Resource Overhead Research Articles

Low-overhead quantum computing with the color code

Fault-tolerant quantum computation demands significant resources: large numbers of physical qubits must be checked for errors repeatedly to protect quantum data as logic gates are implemented in the presence of noise. We demonstrate that an approach based on the color code can lead to considerable reductions in the resource overheads compared with conventional methods, while remaining compatible with a two-dimensional layout. We propose a lattice surgery scheme that exploits the rich structure of the color-code phase to perform arbitrary pairs of commuting logical Pauli measurements in parallel while keeping the space cost low. Compared to lattice surgery schemes based on the surface code with the same code distance, and assuming the same amount of time is needed to complete a round of syndrome measurements, our approach yields about a 3× improvement in the space-time overhead, obtained from a combination of a 1.5× improvement in spatial overhead together with a 2× speedup due to the parallelization of commuting logical measurements. Even when taking into account the color code's lower error threshold using current decoders, the overhead is reduced by 10% at a physical error rate of 10−3 and by 50% at 10−4. Published by the American Physical Society 2024

Tra ined Wi thout My C onsent: Detecting Code Inclusion In Language Models Trained on Code

Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not contain code from protected sources. The recent advent of Large Language Models (LLMs) as coding assistants in the software development process poses new challenges for code auditing. The dataset for training these models is mainly collected from publicly available sources. This raises the issue of intellectual property infringement as developers’ codes are already included in the dataset. Therefore, auditing code developed using LLMs is challenging, as it is difficult to reliably assert if an LLM used during development has been trained on specific copyrighted codes, given that we do not have access to the training datasets of these models. Given the non-disclosure of the training datasets, traditional approaches such as code clone detection are insufficient for asserting copyright infringement. To address this challenge, we propose a new approach, TraWiC; a model-agnostic and interpretable method based on membership inference for detecting code inclusion in an LLM’s training dataset. We extract syntactic and semantic identifiers unique to each program to train a classifier for detecting code inclusion. In our experiments, we observe that TraWiC is capable of detecting 83.87% of codes that were used to train an LLM. In comparison, the prevalent clone detection tool NiCad is only capable of detecting 47.64%. In addition to its remarkable performance, TraWiC has low resource overhead in contrast to pair-wise clone detection that is conducted during the auditing process of tools like CodeWhisperer reference tracker, across thousands of code snippets.

Decoding quantum color codes with MaxSAT

In classical computing, error-correcting codes are well established and are ubiquitous both in theory and practical applications. For quantum computing, error-correction is essential as well, but harder to realize, coming along with substantial resource overheads and being concomitant with needs for substantial classical computing. Quantum error-correcting codes play a central role on the avenue towards fault-tolerant quantum computation beyond presumed near-term applications. Among those, color codes constitute a particularly important class of quantum codes that have gained interest in recent years due to favourable properties over other codes. As in classical computing, decoding is the problem of inferring an operation to restore an uncorrupted state from a corrupted one and is central in the development of fault-tolerant quantum devices. In this work, we show how the decoding problem for color codes can be reduced to a slight variation of the well-known LightsOut puzzle. We propose a novel decoder for quantum color codes using a formulation as a MaxSAT problem based on this analogy. Furthermore, we optimize the MaxSAT construction and show numerically that the decoding performance of the proposed decoder achieves state-of-the-art decoding performance on color codes. The implementation of the decoder as well as tools to automatically conduct numerical experiments are publicly available as part of the Munich Quantum Toolkit (MQT) on GitHub.

Gibbs state sampling via cluster expansions

Gibbs states (i.e., thermal states) can be used for several applications such as quantum simulation, quantum machine learning, quantum optimization, and the study of open quantum systems. Moreover, semi-definite programming, combinatorial optimization problems, and training quantum Boltzmann machines can all be addressed by sampling from well-prepared Gibbs states. With that, however, comes the fact that preparing and sampling from Gibbs states on a quantum computer are notoriously difficult tasks. Such tasks can require large overhead in resources and/or calibration even in the simplest of cases, as well as the fact that the implementation might be limited to only a specific set of systems. We propose a method based on sampling from a quasi-distribution consisting of tensor products of mixed states on local clusters, i.e., expanding the full Gibbs state into a sum of products of local “Gibbs-cumulant” type states easier to implement and sample from on quantum hardware. We begin with presenting results for 4-spin linear chains with XY spin interactions, for which we obtain the ZZ dynamical spin-spin correlation functions and dynamical structure factor. We also present the results of measuring the specific heat of the 8-spin chain Gibbs state ρ8.

