Deterministic Operation Research Articles

Hardware-in-the-Loop testing for low-inertia grids

Abstract Extensive testing of various control algorithms using comprehensive testing frameworks enables us to assess their effectiveness in managing system dynamics and maintaining grid stability. In order to validate the algorithms in the real world, testing of the developed applications in an empirical laboratory environment is essential and the testing parameters in the laboratory have to closely resemble real-world conditions. The fundamental aspect of such testing is to monitor and analyze the dynamics and interaction between the various components in synchronous mode, thereby assessing the system’s ability to operate the grid without violations. The experimental test setup and script-based testing explained in this paper helps to develop innovative control algorithms, with a key focus on medium voltage and low voltage grids with low rotating masses. The test platform makes extended use of a Typhoon Real-Time Simulator (RTS). Firstly, it is used for the simulation of the distributed energy resources and the overlying distribution grid as well as the implementation of control algorithms. Secondly, the RTS acts as a control and SCADA system for the whole testing platform. While the physical components provide measurements in regular intervals, the control algorithms and the energy management system are implemented directly in the Typhoon platform. The data acquisition is performed either via programmable logic controllers for the nodal measurements or through faster measurements such as current transformers. This ensures that the developed algorithms are empirically tested and optimized in close to real-world conditions. The control algorithms provide the set points for the individual components, and the results show the advantages of script-based testing in HIL testing to ensure deterministic and reliable operation of innovative grid components.

Design of a release-free piezo-optomechanical quantum transducer

Quantum transduction between microwave and optical photons offers the potential to merge the long-range connectivity of optical photons with the deterministic quantum operations of superconducting microwave qubits. A promising approach to achieving this uses an intermediary mechanical mode along with piezo-optomechanical interactions. Traditionally, these transducers are suspended to confine mechanical fields, but this complicates manufacturing and comes with the major challenge of poor thermal anchoring and a trade-off between noise and efficiency. To overcome these issues, we introduce the—to our knowledge—first design of a release-free electro-optomechanical quantum transducer. Our release-free, i.e., non-suspended, design leverages a silicon-on-sapphire platform. It combines release-free lithium niobate electromechanical crystals with silicon optomechanical crystals on a sapphire substrate, optimizing thermal anchoring and microwave and mechanical coherence. Despite departing from the traditional suspended transducer paradigm, our release-free design achieves coupling rates sufficient for quantum-level interactions between microwave photons, phonons, and optical photons. Unconventionally, it utilizes high-wavevector mechanical modes tightly confined to the chip surface. Beyond quantum science and engineering, this platform and its design principles could also propel low-power acousto-optic systems in integrated photonics.

Creating and Deleting a Single Dipolar Skyrmion by Surface Spin Twists.

We report deterministic operations on single dipolar skyrmions confined in nanostructured cuboids by using in-plane currents. We achieve highly reversible writing and deleting of skyrmions in a simple cuboid without any artificial defects or pinning sites. The current-induced creation of skyrmions is well-understood through the spin-transfer torque acting on surface spin twists of the spontaneous 3D ferromagnetic state, caused by the magnetic dipole-dipole interaction of the uniaxial Fe3Sn2 magnet with a low-quality factor. Current-induced deletions of skyrmions result from the combined effects of magnetic hysteresis and Joule thermal heating. Our results are replicated consistently through 3D micromagnetic simulations. Our approach offers a viable method for achieving reliable single-bit operations in skyrmionic devices for applications such as random-access memories.

Crossover Operator Inspired by the Selection Operator for an Evolutionary Task Sequencing Algorithm

This paper proposes a novel crossover operator for evolutionary algorithms in task sequencing and verifies its efficacy. Unlike the conventional blind and entirely stochastic selection of sequence fragments exchanged with the second individual, the proposed operator employs a method where the probability of fragment selection is influenced by the total cost of internal connections within the exchanged fragments. At the same time, the new operator retains its stochastic nature and is not a deterministic operator, which reduces the risk of the evolutionary algorithm getting stuck in a local minimum. The idea of the proposed crossover operator was based on the main mechanism of the evolutionary algorithm that determines the success of this type of algorithm selection. To assess its effectiveness, the new operator was compared against previously employed crossover operators using a traveling salesman problem (TSP) instance in a multidimensional space in order to map the problem of symmetric sequencing tasks described with multiparameters (e.g., a symmetric variant of production tasks sequencing).

