Application Of Computation Research Articles

Harnessing ferro-valleytricity in pentalayer rhombohedral graphene for memory and compute

Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such as valley-dependent Hall effects and orbital magnetism, which could revolutionize next-generation electronics. However, achieving independent control over valley polarization and orbital magnetism has been a challenge due to the need for large electric fields. A recent breakthrough involving pentalayer rhombohedral graphene has demonstrated the ability to individually manipulate anomalous Hall signals and orbital magnetic hysteresis, forming what is known as a valley-magnetic quartet. Here, we leverage the electrically tunable ferro-valleytricity of pentalayer rhombohedral graphene to develop nonvolatile memory and in-memory computation applications. We propose an architecture for a dense, scalable, and selector-less nonvolatile memory array that harnesses the electrically tunable ferro-valleytricity. In our designed array architecture, nondestructive read and write operations are conducted by sensing the valley state through two different pairs of terminals, allowing for independent optimization of read/write peripheral circuits. The power consumption of our PRG-based array is remarkably low, with only ∼6 nW required per write operation and ∼2.3 nW per read operation per cell. This consumption is orders of magnitude lower than that of the majority of state-of-the-art cryogenic memories. Additionally, we engineer in-memory computation by implementing majority logic operations within our proposed nonvolatile memory array without modifying the peripheral circuitry. Our framework presents a promising pathway toward achieving ultra-dense cryogenic memory and in-memory computation capabilities.

Complete Set of Unitary Irreps of Discrete Heisenberg Group HW2s

Following the method of induced group representations of Wigner-Mackay, the explicit construction of all the unitary irreducible representations of the discrete finite Heisenberg-Weyl group HW2s over the discrete phase space lattice Z2s⊗Z2s is presented. We explicitly determine their characters and their fusion rules. We discuss possible physical applications for finite quantum mechanics and quantum computation.

Infinite quantum signal processing

Quantum signal processing (QSP) represents a real scalar polynomial of degree d using a product of unitary matrices of size 2×2, parameterized by (d+1) real numbers called the phase factors. This innovative representation of polynomials has a wide range of applications in quantum computation. When the polynomial of interest is obtained by truncating an infinite polynomial series, a natural question is whether the phase factors have a well defined limit as the degree d→∞. While the phase factors are generally not unique, we find that there exists a consistent choice of parameterization so that the limit is well defined in the ℓ1 space. This generalization of QSP, called the infinite quantum signal processing, can be used to represent a large class of non-polynomial functions. Our analysis reveals a surprising connection between the regularity of the target function and the decay properties of the phase factors. Our analysis also inspires a very simple and efficient algorithm to approximately compute the phase factors in the ℓ1 space. The algorithm uses only double precision arithmetic operations, and provably converges when the ℓ1 norm of the Chebyshev coefficients of the target function is upper bounded by a constant that is independent of d. This is also the first numerically stable algorithm for finding phase factors with provable performance guarantees in the limit d→∞.

Exponentially Improved Dispersive Qubit Readout with Squeezed Light.

It has been a long-standing goal to improve dispersive qubit readout with squeezed light. However, injected external squeezing (IES) cannot enable a practically interesting increase in the signal-to-noise ratio (SNR), and simultaneously, the increase of the SNR due to the use of intracavity squeezing (ICS) is even negligible. Here, we counterintuitively demonstrate that using IES and ICS together can lead to an exponential improvement of the SNR for any measurement time, corresponding to a measurement error reduced typically by many orders of magnitude. More remarkably, we find that in a short-time measurement, the SNR is even improved exponentially with twice the squeezing parameter. As a result, we predict a fast and high-fidelity readout. This work offers a promising path toward exploring squeezed light for dispersive qubit readout, with immediate applications in quantum error correction and fault-tolerant quantum computation.