Genuine non-Gaussian entanglement of light and quantum coherence for an atom from noisy multiphoton spin-boson interactions

Harnessing entanglement and quantum coherence plays a central role in advancing quantum technologies. In quantum optical light-atom platforms, these two fundamental resources are often associated with a Jaynes-Cummings model description describing the coherent exchange of a photon between an optical resonator mode and a two-level spin. In a generic nonlinear spin-boson system, more photons and more modes will take part in the interactions. Here we consider such a generalization: the two-mode multiphoton Jaynes-Cummings (MPJC) model. We demonstrate how entanglement and quantum coherence can be optimally generated and subsequently manipulated in experimentally accessible parameter regimes. A detailed comparative analysis of this model reveals that nonlinearities within the MPJC interactions produce genuinely non-Gaussian entanglement, devoid of Gaussian contributions, from noisy resources. More specifically, strong coherent sources may be replaced by weaker, incoherent ones, significantly reducing the resource overhead, though at the expense of reduced efficiency. At the same time, increasing the multiphoton order of the MPJC interactions expedites the entanglement generation process, thus rendering the whole generation scheme again more efficient and robust. We further explore the use of additional dispersive spin-boson interactions and Kerr nonlinearities in order to create spin coherence solely from incoherent sources and to enhance the quantum correlations, respectively. As for the latter, somewhat unexpectedly, there is not necessarily an increase in quantum correlations due to the augmented nonlinearity. Towards possible applications of the MPJC model, we show how, with appropriately chosen experimental parameters, we can engineer arbitrary NOON states as well as the tripartite W state. Published by the American Physical Society 2024

A Taxonomy of Knowledge Management Systems in the Micro-Enterprise

Knowledge Management Systems enhance innovation, increase operational efficiency, and improve decision-making in business organisations. The administrative and resource overheads required to implement and maintain such systems inherently exclude the smallest of firms from reaping these benefits. This research aims to identify, evaluate, and summarize the distribution of research on knowledge management and supporting systems across business size classifications with a particular focus on micro-enterprises. It also seeks to establish if existing knowledge management models, practices, and systems have invested due consideration in their design to cater for the limited resources typically found in the micro-enterprise. It contributes new insight into the applicability of knowledge management systems to micro-enterprises and it stimulates a possible re-think of how such systems can cater to the specific constraints of this prolific business type. This taxonomy is the result of a thorough analysis of 168 research papers from a total of 10511 papers published in reputable conference proceedings since 2012. It focuses on key knowledge management themes covered, including the size of the enterprise, the adoption challenges, the potential benefits, the technologies used, and the aspects of the knowledge management cycle that are being employed. Furthermore, it draws on this analysis to highlight the appropriateness of existing knowledge management systems to the distinctive risk and opportunity characteristics of the micro-enterprise.

Gas Sensor Physically Unclonable Function-Based Lightweight Bidirectional Authentication Protocol for Gas Sensor Networks

In gas sensor networks, users can access the data collected by the sensor nodes, but there is a risk of data leakage during transmission. This paper proposes a lightweight bidirectional authentication protocol based on gas sensor physically unclonable functions (GS-PUFs) with authentication technology to guarantee the reliability of data from sensor nodes. A sensor PUF array is constructed by preparing gas sensors to enhance the data security of the physical layer and reduce hardware resource consumption. The authentication part of the protocol mainly uses lightweight encryption methods, consisting of PUF data, one-way cryptographic hash functions, and iso-or functions, to reduce the computational overhead of the authentication process. The protocol security is enhanced by encrypting the GS-PUF response as an irreversible hash value and verifying the hash value by the user, server, and sensor node to complete bidirectional authentication. The test results demonstrate that the protocol, verified through the ProVerif formal tool, can resist impersonation, replay, node tampering, and cloning attacks. Among the compared schemes, this protocol offers the highest security and the least resource overhead, making it effectively applicable in the Internet of Things and other fields.