Fully Resistive In‐Memory Cryptographic Engine with Dual‐Polarity Memristive Crossbar Array

AbstractAs data volume and complexity increase, conventional software‐based encryption methods face significant challenges, including vulnerability to attacks and high computational demands. This study proposes a hardware‐based cryptographic engine using a dual‐polarity memristive crossbar array with Ta/HfO2/RuO2 memristors, utilizing stochastic and deterministic operations for enhanced security and efficiency. The system integrates random key generation and XOR‐based logic operations within the memristive array, enabling robust encryption and decryption of binary data with a fully resistive data representation. Experimental validation of alphabet image data encryption and the array scale simulation of individual fingerprint authentication are conducted to validate the robustness of the proposed hardware and cryptographic method. The results demonstrate the potential of the proposed hardware for homomorphic encryption, enabling secure data processing without decryption, which can pave the way for memristive hardware to evolve into resistance‐based digital computing hardware.

How Single-Photon Switching Is Quenched with Multiple Λ-Level Atoms.

Single-photon nonlinearity, namely, the change in the response of the system as the result of the interaction with a single photon, is generally considered an inherent property of a single quantum emitter. Although the dependence on the number of emitters is well understood for the case of two-level systems, deterministic operations such as single-photon switching or photon-atom gates inherently require more complex level structures. Here, we theoretically consider single-photon switching in ensembles of emitters with a Λ-level scheme and show that the switching efficiency vanishes with the number of emitters. Interestingly, the mechanism behind this behavior is the quantum Zeno effect, manifested in a slowdown of the photon-controlled dynamics of the atomic ground states.

Overcoming the coherence time barrier in quantum machine learning on temporal data

The practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset operations to reduce circuit executions, while still maintaining an appropriate length persistent temporal memory in the quantum system, confirmed through the proposed Volterra Series analysis. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling in monitored circuits and sampling noise, problems that persist even in hypothetical fault-tolerant quantum computers that have yet to be realized. To validate our approach, we consider the channel equalization task to recover test signal symbols that are subject to a distorting channel. Through simulations and experiments on a 7-qubit quantum processor we demonstrate that NISQRC can recover arbitrarily long test signals, not limited by coherence time.

Dual-mode neuron design with deterministic and non-deterministic operations using adiabatic superconductor devices

Abstract In this research, we unveil an innovative strategy in neuromorphic computing by developing a neuron model tailored for the energy-efficient Adiabatic Quantum-Flux-Parametron (AQFP) logic. This model is particularly aimed at enhancing neural network accelerators. Our design of the AQFP-based neuron operates effectively in both deterministic and non-deterministic modes. In deterministic mode, the design relies on superconducting inductive coupling to activate neurons by comparing the sum of AQFP signal currents against a tunable threshold. For non-deterministic operation, we demonstrate how altering specific circuit parameters can correlate these aggregated currents with the non-deterministic operational range of an AQFP current comparator. We verified its versatility and functionality by fabricating varied circuits and conducting extensive tests, confirming its practical application potential. Our work not only showcases the practical implementation of AQFP in neuromorphic computing but also sets a foundation for future advancements in energy-efficient AI hardware.

A Flexible Envelope Method for the Operation Domain of Distribution Networks Based on “Degree of Squareness” Adjustable Superellipsoid

The operation envelope of distribution networks can obtain the independent p-q controllable range of each active node, providing an effective means to address the issues of different ownership and control objectives between distribution networks and distributed energy resources (DERs). Existing research mainly focuses on deterministic operation envelopes, neglecting the operational status of the system. To ensure the maximization of the envelope operation domain and the feasibility of decomposition, this paper proposes a modified hyperellipsoidal dynamic operation envelopes (MHDOEs) method for distribution networks based on adjustable “Degree of Squareness” hyperellipsoids. Firstly, an improved convex inner approximation method is applied to the non-convex and nonlinear model of traditional distribution networks to obtain a convex solution space strictly contained within the original feasible region of the system, ensuring the feasibility of flexible operation domain decomposition. Secondly, the embedding of the adjustable “Degree of Squareness” maximum hyperellipsoid is used to obtain the total p-q operation domain of the distribution network, facilitating the overall planning of the distribution network. Furthermore, the calculation of the maximum inscribed hyperrectangle of the hyperellipsoid is performed to achieve p-q decoupled operation among the active nodes of the distribution network. Subsequently, a correction coefficient is introduced to penalize “unknown states” during the operation domain calculation process, effectively enhancing the adaptability of the proposed method to complex stochastic scenarios. Finally, Monte Carlo methods are employed to construct various stochastic scenarios for the IEEE 33-node and IEEE 69-node systems, verifying the accuracy and decomposition feasibility of the obtained p-q operation domains.

