-object manipulation relies on a high degree of freedom control of electromagnetic field at localized scales, which remains challenging. Here we propose a manipulation concept: programmable single-object manipulation, based on programming the electromagnetic field in a multi-bit electrode system. This concept is materialized on a Programmable Electric Tweezer (PET) with four individually addressed electrodes, marking a transition from function-fixed single-object manipulation to function-programmable single-object manipulation. By programming the localized electric field, our PET can provide various manipulation functions for achieving precise trapping, movement and rotation of multiscale single microscopic objects, including single proteins, nucleic acids, microparticles and bacteria. Implementing these functions, we are able not only to manipulate the object of interest on demand but also quantitatively measure the charge

Programmability is a unifying paradigm for enacting families of quantum transformations via fixed processors and program states, with a fundamental role and broad impact in quantum computation and control. While there has been a shift from viewing open systems solely as a source of error to treating them as a computational resource, their programmability remains largely unexplored. In this work, we develop a framework that characterizes and quantifies the programmability of Lindbladian semigroups by combining physically implementable retrieval maps with time varying program states. Within this framework, we identify quantum programmable classes enabled by symmetry and stochastic structure, including covariant semigroups and fully dissipative Pauli Lindbladians with finite program dimension. We further provide a necessary condition for physical programmability that rules out coherent generators and typical dissipators generating amplitude damping. For such nonphysically programmable cases, we construct explicit protocols with finite resources. Finally, we introduce an operational programming cost, defined via the number of samples required to program the Lindbladian, and establish i

Nonlinear photonics uses coherent interactions between optical waves to engineer functionality that is not possible with purely linear optics. Traditionally, the function of a nonlinear-optical device is determined during design and fixed during fabrication. In this paper, we present a photonic device with highly programmable nonlinear functionality: an optical slab waveguide with an arbitrarily reconfigurable two-dimensional distribution of $χ^{(2)}$ nonlinearity. The nonlinearity is realized using electric-field-induced $χ^{(2)}$ in a $χ^{(3)}$ material. The programmability is engineered by massively parallel control of the electric-field distribution within the device using a photoconductive layer and optical programming with a spatial light pattern. To showcase the versatility of our device, we demonstrated spectral, spatial, and spatio-spectral engineering of second-harmonic generation by tailoring arbitrary quasi-phase-matching (QPM) grating structures in two dimensions. Second-harmonic light was generated with programmable spectra, enabled by real-time in situ inverse design of QPM gratings. Flexible spatial control was also achieved, including the generation of complex wave

Programmable biomolecule-mediated computing is a new computing paradigm as compared to contemporary electronic computing. It employs nucleic acids and analogous biomolecular structures as information-storing and -processing substrates to tackle computational problems. It is of great significance to investigate the various issues of programmable biomolecule-mediated processors that are capable of automatically processing, storing, and displaying information. This Perspective provides several conceptual designs of programmable biomolecule-mediated processors and provides some insights into potential future research directions for programmable biomolecule-mediated processors.

Parametrized quantum circuits are essential components of variational quantum algorithms. Until now, optical implementations of these circuits have relied solely on adjustable linear optical units. In this study, we demonstrate that using programmable nonlinearities, rather than linear optics, offers a more efficient method for constructing quantum optical circuits -- especially quantum neural networks. This approach significantly reduces the number of adjustable parameters and the circuit depth needed to achieve high-fidelity operation. Specifically, we explored a quantum optical neural network (QONN) architecture composed of meshes of two-mode interferometers programmable by adjustable Kerr-like nonlinearities. We assessed the capabilities of our quantum optical neural network architecture and compared its performance to previously studied architectures that use multimode linear optics units. Additionally, we suggest future research directions for improving programmable quantum optical circuits.

Similar to a classical processor, which is an algorithm for reading a program and executing its instructions on input data, a universal programmable quantum processor is a fixed quantum channel that reads a quantum program $\lvertψ_{U}\rangle$ that causes the processor to approximately apply an arbitrary unitary $U$ to a quantum data register. The present work focuses on a class of simple programmable quantum processors for implementing reflection operators, i.e. $U = e^{i π\lvertψ\rangle\langleψ\rvert}$ for an arbitrary pure state $\lvertψ\rangle$ of finite dimension $d$. Unlike quantum programs that assume query access to $U$, our program takes the form of independent copies of the state to be reflected about $\lvertψ_U\rangle = \lvertψ\rangle^{\otimes n}$. We then identify the worst-case optimal algorithm among all processors of the form $\text{tr}_{\text{Program}}[V (\lvertφ\rangle\langleφ\rvert \otimes (\lvertψ\rangle\langleψ\rvert)^{\otimes n}) V^\dagger]$ where the algorithm $V$ is a unitary linear combination of permutations. By generalizing these algorithms to processors for arbitrary-angle rotations, $e^{i α\lvertψ\rangle\langleψ\rvert}$ for $α\in \mathbb R$, we give a co