A secure authentication framework for IoV based on blockchain and ensemble learning

A secure authentication framework based on blockchain and ensemble learning is proposed to address the problem that vehicle identity privacy data in Internet of Vehicles (IoV) is vulnerable to theft and tampering. First, a secure and efficient authentication method based on blockchain and Physical Unclonable Function (PUF) is implemented, which ensures the identity privacy of the vehicle when accessing IoV, and improves the problem of high resource overhead of the traditional IoV authentication scheme while guaranteeing security, and the computational overhead is about 2.424 ms at the first level of security framework. Secondly, an intrusion detection method based on Whale Optimization Algorithm (WOA) and Extreme Gradient Boosting (XGBoost) is proposed, and the detection model trained based on this method can effectively detect various attacks against IoV. As a security method at the second level of secure framework, the method outperforms related works in detecting malicious attacks with a detection accuracy of 98.41% for ToN-IoT and 99.99% for BoT-IoT.

Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization

Recently, researchers have achieved the goal of using unified architecture to achieve multiple modulation modes under specific conditions. However, existing research still suffers from the problem of large resource and time overhead. This paper proposes an adaptive modem based on a vector quantization (VQ) long short-term memory autoencoder (LSTM-AE), designed to implement modulation and demodulation of signals from the second to thirty-second order in a lightweight way. By leveraging the memory capacity of the LSTM module and the compression capability of the autoencoder, the model is able to support multi-order modulation methods. This study used the Adam optimizer for training and testing on a simple dataset extended by adding AWGN noise only and made modifications based on the MSE loss function to balance the accuracy and training speed of each part of the model. Experiments demonstrate that the proposed method not only achieves comparable modem performance to existing frameworks for second to thirty-second order signals, but also significantly reduces the number of parameters and training time. Experimental results indicate that the proposed methodology not only matches the performance of existing frameworks for signals ranging from the second to the thirty-second order, but also employs merely 79.6% of the average parameter count and a mere 7.4% of the average training duration. This represents a substantial reduction in resource expenditure.

Exponentially tighter bounds on limitations of quantum error mitigation

Quantum error mitigation has been proposed as a means to combat unwanted and unavoidable errors in near-term quantum computing without the heavy resource overheads required by fault-tolerant schemes. Recently, error mitigation has been successfully applied to reduce noise in near-term applications. In this work, however, we identify strong limitations to the degree to which quantum noise can be effectively ‘undone’ for larger system sizes. Our framework rigorously captures large classes of error-mitigation schemes in use today. By relating error mitigation to a statistical inference problem, we show that even at shallow circuit depths comparable to those of current experiments, a superpolynomial number of samples is needed in the worst case to estimate the expectation values of noiseless observables, the principal task of error mitigation. Notably, our construction implies that scrambling due to noise can kick in at exponentially smaller depths than previously thought. Noise also impacts other near-term applications by constraining kernel estimation in quantum machine learning, causing an earlier emergence of noise-induced barren plateaus in variational quantum algorithms and ruling out exponential quantum speed-ups in estimating expectation values in the presence of noise or preparing the ground state of a Hamiltonian.

LSMOF-AD: Three-Stage Optimization Approach with Adaptive Differential for Large-Scale Container Scheduling

Container technology has gained a widespread application in cloud computing environments due to its low resource overhead and high flexibility. However, as the number of containers grows, it becomes increasingly challenging to achieve the rapid and coordinated optimization of multiple objectives for container scheduling, while maintaining system stability and security. This paper aims to overcome these challenges and provides the optimal allocation for a large number of containers. First, a large-scale multi-objective container scheduling optimization model is constructed, which involves the task completion time, resource cost, and load balancing. Second, a novel optimization algorithm called LSMOF-AD (large-scale multi-objective optimization framework with muti-stage and adaptive differential strategies) is proposed to effectively handle large-scale container scheduling problems. The experimental results show that the proposed algorithm has a better performance in multiple benchmark problems compared to other advanced algorithms and can effectively reduce the task processing delay, while achieving a high resource utilization and load balancing compared to other scheduling strategies.

