Resource Overhead Research Articles

Mitiq: A software package for error mitigation on noisy quantum computers

We introduce Mitiq, a Python package for error mitigation on noisy quantum computers. Error mitigation techniques can reduce the impact of noise on near-term quantum computers with minimal overhead in quantum resources by relying on a mixture of quantum sampling and classical post-processing techniques. Mitiq is an extensible toolkit of different error mitigation methods, including zero-noise extrapolation, probabilistic error cancellation, and Clifford data regression. The library is designed to be compatible with generic backends and interfaces with different quantum software frameworks. We describe Mitiq using code snippets to demonstrate usage and discuss features and contribution guidelines. We present several examples demonstrating error mitigation on IBM and Rigetti superconducting quantum processors as well as on noisy simulators.

Blueprint for a High-Performance Fluxonium Quantum Processor

The technological development of hardware heading toward universal fault-tolerant quantum computation requires a large-scale processing unit with high performance. While fluxonium qubits are promising with high coherence and large anharmonicity, their scalability has not been systematically explored. In this work, we propose a superconducting quantum information processor based on compact high-coherence fluxoniums with suppressed crosstalk, reduced design complexity, improved operational efficiency, high-fidelity gates, and resistance to parameter fluctuations. In this architecture, the qubits are readout dispersively using individual resonators connected to a common bus and manipulated via combined on-chip RF and DC control lines, both of which can be designed to have low crosstalk. A multi-path coupling approach enables exchange interactions between the high-coherence computational states and at the same time suppresses the spurious static ZZ rate, leading to fast and high-fidelity entangling gates. We numerically investigate the cross resonance controlled-NOT and the differential AC-Stark controlled-Z operations, revealing low gate error for qubit-qubit detuning bandwidth of up to 1 GHz. Our study on frequency crowding indicates high fabrication yield for quantum processors consisting of over thousands of qubits. In addition, we estimate low resource overhead to suppress logical error rate using the XZZX surface code. These results promise a scalable quantum architecture with high performance for the pursuit of universal quantum computation.

High-fidelity photonic quantum logic gate based on near-optimal Rydberg single-photon source

Compared to other types of qubits, photon is one of a kind due to its unparalleled advantages in long-distance quantum information exchange. Therefore, photon is a natural candidate for building a large-scale, modular optical quantum computer operating at room temperature. However, low-fidelity two-photon quantum logic gates and their probabilistic nature result in a large resource overhead for fault tolerant quantum computation. While the probabilistic problem can, in principle, be solved by employing multiplexing and error correction, the fidelity of linear-optical quantum logic gate is limited by the imperfections of single photons. Here, we report the demonstration of a linear-optical quantum logic gate with truth table fidelity of 99.84(3)% and entangling gate fidelity of 99.69(4)% post-selected upon the detection of photons. The achieved high gate fidelities are made possible by our near-optimal Rydberg single-photon source. Our work paves the way for scalable photonic quantum applications based on near-optimal single-photon qubits and photon-photon gates.

A More Accurate and Robust Binary Ring-LWE Decryption Scheme and Its Hardware Implementation for IoT Devices