Programmable packet scheduling allows the deployment of scheduling algorithms into existing switches without need for hardware redesign. Scheduling algorithms are programmed by tagging packets with ranks, indicating their desired priority. Programmable schedulers then execute these algorithms by serving packets in the order described in their ranks. The ideal programmable scheduler is a Push-In First-Out (PIFO) queue, which achieves perfect packet sorting by pushing packets into arbitrary positions in the queue, while only draining packets from the head. Unfortunately, implementing PIFO queues in hardware is challenging due to the need to arbitrarily sort packets at line rate based on their ranks. In the last years, various techniques have been proposed, approximating PIFO behaviors using the available resources of existing data planes. While promising, approaches to date only approximate one of the characteristic behaviors of PIFO queues (i.e., its scheduling behavior, or its admission control). We propose PACKS, the first programmable scheduler that fully approximates PIFO queues on all their behaviors. PACKS does so by smartly using a set of strict-priority queues. It uses packe

Programmable photonic integrated circuits (PICs) are emerging as powerful tools for the precise manipulation of light, with applications in quantum information processing, optical range finding, and artificial intelligence. The leading architecture for programmable PICs is the mesh of Mach-Zehnder interferometers (MZIs) embedded with reconfigurable optical phase shifters. Low-power implementations of these PICs involve micromechanical structures driven capacitively or piezoelectrically but are limited in modulation bandwidth by mechanical resonances and high operating voltages. However, circuits designed to operate exclusively at these mechanical resonances would reduce the necessary driving voltage from resonantly enhanced modulation as well as maintaining high actuation speeds. Here we introduce a synchronous, micromechanically resonant design architecture for programmable PICs, which exploits micromechanical eigenmodes for modulation enhancement. This approach combines high-frequency mechanical resonances and optically broadband phase shifters to increase the modulation response on the order of the mechanical quality factor $Q_m$, thereby reducing the PIC's power consumption, vo

Cyclic motions are fundamental patterns in robotic applications including industrial manipulation and legged robot locomotion. This paper proposes an approach for the online modulation of cyclic motions in robotic applications. For this purpose, we present an integrated programmable Central Pattern Generator (CPG) for the online generation of the reference joint trajectory of a robotic system out of a library of desired periodic motions. The reference trajectory is then followed by the lower-level controller of the robot. The proposed CPG generates a smooth reference joint trajectory convergence to the desired one while preserving the position and velocity joint limits of the robot. The integrated programmable CPG consists of one novel bounded output programmable oscillator. We design the programmable oscillator for encoding the desired multidimensional periodic trajectory as a stable limit cycle. We also use the state transformation method to ensure that the oscillator's output and its first-time derivative preserve the joint position and velocity limits of the robot. With the help of Lyapunov-based arguments, We prove that the proposed CPG provides the global stability and conver

Programmable logic arrays (PLAs) are traditional digital electronic devices. A PLA is a simple programmable logic device (SPLD) used to implement combinational logic circuits. A PLA has a set of programmable AND gates, which link to a set of programmable OR gates to produce an output. The AND-OR layout of a PLA allows for implementing logic functions that are in a sum-of-products form. PLAs are available in the market in different types. PLAs could be stand alone chips, or parts of bigger processing systems. Stand alone PLAs are available as mask programmable (MPLAs) and field programmable (FPLAs) devices. The attractions of PLAs that brought them to mainstream engineers include their simplicity, relatively small circuit area, predictable propagation delay, and ease of development. The powerful-but-simple property brought PLAs to rapid prototyping, synthesis, design optimization techniques, embedded systems, traditional computer systems, hybrid high-performance computing systems, etc. Indeed, there has been renewable interests in working with the simple AND-to-OR PLAs.

Programmable photonic integrated circuits (PICs) have recently gained significant interest due to their potential in creating next-generation technologies ranging from artificial neural networks and microwave photonics to quantum information processing. The fundamental building block of such programmable PICs is a tunable 2 x 2 switch, traditionally controlled by the thermo-optic or free-carrier dispersion. Yet, these implementations are power-hungry, volatile, and have a large footprint (typically > 100 um). Therefore, a truly 'set-and-forget' type 2 x 2 switch with zero static power consumption is highly desirable for large-scale PICs. Here, we report a broadband nonvolatile electrically programmable 2 x 2 silicon photonic switch based on the phase-change material Ge2Sb2Te5. The directional coupler switch exhibits a compact coupling length (64 um), small insertion loss (<2 dB), and minimal crosstalk (<-8 dB) across the entire telecommunication C-band while maintaining a record-high endurance of over 2,800 switching cycles. This demonstrated switch constitutes a critical component for realizing future generic programmable silicon photonic systems.

Programmable switches have emerged as powerful and flexible alternatives to fixed-function forwarding devices. But because of the unique hardware constraints of network switches, the design and implementation of compilers targeting these devices is tedious and error prone. Despite the important role that compilers play in software development, there is a dearth of tools for testing compilers for programmable network devices. We present Druzhba, a programmable switch simulator used for testing compilers targeting programmable packet-processing substrates. We show that we can model the low-level behavior of a switch's programmable hardware. We further show how our machine model can be used by compiler developers to target Druzhba as a compiler backend. Generated machine code programs are fed into Druzhba and tested using a fuzzing-based approach that allows compiler developers to test the correctness of their compilers. Using a program-synthesis-based compiler as a case study, we demonstrate how Druzhba has been successful in testing compiler-generated machine code for our simulated switch pipeline instruction set.