Topological superconductivity in monolayer Td−MoTe2

Topological superconductivity has attracted significant attention due to its potential applications in quantum computation, but its experimental realization remains challenging. Recently, monolayer Td−MoTe2 was observed to exhibit gate tunable superconductivity, and its in-plane upper critical field exceeds the Pauli limit. Here, we show that an in-plane magnetic field beyond the Pauli limit can drive the superconducting monolayer Td−MoTe2 into a topological superconductor. The topological superconductivity arises from the interplay between the in-plane Zeeman coupling and the unique Ising plus in-plane spin-orbit coupling (SOC) in the monolayer Td−MoTe2. The Ising plus in-plane SOC plays the essential role to enable the effective px + ipy pairing. As the essential Ising plus in-plane SOC in the monolayer Td−MoTe2 is generated by an in-plane polar field, our proposal demonstrates that applying an in-plane magnetic field to a gate tunable 2D superconductor with an in-plane polar axis is a feasible way to realize topological superconductivity.

Understanding Information Disclosure from Secure Computation Output: A Comprehensive Study of Average Salary Computation

Secure multi-party computation has seen substantial performance improvements in recent years and is being increasingly used in commercial products. While a significant amount of work was dedicated to improving its efficiency under standard security models, the threat models do not account for information leakage from the output of secure function evaluation. Quantifying information disclosure about private inputs from observing the function outcome is the subject of this work. Motivated by the City of Boston gender pay gap studies, in this work we focus on the computation of the average of salaries and quantify information disclosure about private inputs of one or more participants (the target) to an adversary via information-theoretic techniques. We study a number of distributions including log-normal, which is typically used for modeling salaries. We consequently evaluate information disclosure after repeated evaluation of the average function on overlapping inputs, as was done in the Boston gender pay study that ran multiple times, and provide recommendations for using the sum and average functions in secure computation applications. Our goal is to develop mechanisms that lower information disclosure about participants’ inputs to a desired level and provide guidelines for setting up real-world secure evaluation of this function.

Non-hemolytic peptide classification using a quantum support vector machine

Quantum machine learning (QML) is one of the most promising applications of quantum computation. Despite the theoretical advantages, it is still unclear exactly what kind of problems QML techniques can be used for, given the current limitation of noisy intermediate-scale quantum devices. In this work, we apply the well-studied quantum support vector machine (QSVM), a powerful QML model, to a binary classification task which classifies peptides as either hemolytic or non-hemolytic. Using three peptide datasets, we apply and contrast the performance of the QSVM with a number of popular classical SVMs, out of which the QSVM performs best overall. The contributions of this work include: (i) the first application of the QSVM to this specific peptide classification task and (ii) empirical results showing that the QSVM is capable of outperforming many (and possibly all) classical SVMs on this classification task. This foundational work provides insight into possible applications of QML in computational biology and may facilitate safer therapeutic developments by improving our ability to identify hemolytic properties in peptides.

Quantum-inspired gravitational search algorithm-based low-price binary task offloading for multi-users in unmanned aerial vehicle-assisted edge computing systems

Unmanned aerial vehicles (UAVs)-assisted edge computing (EC) brings computing resources closer to smart mobile devices (SMDs). Users of SMDs with limited resources can experience the flavour of computation and data-intensive applications. Users conserve energy by offloading resource-intensive tasks to edge servers (ESs) or UAVs. While offloading may introduce communication delays, leveraging ample wireless bandwidth can mitigate this issue by facilitating faster data transmission rates. However, mobile service providers (MSPs), facilitating offloading infrastructure, impose charges on users for bandwidth usage. Efficient utilization of limited computation units (CUs) is crucial. This paper introduces a binary task offloading (BTO) model for multi-user in multi-UAV-assisted EC systems using a quantum-inspired gravitational search algorithm (QGSA). Quantum encoding and decoding of the agents are provided. The decoding phase uses a novel hashing. The fitness function considers energy, delay, price, and resource utilization. Two penalties are incorporated with the fitness to discard the decoded agent that violates the constraints of the BTO model. The proposed QGSA ensures polynomial time execution. The proposed work is extensively simulated in multiple scenarios and compared with existing approaches. It is observed that the QGSA outperformed other algorithms in terms of delay, energy, resource utilization, and price. Analyses of convergence and statistics are performed.