An edge‐based event‐triggered delayed distributed algorithm for economic dispatch in smart grids

AbstractThis paper presents an edge‐based event‐triggered delay distributed algorithm for solving the economic dispatch problem (EDP) in smart grids. The objective of the EDP is to minimize the total generation cost by allocating power to individual generators, each with its local generation constraint. To save the overhead of communication resources between agents, an edge‐based event‐triggered mechanism is suggested. By setting distinct triggering thresholds for each communication edge, the agent may efficiently regulate the communication frequency among all neighbors. However, in practice, owing to the instability of the network, the agent may receive information regarding its neighbors, leading to communication lags for every communication link. The virtual agent technique and double‐stochastic matrix augmentation technique provide an equivalent delay‐free EDP. It is demonstrated that the proposed algorithm can asymptotically converge to the global optimal solution as long as the communication delay is arbitrary, time‐varying, and random, but bounded. Objective and clear evidence is provided through a case study in smart grids to verify the algorithm's feasibility.

Lightweight adaptive Byzantine fault tolerant consensus algorithm for distributed energy trading

With the rapid development of smart grid, constructing distributed energy trading market (DETM) based on blockchain to coordinate distributed energy resources (DER) has become a future direction. However, existing consensus algorithms of blockchain face many challenges in large-scale energy trading scenarios, such as high resource overhead, slow transaction procedure. To solve the above crucial problems for wide deployment of distributed energy trading, this study proposes a novel consensus algorithm named Lightweight adaptive Byzantine fault tolerant consensus (LA-BFT), and a reputation calculation method based on behavioral characteristics for selection of consensus nodes. The LA-BFT consists of two parts: (i) weak consensus for normal cases. By introducing threshold signature mechanism, weak consensus simplifies the consensus process to achieve linear communication complexity O(n). (ii) byzantine node detection scheme is enable for malicious cases. With consensus committee, the detection scheme can detect the potential byzantine nodes by cross-validation, which ensures transaction safety. The reputation calculation method is presented to cooperate with LA-BFT to elect leaders and candidates for consensus procedure. Once a round of consensus is completed, the reputation of each node needs to be updated, only nodes with high reputation are eligible to become leaders or committee nodes in the next round. With the reputation calculation method, honest nodes and byzantine nodes can be effectively identified, ensuring the security of the consensus process. Numerical results indicate that LA-BFT exhibits superior performance on communication overhead and bandwidth occupancy in large-scale concurrent energy trading scenarios. When the number of nodes is 50, under normal scenarios, LA-BFT’s communication overhead is notably lower, constituting a mere 6.02% of PBFT and 24.26% of SHBFT, while bandwidth occupancy amounts to merely 5.55% of PBFT and 9.76% of SHBFT.

Sentiment analysis of social media comments based on multimodal attention fusion network

Social media comments are no longer in a single textual modality, but heterogeneous data in multiple modalities, such as vision, sound, and text, which is why multimodal sentiment analysis strategies has been introduced. However, among the multimodal sentiment analysis domains, a majority of the current multimodal sentiment analysis models employ the Transformer architecture due to its great impact and benefits, thereby leading to an augmentation in resource overhead. In this paper, a multimodal attention fusion (MAF) network model is proposed for sentiment analysis of multimodal data. MAF is mainly composed of the cross attention and residual unit. The Cross Attention Unit is designed to select one core modality out of three modes, while the remaining modes serve as base modalities. The core modality is then combined with the base modality information to facilitate significant interaction between the two modalities, resulting in three sets of two-by-two attention computations. Moreover, a residual unit is employed to integrate the overall information into the attentional information. This approach not only enables modality-to-modality interaction, but also supplements the overall information. In the end, experiments are conducted on two publicly available multimodal sentiment analysis datasets from Carnegie Mellon University(CMU), CMU-MOSEI (abbreviated as MOSEI) and CMU-MOSI (abbreviated as MOSI), to validate that the method achieves high performance while removing the complex structure, and is comparable to the State-Of-The-Art(SOTA) model with high-performance A100 and V100 Graphics Processing Units(GPU) in an ordinary hardware environment.