Programmable network switches promise flexibility and high throughput, enabling applications such as load balancing and traffic engineering. Network measurement is a fundamental building block for such applications, including tasks such as the identification of heavy hitters (largest flows) or the detection of traffic changes. However, high-throughput packet processing architectures place certain limitations on the programming model, such as restricted branching, limited capability for memory access, and a limited number of processing stages. These limitations restrict the types of measurement algorithms that can run on programmable switches. In this paper, we focus on the RMT programmable high-throughput switch architecture, and carefully examine its constraints on designing measurement algorithms. We demonstrate our findings while solving the heavy hitter problem. We introduce PRECISION, an algorithm that uses \emph{Probabilistic Recirculation} to find top flows on a programmable switch. By recirculating a small fraction of packets, PRECISION simplifies the access to stateful memory to conform with RMT limitations and achieves higher accuracy than previous heavy hitter detection

We demonstrate experimentally that reflectionless scattering modes (RSMs), a generalized version of coherent perfect absorption, can be functionalized to perform reflectionless programmable signal routing. We achieve versatile programmability both in terms of operating frequencies and routing functionality with negligible reflection upon in-coupling, which avoids unwanted signal-power echoes in radio-frequency or photonic networks. We report in-situ observations of routing functionalities like wavelength demultiplexing, including cases where multi-channel excitation requires adapted coherent input wavefronts. All experiments are performed in the microwave domain based on the same irregularly shaped cavity with strong modal overlap that is massively parametrized by a 304-element programmable metasurface. RSMs in our highly overdamped multi-resonance transport problem are fundamentally intriguing because the simple critical-coupling picture for reflectionless excitation of isolated resonances fails spectacularly. We show in simulation that the distribution of damping rates of scattering singularities broadens under strong absorption so that weakly damped zeros can be tuned toward fun

SDN divides the networking landscape into 2 parts: control and data plane. SDN expanded it's foot mark starting with OpenFlow based highly flexible control plane and rigid data plane. Innovation and improvement in hardware design and development is bringing various new architectures for data plane. Data plane is becoming more programmable then ever before. A common abstract model of data plane is required to develop complex application over these heterogeneous data plane devices. It can also provide insight about performance optimization and bench-marking of programmable data plane devices. Moreover, to understand and utilize data plane's programmability, a detailed structural analysis and an identifiable matrix to compare different devices are required. In this work, an improved and structured abstract model of the programmable data plane devices is presented and features of its components are discussed in detail. Several commercially available programmable data plane devices are also compared based on those features.

The transition of fifth generation (5G) cellular systems to softwarized, programmable, and intelligent networks depends on successfully enabling public and private 5G deployments that are (i) fully software-driven and (ii) with a performance at par with that of traditional monolithic systems. This requires hardware acceleration to scale the Physical (PHY) layer performance, end-to-end integration and testing, and careful planning of the Radio Frequency (RF) environment. In this paper, we describe how the X5G testbed at Northeastern University has addressed these challenges through the first 8-node network deployment of the NVIDIA Aerial RAN CoLab (ARC), with the Aerial Software Development Kit (SDK) for the PHY layer, accelerated on Graphics Processing Unit (GPU), and through its integration with higher layers from the OpenAirInterface (OAI) open-source project through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). We discuss software integration, the network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video st

We present an intelligent programmable computational meta-imager that tailors its sequence of coherent scene illuminations not only to a specific information-extraction task (e.g., object recognition) but also adapts to different types and levels of noise. We systematically study how the learned illumination patterns depend on the noise, and we discover that trends in intensity and overlap of the learned illumination patterns can be understood intuitively. We conduct our analysis based on an analytical coupled-dipole forward model of a microwave dynamic metasurface antenna (DMA); we formulate a differentiable end-to-end information-flow pipeline comprising the programmable physical measurement process including noise as well as the subsequent digital processing layers. This pipeline allows us to jointly inverse-design the programmable physical weights (DMA configurations that determine the coherent scene illuminations) and the trainable digital weights. Our noise-adaptive intelligent meta-imager outperforms the conventional use of pseudo-random illumination patterns most clearly under conditions that make the extraction of sufficient task-relevant information challenging: latency c

We discuss the problem of designing unambiguous programmable discriminators for any n unknown quantum states in an m-dimensional Hilbert space. The discriminator is a fixed measurement that has two kinds of input registers: the program registers and the data register. The quantum state in the data register is what users want to identify, which is confirmed to be among the n states in program registers. The task of the discriminator is to tell the users which state stored in the program registers is equivalent to that in the data register. First, we give a necessary and sufficient condition for judging an unambiguous programmable discriminator. Then, if $m=n$, we present an optimal unambiguous programmable discriminator for them, in the sense of maximizing the worst-case probability of success. Finally, we propose a universal unambiguous programmable discriminator for arbitrary n quantum states.