Photon-Induced Ultrafast Multitemporal Programming of Terahertz Metadevices.

Dynamic terahertz (THz) metasurface can feature modulated and multiplexed electromagnetic functionalities, important for wave-based computation, six-generation communications, and other applications. The versatile dynamic switching typically relies on a series of complex or incompatible multifield activations, with excessive system complexity, additional loss, slow modulation speed, and inertial time-varying properties, limiting more widespread applications. Here, a photon-induced ultrafast programmable THz metadevice is reoprted in time-frequency dimensions with polarization-decoupled temporal responses. By the pixelated design with multi-materials and triggering switches, the multimodal modulation transcends the constraints inherent in materials, enabling the ultrafast programmable temporal evolution. All the resonances can be independently programmed at the working band from 0.6 to 2 THz. The tri-temporal (with switching time of 1.25, 1, and 4.75ps) and bi-temporal (with switching time of 2.25 and 4.75ps) dynamic manipulations are performed by all-optical driven molecularization process of hybrid metasurfaces loaded with silicon (Si)and germanium (Ge) under different polarizations. Combining these features, the temporally programmed THz logic gates are last experimentally demonstrated, possessing basic operation of XNOR, NOR, and OR,as a proof-of-concept application. This reported light-driven programmable THz flat-optics allows ultrafast hybrid molecularization processes and new possibilities for miniaturized, flexible, multifunctional, and temporally programmable integrated devices.

Efficient multidimensional quantum random number generator using a CMOS SPAD array.

Quantum random number generators (QRNGs) can generate true random numbers and have significant applications in quantum communication, numerical computation, and model simulation. However, the rate of random number generation based on photon detection is constrained by the maximum count rate of a single-photon detector. Therefore, improving the efficiency of random number generation for individual photon detection events becomes an optional way to increase the rate of random number generation. In this paper, multidimensional photon detection is implemented to enhance single-photon detection events, thereby providing a new, to the best of our knowledge, technical development strategy for high-speed random number generators. The temporal and spatial coherence of coherent-state photons is utilized as a valuable quantum resource, enabling us to achieve the simultaneous extraction of time-space measurement collapsed randomness for single-photon detection events using a chip-scale CMOS-integrated single-photon avalanche diode array. The efficiency of random number generation for single-photon detection events is effectively improved. In our experiments, up to 20 bits can be extracted from an individual photon detection event, and the rate of random number generation reaches up to 2.067 Gbps.

Mathematical Foundations of Real Numbers and its Application in Computation

The study of Real Numbers (R) is a fundamental pillar of Mathematical Analysis, serving as the cornerstone for a broad spectrum of Mathematical principles and practical applications. Real Numbers are examined both in theory and in practice, impacting fields like pure Mathematics, physics, engineering, and economics. This paper thoroughly explores Real Numbers, looking at their properties, how they are represented, and their significant role in Mathematical research and various applications. We highlight the importance of Real Numbers in advancing our understanding of Mathematics. This research article provides a comprehensive overview of the Mathematical foundations on Real Numbers and its application in computation. It explores historical developments, theoretical frameworks, Mathematical proofs, comparative analyses, applications, and future directions in the study of Real Numbers. The study aims to synthesize existing knowledge, highlight key contributions, theoretical framework and application in computation.

Recent Developments on Novel 2D Materials for Emerging Neuromorphic Computing Devices

The rapid advancement of artificial intelligent and information technology has led to a critical need for extremely low power consumption and excellent efficiency. The capacity of neuromorphic computing to handle large amounts of data with low power consumption has garnered a lot of interest during the last few decades. For neuromorphic applications, 2D layered semiconductor materials have shown a pivotal role due to their distinctive properties. This comprehensive review provides an extensive study of the recent advancements in 2D materials‐based neuromorphic devices especially in multiterminal synaptic devices, two‐terminal synaptic devices, neuronal devices, and the integration of synaptic and neuronal devices. Herein, a wide range of potential applications of memory, computation, adaptation, and artificial intelligence is incorporated. Finally, the limitations and challenges of neuromorphic devices based on novel 2D materials are discussed. Thus, this review aims to illuminate the design and fabrication of neuromorphic devices based on van der Waals (vdW) heterostructure materials, leveraging promising engineering techniques to excel the applications and potential of neuromorphic computing for hardware implementations.

Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components-the so-called agents-discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment.

Spontaneous parametric downconversion in a laser-poled lithium niobate nonlinear photonic crystal with nanoscale resolution.

Based on quasi-phase-matching (QPM) theory, nonlinear photonic crystals (NPCs) are capable of realizing efficient spontaneous parametric downconversion (SPDC) for the generation of photonic entangled states. However, the traditional electric field poling techniques employed in NPC fabrication often result in non-negligible processing errors of a few hundred nanometers, thus impeding the production of quantum photon pairs as intended. In this work, we investigate the SPDC photon pairs generated in a laser-poled lithium niobate (LN) NPC. By using the recently developed laser poling technique, the processing error of the NPC is substantially reduced to approximately 15 nm. Consequently, the coincidence counts of the generated photon pairs in the experiment reach 83.6% of the designed value. Our result paves the way for on-demand production of high-quality quantum sources, which has potential applications in quantum communications and quantum computations.

The Multiple Millionaires' Problem: New Algorithmic Approaches and Protocols

We study a fundamental problem in Multi-Party Computation, which we call the Multiple Millionaires Problem (MMP). Given a set of private integer inputs, the problem is to identify the subset of inputs that equal the maximum (or minimum) of that set, without revealing any further information on the inputs beyond what is implied by the desired output. Such a problem is a natural extension of the Millionaires Problem, which is the very first Multi-Party Computation problem that was presented in Andrew Yaos seminal work (FOCS 1982). A closely related problem is MaxP, in which the value of the maximum is sought. We study these fundamental problems and describe several algorithmic approaches and protocols for their solution. In addition, we compare the performance of the protocols under several selected settings. As applications of privacy-preserving computation are more and more commonly implemented in industrial systems, MMP and MaxP become important building blocks in privacy-preserving statistics, machine learning, auctions and other domains. One of the prominent advantages of the protocols that we present here is their simplicity. As they solve fundamental problems that are essential building blocks in various application scenarios, we believe that the presented solutions to those problems, and the comparison between them, will serve well future researchers and practitioners of secure distributed computing.

Strain-Controlled Electronic and Magnetic Properties of Janus Nitride MXene Monolayer MnCrNO2

Two-dimensional (2D) van der Waals (vdW) magnetic materials show potential for the advancement of high-density, energy-efficient electronic and spintronic applications in future memory and computation. Here, by using first-principles density functional theory (DFT) calculations, we predict a new 2D Janus nitride MXene MnCrNO2 monolayer. Our results suggest that the optimized MnCrNO2 monolayer possesses a hexagonal structure and exhibits good dynamical stability. The intrinsic monolayer MnCrNO2 exhibits semiconductive properties and adopts a ferromagnetic ground state with an out-of-plane easy axis. It can sustain strain effects within a wide range of strains from −10% to +8%, as indicated by the phonon dispersion spectra. Under the biaxial tensile strain, a remarkable decrease in the bandgap of the MnCrNO2 is induced, which is attributed to the distinct roles played by Mn and Cr in the VBM or CBM bands. Furthermore, when the compressive strain reaches approximately −8%, the magnetic anisotropy undergoes a transition from an out-of-plane easy axis to an in-plane easy axis. This change is mainly influenced by the efficient hybridization of the d orbitals, particularly in Mn atoms. Our study of the Janus MXene MnCrNO2 monolayer indicates its potential as a promising candidate for innovative electronic and spintronic devices; this potential is expected to create interest in its synthesis.

