Achieve Fault Tolerance Research Articles

On Blockchain: Design Principle, Building Blocks, Core Innovations, and Misconceptions

Blockchain has become one of the hottest research areas in recent years. The technology could potentially lead to a new generation of decentralized applications and decentralized autonomous organizations. Unfortunately, there is simply too much misinformation regarding blockchain. Most notably, blockchain has been used as a buzzword synonymous with data immutability and trust. In fact, this is far from the truth. In this article, we provide a concise description of exactly what blockchain technology is, including its design principle, building blocks, core innovations, and benefits. This is followed by an analysis of data immutability. We show that to create an insurmountable barrier against attacks on data immutability, decentralization and system scale are both necessary. Based on this analysis, we further dissect what benefits private and consortium blockchain could actually offer when decentralization is removed. We show that private and consortium blockchain cannot offer data immutability and trust as many works in the literature have claimed or implied. Instead, the centralized version of blockchain technology provides an elegant solution to achieving fault tolerance and atomic contract execution, which could make private and consortium blockchain useful for enterprises that would like to provide high availability to their customers and for their internal operations.

Achieving Fault Tolerance on Capped Color Codes with Few Ancillas

Attaining fault tolerance while maintaining low overhead is one of the main challenges in a practical implementation of quantum circuits. One major technique that can overcome this problem is the flag technique, in which high-weight errors arising from a few faults can be detected by a few ancillas and distinguished using subsequent syndrome measurements. The technique can be further improved using the fact that for some families of codes, errors of any weight are logically equivalent if they have the same syndrome and weight parity, as previously shown in [Phys. Rev. A 104, 042410 (2021)]. In this work, we develop a notion of distinguishable fault set which captures both concepts of flags and weight parities, and extend the use of weight parities in error correction from [Phys. Rev. A 104, 042410 (2021)] to families of capped and recursive capped color codes. We also develop fault-tolerant protocols for error correction, measurement, state preparation, and logical T gate implementation via code switching, which are sufficient for performing fault-tolerant Clifford computation on a capped color code, and performing fault-tolerant universal quantum computation on a recursive capped color code. Our protocols for a capped or a recursive capped color code of any distance require only 2 ancillas, assuming that the ancillas can be reused. The concept of distinguishable fault set also leads to a generalization of the definitions of fault-tolerant gadgets proposed by Aliferis, Gottesman, and Preskill.

ByteGraph

Most products at ByteDance, e.g., TikTok, Douyin, and Toutiao, naturally generate massive amounts of graph data. To efficiently store, query and update massive graph data is challenging for the broad range of products at ByteDance with various performance requirements. We categorize graph workloads at ByteDance into three types: online analytical, transaction, and serving processing, where each workload has its own characteristics. Existing graph databases have different performance bottlenecks in handling these workloads and none can efficiently handle the scale of graphs at ByteDance. We developed ByteGraph to process these graph workloads with high throughput, low latency and high scalability. There are several key designs in ByteGraph that make it efficient for processing our workloads, including edge-trees to store adjacency lists for high parallelism and low memory usage, adaptive optimizations on thread pools and indexes, and geographic replications to achieve fault tolerance and availability. ByteGraph has been in production use for several years and its performance has shown to be robust for processing a wide range of graph workloads at ByteDance.

Achieving fault tolerance against amplitude-damping noise

With the intense interest in small, noisy quantum computing devices comes the push for larger, more accurate -- and hence more useful -- quantum computers. While fully fault-tolerant quantum computers are, in principle, capable of achieving arbitrarily accurate calculations using devices subjected to general noise, they require immense resources far beyond our current reach. An intermediate step would be to construct quantum computers of limited accuracy enhanced by lower-level, and hence lower-cost, noise-removal techniques. This is the motivation for our work, which looks into fault-tolerant encoded quantum computation targeted at the dominant noise afflicting the quantum device. Specifically, we develop a protocol for fault-tolerant encoded quantum computing components in the presence of amplitude-damping noise, using a 4-qubit code and a recovery procedure tailored to such noise. We describe a universal set of fault-tolerant encoded gadgets and compute the pseudothreshold for the noise, below which our scheme leads to more accurate computation. Our work demonstrates the possibility of applying the ideas of quantum fault tolerance to targeted noise models, generalizing the recent pursuit of biased-noise fault tolerance beyond the usual Pauli noise models. We also illustrate how certain aspects of the standard fault tolerance intuition, largely acquired through Pauli-noise considerations, can fail in the face of more general noise.

Quantum logic with spin qubits crossing the surface code threshold

High-fidelity control of quantum bits is paramount for the reliable execution of quantum algorithms and for achieving fault tolerance—the ability to correct errors faster than they occur1. The central requirement for fault tolerance is expressed in terms of an error threshold. Whereas the actual threshold depends on many details, a common target is the approximately 1% error threshold of the well-known surface code2,3. Reaching two-qubit gate fidelities above 99% has been a long-standing major goal for semiconductor spin qubits. These qubits are promising for scaling, as they can leverage advanced semiconductor technology4. Here we report a spin-based quantum processor in silicon with single-qubit and two-qubit gate fidelities, all of which are above 99.5%, extracted from gate-set tomography. The average single-qubit gate fidelities remain above 99% when including crosstalk and idling errors on the neighbouring qubit. Using this high-fidelity gate set, we execute the demanding task of calculating molecular ground-state energies using a variational quantum eigensolver algorithm5. Having surpassed the 99% barrier for the two-qubit gate fidelity, semiconductor qubits are well positioned on the path to fault tolerance and to possible applications in the era of noisy intermediate-scale quantum devices.