Learning with error (LWE) over the ring based on binary distribution (ring-BinLWE) has become a potential Internet-of-Things (IoT) confidentiality solution with its anti-quantum attack properties and uncomplicated calculations. Compared with ring-LWE based on discrete Gaussian distribution, the decryption scheme of ring-LWE based on binary distribution needs to be re- determined due to the asymmetry of the error distribution. The direct application of the ring-LWE decryption function based on discrete Gaussian distribution can cause serious misjudgment. In this article, we propose a more accurate and robust decryption scheme for ring-BinLWE based on 2’s complement ring. Compared with the previous decryption function, the re- derived decryption function significantly improves the decoding rate by 50%. Furthermore, based on the proposed decryption function, high-performance, and lightweight hardware architectures for terminal devices in IoT are, respectively, proposed, which are scalable and can be easily adapted to ring-BinLWE hardware deployment with other parameter sets. When the parameter set is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\,\,=$ </tex-math></inline-formula> 256, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q\,\,=$ </tex-math></inline-formula> 256, the high-performance implementation consumes 7.6k LUTs, 6.2k FFs, and 2.3k SLICEs on Spartan 6 field-programmable gate array (FPGA) platform. Compared with the previous implementation, our resource overhead increases by only 23% while the decryption accuracy is significantly improved by 50%. The lightweight implementation for parameter set <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\,\,=$ </tex-math></inline-formula> 256, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q\,\,=$ </tex-math></inline-formula> 256 consumes only 230 LUTs, 338 FFs, and 84 SLICEs on the Spartan 6 FPGA platform. Compared with the previous work, the area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> time (AT) is reduced by 47.8%, which is more suitable for deployment on resource-constrained IoT nodes.

Research Management in "Many Analysts" Projects

With the recent increase of large scientific collaborations in the form of "Many Analysts" projects, research project management has quickly moved from small/medium-scale coordination to large-scale planning. Project coordinators are faced with unprecedented challenges which make "Many Analysts" projects time consuming and resource demanding. With this poster, we would like to share our experience managing the ongoing Many Speech Analysis project (https://many-speech-analyses.github.io) and offer tips and logistic solutions to minimise the resource overhead required for such type of projects. We will also discuss which steps in the process didn't go as expected and how on hindsight we wished we approached them.&#x0D; &#x0D; More specifically we will discuss the following aspects:&#x0D; &#x0D; &#x0D; Overall design of the study in five phases: recruitment, teams' analyses, analyses peer-review, meta-analysis and write-up.&#x0D; Preparation of a Registered Report and process of getting an In Principle Acceptance prior to the beginning of the first phase (recruitment).&#x0D; Preparation and management of a project-wide OSF repository using the R package osfr, and integration with GitHub.&#x0D; Logistics of recruitment and team creation with social media, Eventbrite and Google Sheets.&#x0D; Development of questionnaires to gather background information on the recruited analysts and to collect reports on the the teams' analyses.&#x0D; General project management with GitHub projects, issues and GitHub actions.&#x0D;

All-Photonic Architecture for Scalable Quantum Computing with Greenberger-Horne-Zeilinger States

Linear optical quantum computing is beset by the lack of deterministic entangling operations besides photon loss. Motivated by advancements at the experimental front in deterministic generation of various kinds of multiphoton entangled states, we propose an architecture for linear-optical quantum computing that harnesses the availability of three-photon Greenberger-Horne-Zeilinger (GHZ) states. Our architecture and its subvariants use polarized photons in GHZ states, polarization beam splitters, delay lines, optical switches and on-off detectors. We concatenate topological quantum error correction code with three-qubit repetition codes and estimate that our architecture can tolerate remarkably high photon-loss rate of $11.5 \%$; this makes a drastic change that is at least an order higher than those of known proposals. Further, considering three-photon GHZ states as resources, we estimate the resource overheads to perform gate operations with an accuracy of $10^{-6}~(10^{-15})$ to be $2.0\times10^6~(5.6\times10^7)$. Compared to other all-photonic schemes, our architecture is also resource-efficient. In addition, the resource overhead can be even further improved if larger GHZ states are available. Given its striking enhancement in the photon loss threshold and the recent progress in generating multiphoton entanglement, our scheme will make scalable photonic quantum computing a step closer to reality.

A design of the lightweight key management system for multi-level embedded devices

Abstract Embedded devices often face security risks such as device loss, cracking, key disclosure and side-channel attacks. Owing to the relative meagreness characterising the availability of computing as well as storage resources, it might be said that embedded devices themselves have limited resources, and traditional key management solutions are thus not fully suitable for embedded device usage scenarios. In addition, embedded device key management also necessitates consideration of the dynamic changes of devices, such as device joining and withdrawal, and blacklist management and control. A lightweight key management scheme is proposed for multi-layer embedded devices, and the management and control problems of the device blacklist library are analysed and an optimisation scheme is proposed. Finally, the scheme of this paper is analysed in detail based on the aspects of security and resource overhead. Compared with the traditional key management scheme, the scheme designed in this paper can be better applied to the communication of embedded devices to meet the security requirements of embedded systems.