Distance optimization KNN and EMD based lightweight hardware IP core design for EEG epilepsy detection

Long-term and effective detection of epileptic seizures is a crucial aspect of epilepsy monitoring and treatment. Addressing the resource overhead issue of wearable epilepsy detection devices, this paper proposes a lightweight hardware implementation scheme for epilepsy detection based on a reusable architecture empirical mode decomposition (EMD) and K-Nearest Neighbors (KNN). Firstly, EMD is used to extract epileptic features from electroencephalogram (EEG), optimized through a reusable architecture design and sawtooth transform to reduce hardware resource usage. Subsequently, a KNN classifier with similarity judgment mechanism is designed to improve the recognition efficiency. Implemented on TSMC 65 nm process, the circuit area is 1.91 mm2, operates at 1 V and 20 MHz, with a power consumption of 4.034 mW. Evaluation on the Bonn EEG dataset yielded a classification accuracy of 96 %, sensitivity of 98 %, and a single detection delay of 1.51 ms. The hardware design offers a simple structure, high accuracy, and low resource consumption, making it suitable for wearable epilepsy detection devices.

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

Fault-Tolerant Quantum Computation Using Large Spin-Cat Codes

We construct a fault-tolerant quantum error-correcting protocol based on a qubit encoded in a large spin qudit using a spin-cat code, analogous to the continuous-variable cat encoding. With this, we can correct the dominant error sources, namely processes that can be expressed as error operators that are linear or quadratic in the components of angular momentum. Such codes tailored to dominant error sources can exhibit superior thresholds and lower resource overheads when compared to those designed for unstructured noise models. A key component is the gate that preserves the rank of spherical tensor operators. Categorizing the dominant errors as phase and amplitude errors, we demonstrate how phase errors, analogous to phase-flip errors for qubits, can be effectively corrected. Furthermore, we propose a measurement-free error-correction scheme to address amplitude errors without relying on syndrome measurements. Through an in-depth analysis of logical gate errors, we establish that the fault-tolerant threshold for error correction in the spin-cat encoding surpasses that of standard qubit-based encodings. We consider a specific implementation based on neutral-atom quantum computing, with qudits encoded in the nuclear spin of 87Sr, and show how to generate the universal gate set, including the rank-preserving gate, using quantum control and the Rydberg blockade. These findings pave the way for encoding a qubit in a large spin with the potential to achieve fault tolerance, high threshold, and reduced resource overhead in quantum information processing. Published by the American Physical Society 2024

A Hardware Security Protection Method for Conditional Branches of Embedded Systems.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to tamper with the branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of branch target buffer (BTB) updates every 80 clock cycles. If the number exceeds the set threshold, the BTB will be locked and prevent any process from tampering with the BTB entries, thereby resisting branch prediction analysis (BPA) attacks. Moreover, to prevent attackers from stealing the critical information of branches, the method designs the hybrid arbiter physical unclonable function (APUF) circuit to encrypt and decrypt the directions, addresses, and indexes of branches. This circuit combines the advantages of double APUF and Feed-Forward APUF, which can enhance the randomness of output response and resist machine learning attacks. If attackers still successfully tamper with the branches and disrupt the control flow integrity (CFI), this method detects tampering with the instruction codes, jump addresses, and jump directions in a timely manner through dynamic and static label comparison. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization

Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 100 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources.

Performance of Rotation‐Symmetric Bosonic Codes in a Quantum Repeater Network

Performance of Rotation‐Symmetric Bosonic Codes in a Quantum Repeater Network