Monolith: Circuit-Friendly Hash Functions with New Nonlinear Layers for Fast and Constant-Time Implementations

Hash functions are a crucial component in incrementally verifiable computation (IVC) protocols and applications. Among those, recursive SNARKs and folding schemes require hash functions to be both fast in native CPU computations and compact in algebraic descriptions (constraints). However, neither SHA-2/3 nor newer algebraic constructions, such as Poseidon, achieve both requirements. In this work we overcome this problem in several steps. First, for certain prime field domains we propose a new design strategy called Kintsugi, which explains how to construct nonlinear layers of high algebraic degree which allow fast native implementations and at the same time also an efficient circuit description for zeroknowledge applications. Then we suggest another layer, based on the Feistel Type-3 scheme, and prove wide trail bounds for its combination with an MDS matrix. We propose a new permutation design named Monolith to be used as a sponge or compression function. It is the first arithmetization-oriented function with a native performance comparable to SHA3-256. At the same time, it outperforms Poseidon in a circuit using the Merkle tree prover in the Plonky2 framework. Contrary to previously proposed designs, Monolith also allows for efficient constant-time native implementations which mitigates the risk of side-channel attacks.

Programming Fast DNA Amplifier Circuits with Versatile Toehold Exchange Pathway.

DNA amplifier circuits establish powerful tools to dynamically control molecular assembly for computation, sensing, and biological applications. However, the slow reaction speed remains a major barrier to their practical utility. Here, diverse fast DNA amplifier circuits termed toehold exchange polymerization (TEP) and toehold exchange catalysis (TEC) using toehold exchange-mediated assembly as a fundamental mechanism are built. Both TEP and TEC with a duplex and a hairpin can respond within minutes to diverse nucleic acid inputs with high fidelity. In addition, the circuits can amplify live-cell signals for fluorescence imaging target RNA dynamics and discriminating different cell lines. Compared with existing DNA circuits that involve time scales of hours for transducing small signals, TEP and TEC exhibit much faster dynamics, simpler design, and comparable sensitivity. These features make TEP and TEC promising platforms to develop programmable nucleic acid tools and devices and to create fast sensing and processing systems, amenable to wide practical applications.

Volatility dynamics in energy and agriculture markets: An analysis of domestic and global uncertainty factors

PurposeThis study aims to explore the intricate relationship between uncertainty indicators and volatility of commodity futures, with a specific focus on agriculture and energy sectors.Design/methodology/approachThe authors analyse the volatility of Indian agriculture and energy futures using the GARCH-MIDAS model, taking into account different types of uncertainty factors. The evaluation of out-sample predictive capability involves the application of out-sample R-squared test and computation of various loss functions.FindingsThe research outcomes underscore the significant impact of diverse uncertainty factors such as domestic economic policy uncertainty (EPU), global EPU (GEPU), US EPU and geopolitical risk (GPR) on long-run volatility of Indian energy and agriculture (agri) futures. Additionally, the study demonstrates that GPR exhibits superior predictive capability for crude oil futures volatility, while domestic EPU stands out as an effective predictor for agri futures, particularly castor seed and guar gum.Practical implicationsThe study offers practical implications for market participants and policymakers to adopt a comprehensive perspective, incorporating diverse uncertainty factors, for informed decision-making and effective risk management in commodity markets.Originality/valueThe research makes an inaugural attempt to examine the impact of domestic and global uncertainty indicators on modelling and predicting volatility in energy and agri futures. The distinctive feature of considering an emerging market also adds a novel dimension to the research landscape.

A new generalized perspective to establish vortex beams

Vortex beams are powerful primitives for optical operations and communications. We propose general mathematical methods to establish new vortex beams and optimize existing vortex beams. Based on these methods, two new types of vortex beams are demonstrated theoretically and experimentally. The propagation characteristics of these vortex beams can be finely controlled through specific parameters. Additionally, influences of periodic discontinuities and orbital angular momentum of two vortex beams are analyzed. The innovative concept of beam modulation and the introduction of new vortex beams in this paper indicate significant potential for applications in particle trapping, spatial communication, and quantum computation.