Design and Implementation of a Reliable Reconfigurable Imaging System

Because of SRAM sensitivity to radiation, SRAM-based FPGA systems deployed in harsh environments require error mitigation methods to reduce their overall downtime. This paper presents a fault-tolerant reconfigurable imaging system that relies on the DPR feature for correcting errors in the configuration memory and loading camera system IPs. The system reliability is evaluated by injecting faults in the FPGA configuration memory at runtime using the Xilinx SEM IP. The faults are injected internally using the Internal Configuration Access Port (ICAP), which is shared between the fault injection core and system parts. The results showed that 95% of the errors can by corrected automatically. This paper also proposes a fast-Built-in-Self-Test (BIST) mitigation technique to reduce the overall downtime in case of errors. This technique can reduce the recovery time by 80%. Moreover, Triple Modular Redundancy (TMR) is used to increase the overall reliability without significantly increasing the resource overhead.

ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems

Due to the surge of cloud-assisted AI services, the problem of designing resilient prediction serving systems that can effectively cope with stragglers and minimize response delays has attracted much interest. The common approach for tackling this problem is replication which assigns the same prediction task to multiple workers. This approach, however, is inefficient and incurs significant resource overheads. Hence, a learning-based approach known as parity model (ParM) has been recently proposed which learns models that can generate ``parities’’ for a group of predictions to reconstruct the predictions of the slow/failed workers. While this learning-based approach is more resource-efficient than replication, it is tailored to the specific model hosted by the cloud and is particularly suitable for a small number of queries (typically less than four) and tolerating very few stragglers (mostly one). Moreover, ParM does not handle Byzantine adversarial workers. We propose a different approach, named Approximate Coded Inference (ApproxIFER), that does not require training any parity models, hence it is agnostic to the model hosted by the cloud and can be readily applied to different data domains and model architectures. Compared with earlier works, ApproxIFER can handle a general number of stragglers and scales significantly better with the number of queries. Furthermore, ApproxIFER is robust against Byzantine workers. Our extensive experiments on a large number of datasets and model architectures show significant degraded mode accuracy improvement by up to 58% over ParM.

HARS: A High-Available and Resource-Saving Service Function Chain Placement Approach in Data Center Networks

Network function virtualization (NFV) is a promising technology that decouples network functions from hardware. Connecting virtual network functions (VNFs) in series to form a service function chain (SFC) can flexibly orchestrate and expand network functions. However, there are higher availability requirements for SFCs. This paper aims to solve the SFC placement problem under availability and resource constraints. This paper proposes the sideway cross (SC) backup model, which considers the availability of both VNFs and physical machines (PMs) in a data center. The SC model cross-arranges the backup instances of VNFs to guarantee availability and optimize resource consumption. Then, this paper proposes the heuristic meteor shower optimization (MSO) algorithm to place SFCs. Compared to traditional heuristic algorithms, MSO can improve the execution time by approximately 200&#x0025;. Combined with the SC backup model, MSO can effectively improve the availability and resource overhead. The evaluation results show that the proposed approach can guarantee higher availability and consumes fewer resources. The proposed approach only needs 75&#x0025; of the resources to achieve the same availability as the state-of-the-art models.

A Novel Lightweight Anonymous Proxy Traffic Detection Method Based on Spatio-Temporal Features.