A Method of Integrating Air Conditioning Usage Models to Building Simulations for Predicting Residential Cooling Energy Consumption

Great deviations in building energy consumption simulation are attributed to the simplified settings of occupants’ air conditioning (AC) usage schedules. This study was designed to develop a method to quantify the uncertainty and randomness of AC usage behavior and incorporate the model into simulations, in order to improve the prediction performance of AC energy consumption. Based on long-term onsite monitoring of household thermal environments and AC usage patterns, two stochastic models were built using unsupervised cluster and statistical methods. Based on the Monte Carlo method, the AC operation schedule was generated through AC opening duration, setpoints, and other relevant parameters, and was further incorporated into EnergyPlus. The results show that the ideally deterministic AC operation settings from the standard significantly overestimate the cooling energy consumption, where the value based on the fixed mode was 6.35 times higher. The distribution of daily AC energy consumption based on the stochastic modeling was highly consistent with the actual situation, thanks to the accurate prediction of the randomness and dynamics of residents’ AC usage patterns. The total cooling energy consumption based on two stochastic models was found to be much closer to the actual values. The work proposes a method of embedding stochastic AC usage models to EnergyPlus 22.1 benefits for an improvement in building energy consumption simulation and the energy efficiency evaluation regarding occupant behavior in the future.

Distributed Cooperative Optimal Operation of Multiple Virtual Power Plants Based on Multi-Stage Robust Optimization

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

Deterministic quasi-continuous tuning of phase-change material integrated on a high-volume 300-mm silicon photonics platform

Programmable photonic integrated circuits (PICs) consisting of reconfigurable on-chip optical components have been creating new paradigms in various applications, such as integrated spectroscopy, multi-purpose microwave photonics, and optical information processing. Among many reconfiguration mechanisms, non-volatile chalcogenide phase-change materials (PCMs) exhibit a promising approach to the future very-large-scale programmable PICs, thanks to their zero static power and large optical index modulation, leading to extremely low energy consumption and ultra-compact footprints. However, the scalability of the current PCM-based programmable PICs is still limited since they are not directly off-the-shelf in commercial photonic foundries now. Here, we demonstrate a scalable platform harnessing the mature and reliable 300 mm silicon photonic fab, assisted by an in-house wide-bandgap PCM (Sb2S3) integration process. We show various non-volatile programmable devices, including micro-ring resonators, Mach-Zehnder interferometers and asymmetric directional couplers, with low loss (~0.0044 dB/µm), large phase shift (~0.012 π/µm) and high endurance (>5000 switching events with little performance degradation). Moreover, we showcase this platform’s capability of handling relatively complex structures such as multiple PIN diode heaters in devices, each independently controlling an Sb2S3 segment. By reliably setting the Sb2S3 segments to fully amorphous or crystalline state, we achieved deterministic multilevel operation. An asymmetric directional coupler with two unequal-length Sb2S3 segments showed the capability of four-level switching, beyond cross-and-bar binary states. We further showed unbalanced Mach-Zehnder interferometers with equal-length and unequal-length Sb2S3 segments, exhibiting reversible switching and a maximum of 5 (N+1,N=4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N+1,N=4$$\\end{document}) and 8 (2N,N=3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${2}^{N},N=3$$\\end{document}) equally spaced operation levels, respectively. This work lays the foundation for future programmable very-large-scale PICs with deterministic programmability.

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers.

Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator ("one-to-one" problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones ("one-to-many" problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach.

Telecom‐Band Quantum Dots Compatible with Silicon Photonics for Photonic Quantum Applications

AbstractSilicon photonics is promising for quantum photonics applications owing to its large‐scale and high‐performance circuitry enabled by complementary‐metal‐oxide‐semiconductor fabrication processes. However, there is a lack of bright single‐photon sources (SPSs) capable of deterministic operation on Si platforms, which largely limits their applications. To this end, on‐Si integration of high‐performance solid‐state quantum emitters, such as semiconductor quantum dots (QDs), is greatly desired. In particular, it is preferable to integrate SPSs emitting at telecom wavelengths for fully leveraging the power of silicon photonics, including efficient chip‐to‐fiber coupling. In this review, recent progress and challenges in the integration of telecom QD SPSs onto silicon photonic platforms are discussed.

Strong interactions between solitons and background light in Brillouin-Kerr microcombs.

Dissipative Kerr-soliton combs are laser pulses regularly sustained by a localized solitary wave on top of a continuous-wave background inside a nonlinear resonator. Usually, the intrinsic interactions between the background light and solitons are weak and localized. Here, we demonstrate a strong interaction between the generated soliton comb and the background light in a Brillouin-Kerr microcomb system. This strong interaction enables the generation of a monostable single-soliton microcomb on a silicon chip. Also, new phenomena related to soliton physics including solitons hopping between different states as well as controlling the formations of the soliton states by the pump power, are observed owing to such strong interaction. Utilizing this monostable single-soliton microcomb, we achieve the 100% deterministic turnkey operation successfully without any feedback controls. Importantly, it allows to output turnkey ultra-low-noise microwave signals using a free-running pump.