Anonymous proxies are used by criminals for illegal network activities due to their anonymity, such as data theft and cyber attacks. Therefore, anonymous proxy traffic detection is very essential for network security. In recent years, detection based on deep learning has become a hot research topic, since deep learning can automatically extract and select traffic features. To make (heterogeneous) network traffic adapt to the homogeneous input of typical deep learning algorithms, a major branch of existing studies convert network traffic into images for detection. However, such studies are commonly subject to the limitation of large-sized image representation of network traffic, resulting in very large storage and computational resource overhead. To address this limitation, a novel method for anonymous proxy traffic detection is proposed. The method is one of the solutions to reduce storage and computational resource overhead. Specifically, it converts the sequences of the size and inter-arrival time of the first N packets of a flow into images, and then categorizes the converted images using the one-dimensional convolutional neural network. Both proprietary and public datasets are used to validate the proposed method. The experimental results show that the converted images of the method are at least 90% smaller than that of existing image-based deep learning methods. With substantially smaller image sizes, the method can still achieve F1 scores up to 98.51% in Shadowsocks traffic detection and 99.8% in VPN traffic detection.

Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing

Benefitting from the outstanding capabilities of intelligent controlling and prediction, federated learning (FL) has been widely applied in Internet of Vehicle (IoV). However, applying FL into fog-computing-based IoV still suffers from two crucial problems: (i) how to achieve the privacy-preserving FL under the flexible architecture of fog computing with no assistance of cloud server, and (ii) how to guarantee the privacy-preserving FL to perform with high efficiency and low overhead in fog-computing settings. For addressing the above issues, we propose a practical framework, named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Galaxy</small> , the first of its kind in the regime of privacy-preserving FL under the setting of non-cloud-assisted fog computing. Based on the secure multi-party computation (MPC) technology, our framework satisfies the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{(T,N)}$</tex-math></inline-formula> -threshold property, permitting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{N}$</tex-math></inline-formula> (a scalable number) fog nodes to cooperate with multiple users for implementing privacy-preserving FL, while resisting the collusion up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{T}-\boldsymbol{1}$</tex-math></inline-formula> fog nodes, and being robust to at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{N}-\boldsymbol{T}$</tex-math></inline-formula> fog nodes simultaneously dropping out. Besides, considering the practical scenario that low-quality data may negatively impair the FL model convergence, our scheme can handle users’ low-quality data while protecting all user-related information under our secure framework. Based on the above superior properties, our scheme can perform with high scalability, high processing efficiency, and low resource overhead, being practical for fog-computing-based IoV. Extensive experiment results demonstrate our scheme with high-level performance.

CLASRM: A Lightweight and Secure Certificateless Aggregate Signature Scheme with Revocation Mechanism for 5G-Enabled Vehicular Networks

The rapid deployment of 5G technology has further strengthened the large-scale interconnection between sensing devices and systems and promoted the rapid development of smart cities and intelligent transportation systems. 5G-enabled vehicular networks take advantage of cellular vehicle-to-everything (C-V2X) technology to achieve the connection between moving vehicles, between vehicles and infrastructure, and between vehicles and the cloud, which can reduce the possibility of traffic jams and accidents, improve transportation efficiency, and realize automatic driving. Besides, 5G-enabled vehicular networks also provide infotainment services and industry application services. High-strength data transmission, however, will bring a serious burden of resource overhead, and there are hidden dangers of security and privacy in the communication process of vehicular networks. Some current vehicular network authentication schemes adopt public key infrastructure-based (PKI-based) and identity-based authentication methods to achieve conditional privacy preservation. Still, these schemes are too expensive and cannot address the problems of costly certificate management or risky key escrow. Some schemes use computationally complex bilinear pairing operations that result in low efficiency and do not consider the revocation of malicious nodes so that they cannot effectively prevent further malicious attacks. This paper proposes a lightweight certificateless aggregate signature (CLAS) scheme with a revocation mechanism suitable for 5G-enabled vehicular networks in response to the above problems. Our proposed scheme uses aggregation signature technology to aggregate multiple signatures into a single short signature, thus reducing communication overhead and storage overhead of road side units (RSUs). Furthermore, our proposed scheme utilizes the elliptic curve cryptography (ECC) to reduce verification time and computational overhead. Moreover, in order to prevent malicious users from sending invalid signatures to attack, our proposed scheme uses binary search to identify invalid signatures and introduces a cuckoo filter to revoke malicious users to prevent reattack. Finally, formal proof and experimental analysis show that our proposed scheme has greater advantages with respect to security and efficiency compared with the previous schemes.

Adaptive Real-Time Object Detection for Autonomous Driving Systems.

Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems (ADS). While detecting the obstacles on the road during various environmental circumstances add to the reliability of ADS, it results in more intensive computations and more complicated systems. The stringent real-time requirements of ADS, resource constraints, and energy efficiency considerations add to the design complications. This work presents an adaptive system that detects pedestrians and vehicles in different lighting conditions on the road. We take a hardware-software co-design approach on Zynq UltraScale+ MPSoC and develop a dynamically reconfigurable ADS that employs hardware accelerators for pedestrian and vehicle detection and adapts its detection method to the environment lighting conditions. The results show that the system maintains real-time performance and achieves adaptability with minimal resource overhead.

Computationally efficient zero-noise extrapolation for quantum-gate-error mitigation

Zero noise extrapolation (ZNE) is a widely used technique for gate error mitigation on near term quantum computers because it can be implemented in software and does not require knowledge of the quantum computer noise parameters. Traditional ZNE requires a significant resource overhead in terms of quantum operations. A recent proposal using a targeted (or random) instead of fixed identity insertion method (RIIM versus FIIM) requires significantly fewer quantum gates for the same formal precision. We start by showing that RIIM can allow for ZNE to be deployed on deeper circuits than FIIM, but requires many more measurements to maintain the same statistical uncertainty. We develop two extensions to FIIM and RIIM. The List Identity Insertion Method (LIIM) allows to mitigate the error from certain CNOT gates, typically those with the largest error. Set Identity Insertion Method (SIIM) naturally interpolates between the measurement-efficient FIIM and the gate-efficient RIIM, allowing to trade off fewer CNOT gates for more measurements. Finally, we investigate a way to boost the number of measurements, namely to run ZNE in parallel, utilizing as many quantum devices as are available. We explore the performance of RIIM in a parallel setting where there is a non-trivial spread in noise across sets of qubits within or across quantum computers.

Research on NTP Nonlinear Reflection Attack Identification Based on AHP Multidimensional Matrix in Global COVID-19 Environment

Under the influence of the global epidemic, various businesses have moved online one after another. With the rise of emerging industries such as online medical treatment, online education, and online conference, the proportion of attacks in the network service industry has increased year by year. UDP-FLOOD is still the primary scenario of DDoS attacks. Among them, with a large number of attack resources and most of them are high configuration servers, NTP (Network Time Protocol) reflection has become the most common UDP reflection attack method, accounting for 59% of the overall distribution. Therefore, establishing an efficient NTP attack detection system is a very important content to prevent network malicious attacks. At present, NTP-attacking based defensed methods mainly include IP filtering, hop mapping, and response packet detection, but they all have obvious weaknesses. Among them, the IP detection scheme can only detect historical attack IP, the implementation of hop mapping scheme is complex, and the resource overhead of response packet detection scheme is too large. Therefore, this paper proposes a nonlinear detection algorithm based on AHP multidimensional matrix quad information entropy. Through simulation experiments, the change of quad information entropy of attack intensity from 10% to 100% is counted. The detection rate based on the traditional target IP and target port algorithm is only 50% and 60%, which is significantly lower than this algorithm. Experiments show that the detection rate of this algorithm is higher.