Leveraging Probabilistic Switching in Superparamagnets for Temporal Information Encoding in Neuromorphic Systems

Brain-inspired computing -leveraging neuroscientific principles underpinning the unparalleled efficiency of the brain in solving cognitive tasks -is emerging to be a promising pathway to solve several algorithmic and computational challenges faced by deep learning today. Nonetheless, current research in neuromorphic computing is driven by our well-developed notions of running deep learning algorithms on computing platforms that perform deterministic operations. In this article, we argue that taking a different route of performing temporal information encoding in probabilistic neuromorphic systems may help solve some of the current challenges in the field. The article considers superparamagnetic tunnel junctions as a potential pathway to enable a new generation of brain-inspired computing that combines the facets and associated advantages of two complementary insights from computational neuroscience – how information is encoded and how computing occurs in the brain. Hardware-algorithm co-design analysis demonstrates 97.41% accuracy of a state-compressed 3-layer spintronics enabled stochastic spiking network on the MNIST dataset with high spiking sparsity due to temporal information encoding.

Improving Atmospheric Models by Accounting for Chaotic Physics

Abstract It is well known that randomly perturbing an atmospheric model’s diabatic tendencies can increase its probabilistic forecast skill, mainly by increasing the spread of ensemble forecasts and making it more consistent with the errors of ensemble-mean forecasts. Less obvious and less well established is that such perturbations can also reduce the errors of the ensemble-mean forecasts and improve the model’s mean climate, variability, and sensitivity to forcing. A clear reduction in ensemble-mean forecast errors is demonstrated here in large ensembles of 15-day forecasts made with NOAA’s Global Forecast System model. The nearly ubiquitous reduction around the globe, obtained throughout the forecast range, is interpreted as arising in effect from a modification of the model’s deterministic evolution operator by a stochastic noise-induced drift. The effect is general in systems with state-dependent noise, and occurs even if the noise is not white. In the atmospheric context considered here, the effect is suggested to arise largely from noise-induced reductions of mechanical and thermal damping by chaotic boundary layer and cloud-radiative processes, which also tend to increase model sensitivity to forcing. The results presented here are consistent with many previous studies performed with models ranging from simple stochastically forced models to comprehensive global weather and climate models. They suggest that the diabatic interactions in most current global atmospheric models may not be sufficiently chaotic and this deficiency could be partly remedied by specifying additional stochastic terms. Using some empirical guidance in such specifications may be unavoidable, given the generally intractable complexity of the diabatic interactions.

Fractional stochastic modelling of monkeypox dynamics

Monkeypox virus is primarily transferred to humans via wild animals including rodents and more often the transmission ensues between humans to humans. This disease has been neglected in the past and little effort has been made by research to study the dynamics of the disease. This paper aims to use a fractional stochastic modeling approach to investigate the Monkeypox model dynamics. In this work, a fractionalized mathematical model is constructed with an emphasis on the unstigmatized and stigmatized in the community. First, we carried out a deterministic model using fractional-order operator with Atangana–Baleanu–Caputo derivative. The basic properties and the dynamical behavior of the deterministic model are investigated. Next, the existence and uniqueness of the solutions of the model based on the fractional stochastic approach are examined. Numerical simulations of deterministic and stochastic fractional model are conducted to support the analytical results which depict that fractional order derivative has a serious effect on the dynamics of the monkeypox. This work presented more detailed the characteristic of the monkeypox dynamics than deterministic fractional operators. The infection rate in humans has an impact on the spread of the disease in the human compartment.

Time-Domain Universal Linear-Optical Operations for Universal Quantum Information Processing.

We demonstrate universal and programmable three-mode linear-optical operations in the time domain by realizing a scalable dual-loop optical circuit suitable for universal quantum information processing (QIP). The programmability, validity, and deterministic operation of our circuit are demonstrated by performing nine different three-mode operations on squeezed-state pulses, fully characterizing the outputs with variable measurements, and confirming their entanglement. Our circuit can be scaled up just by making the outer loop longer and also extended to universal quantum computers by incorporating feed forward systems. Thus, our work paves the way to large-scale universal optical QIP.

Quantum vortices of strongly interacting photons

Vortices are topologically nontrivial defects that generally originate from nonlinear field dynamics. All-optical generation of photonic vortices—phase singularities of the electromagnetic field—requires sufficiently strong nonlinearity that is typically achieved in the classical optics regime. We report on the realization of quantum vortices of photons that result from a strong photon-photon interaction in a quantum nonlinear optical medium. The interaction causes faster phase accumulation for copropagating photons, producing a quantum vortex-antivortex pair within the two-photon wave function. For three photons, the formation of vortex lines and a central vortex ring confirms the existence of a genuine three-photon interaction. The wave function topology, governed by two- and three-photon bound states, imposes a conditional phase shift of π per photon, a potential resource for deterministic quantum logic operations.