A human vision based system for biometric images recognition

&lt;p&gt;&lt;span&gt;In this paper, a universal biometric system based on human vision is proposed. From recent biological and physiological results, A human identification system that approximates the natural vision and recognition of individuals is conceived. Liquid state machine (LSM), as a recurrent spiking neural network, is highly inspired by the brain neural architecture with low training cost. However, input dimension of large scale images requires efficient processing at the cost of performance or resource overhead. This paper propose a new neural input coding for images based on frequency signals rather than pixels. Each image is filtered and fragmented then the LSM liquid (or reservoir) will receive, first, high frequency signals, then low frequency signals from each fragment. The two sets of output neurons states corresponding to each type of filter will be matched to the entire enrollment database. A weighted sum rule between the matching results will determine the right class of a biometric image. The system was tested on three different biometric datasets: face, palmprint and off-line signature, results show the reliability of the proposed approach.&lt;/span&gt;&lt;/p&gt;

Interior Design Production Plan Simulation Modeling with Dynamics System: A Case Study of PT. Panutan Sejati

This research was to make interior design production planning more controlled and optimal. The simulation method was considered able to represent the dynamic form of the system and can find scenarios to solve the problems at PT Panutan Sejati. The qualitative research was at PT. Panutan Sejati models located at the Workshop: Jl. Kartini Health Center No. 113 Kalisari, East Jakarta from June to September 2021. The results showed that from the modeling designed based on existing conditions, in the design production planning at PT Panutan Sejati, the factors influencing production planning were: market demand and the production process. While the factors that affected profit were production costs, labor resource costs, overhead costs, commercial costs, and net revenue. It was necessary for improving the proposed system improvement to create and implement scenarios to fulfill effective and efficient production planning to get increased profits. The scenarios included adding workers and creating a design division. The scenario that gave optimal results in increasing profit was scenario 1. This scenario changed the parameter value of the workforce. Based on changes in labor parameters as a variable in the model, if the percentage of labor was set to 45%, then production costs would decrease every year. It was predicted to reach 21.40% and an increase in profit by 13%, with an average decrease in production costs until 2026.

Engineering fast bias-preserving gates on stabilized cat qubits

Stabilized cat codes can provide a biased noise channel with a set of bias-preserving (BP) gates, which can significantly reduce the resource overhead for fault-tolerant quantum computing. All existing schemes of BP gates, however, require adiabatic quantum evolution, with performance limited by excitation loss and nonadiabatic errors during the adiabatic gates. In this paper, we apply a derivative-based leakage-suppression technique to overcome nonadiabatic errors, so that we can implement fast BP gates on Kerr-cat qubits with improved gate fidelity while maintaining high noise bias. When applied to concatenated quantum error correction, the fast BP gates not only can improve the logical error rate but also can reduce resource overhead, which enables more efficient implementation of fault-tolerant quantum computing.

A new RFID ultra-lightweight authentication protocol for medical privacy protection in smart living

The widespread application of Radio Frequency Identification (RFID) technology in smart living, especially in smart healthcare, has greatly facilitated patient management, reduced the labor cost of medical services, and provided patients with better medical services. However, improper use of RFID tags is highly likely to threaten the safety and privacy of hospitals and even patients, which may not only cause physical and mental harm to patients but also damage the reputation of hospitals. Medical monitoring system based on RFID technology plays a significant role in guaranteeing the security of medical records as well as the privacy of patients. Nevertheless, due to the resource constraint of RFID tags/readers, it is also challenging to design an efficient and effective authentication protocol for RFID medical monitoring system. Aiming to reduce the resource overhead and meet security requirements of the RFID system, in this paper, we propose an ultra-lightweight RFID security authentication protocol utilizing a cloud server named CRUSAP, which is based on Bit-Crossing XOR rearrangement operations that can effectively resist forgery attacks, replay attacks, desynchronization attacks, and denial of service attacks while certifying the cloud server. The formal logic of BAN proves the safety and feasibility of the protocol. Additionally, the security analysis and experimental results show that the proposed authentication protocol can realize higher security goals at a lower cost and is more suitable for scaling to large-scale authentication.