Hierarchical fault-tolerant control for over-actuated hypersonic reentry vehicles

This paper proposes a performance-focused and deep learning-based hierarchical fault-tolerant control framework for over-actuated hypersonic reentry vehicles. The hierarchical fault tolerance mechanism refers to the fault tolerance that can be realized both in the control allocation and control layers. First, a long short-term memory neural network-based fault diagnosis unit is proposed to solve the difficult problem of diagnosing actuator fault information under multisource disturbances. Based on the diagnosed fault information, the robust least-squares control allocation method is deployed to distribute the desired control moment to the actuator and achieve fault tolerance in the control allocation layer. Then, combined with a fixed time extended state observer, an improved multiple-degree-of-freedom prescribed performance control method is used to compensate the lumped disturbances, including control allocation error, ensure the prescribed tracking performance, and realize fault tolerance in the control layer. Finally, the theoretical analysis proves the stability of the proposed hierarchical fault-tolerant control scheme, and the effectiveness is verified by numerical simulation.

Quantum Coding with Low-Depth Random Circuits

Random quantum circuits have played a central role in establishing the computational advantages of near-term quantum computers over their conventional counterparts. Here, we use ensembles of low-depth random circuits with local connectivity in $D\ge 1$ spatial dimensions to generate quantum error-correcting codes. For random stabilizer codes and the erasure channel, we find strong evidence that a depth $O(\log N)$ random circuit is necessary and sufficient to converge (with high probability) to zero failure probability for any finite amount below the optimal erasure threshold, set by the channel capacity, for any $D$. Previous results on random circuits have only shown that $O(N^{1/D})$ depth suffices or that $O(\log^3 N)$ depth suffices for all-to-all connectivity ($D \to \infty$). We then study the critical behavior of the erasure threshold in the so-called moderate deviation limit, where both the failure probability and the distance to the optimal threshold converge to zero with $N$. We find that the requisite depth scales like $O(\log N)$ only for dimensions $D \ge 2$, and that random circuits require $O(\sqrt{N})$ depth for $D=1$. Finally, we introduce an "expurgation" algorithm that uses quantum measurements to remove logical operators that cause the code to fail by turning them into additional stabilizers or gauge operators. With such targeted measurements, we can achieve sub-logarithmic depth in $D\ge 2$ below capacity without increasing the maximum weight of the check operators. We find that for any rate beneath the capacity, high-performing codes with thousands of logical qubits are achievable with depth 4-8 expurgated random circuits in $D=2$ dimensions. These results indicate that finite-rate quantum codes are practically relevant for near-term devices and may significantly reduce the resource requirements to achieve fault tolerance for near-term applications.

Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning.

The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses. Thus, it is essential to develop an online and real-time computer-aided diagnosis (CAD) approach to support physicians and avoid further spreading of the disease. In this research, a convolutional neural network (CNN) -based Residual neural network (ResNet50) has been employed to detect COVID-19 through chest X-ray images and achieved 98% accuracy. The proposed CAD system will receive the X-ray images from the remote hospitals/healthcare centers and perform diagnostic processes. Furthermore, the proposed CAD system uses advanced load balancer and resilience features to achieve fault tolerance with zero delays and perceives more infected cases during this pandemic.

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Bungie: Improving Fault Tolerance via Extensible Application-Level Protocols

We present Bungie, an approach based on applicationlevel protocols that precisely capture the causality inherent to the interactions among agents. We show through patterns and examples how Bungie provides abstractions for achieving fault tolerance.

Virtual Logical Qubits: A Compact Architecture for Fault-Tolerant Quantum Computing

Fault-tolerant quantum computing is required to execute many of the most promising quantum applications. In recent years, numerous error correcting codes, such as the surface code, have emerged which are well suited for current and future limited connectivity 2-D devices. We find quantum memory, particularly resonant cavities with transmon qubits arranged in a 2.5-D architecture, can efficiently implement surface codes with around 20× fewer transmons via this work. We virtualize 2-D memory addresses by storing the code in layers of qubit memories connected to each transmon. Distributing logical qubits across many memories has minimal impact on fault tolerance and results in substantially more efficient logical operations. Virtualized logical qubit (VLQ) systems can achieve fault tolerance comparable to conventional 2-D transmon-only architectures while putting within reach a proof-of-concept experimental demonstration of around ten logical qubits, requiring only 11 transmons and 9 attached cavities.

Efficient and Robust Certification of Genuine Multipartite Entanglement in Noisy Quantum Error Correction Circuits

Ensuring the correct functioning of quantum error correction (QEC) circuits is crucial to achieve fault tolerance in realistic quantum processors subjected to noise. The first checkpoint for a fully operational QEC circuit is to create genuine multipartite entanglement across all subsystems of physical qubits. We introduce a conditional witnessing technique to certify genuine multipartite entanglement (GME) that is efficient in the number of subsystems and, importantly, robust against experimental noise and imperfections. Specifically, we prove that the detection of entanglement in a linear number of bipartitions by a number of measurements that also scales linearly, suffices to certify GME. Moreover, our method goes beyond the standard procedure of separating the state from the convex hull of biseparable states, yielding an improved finesse and robustness compared to previous techniques. We apply our method to the noisy readout of stabilizer operators of the distance-three topological color code and its flag-based fault-tolerant version. In particular, we subject the circuits to combinations of three types of noise, namely, uniform depolarizing noise, two-qubit gate depolarizing noise, and bit-flip measurement noise. We numerically compare our method with the standard, yet generally inefficient, fidelity test and to a pair of efficient witnesses, verifying the increased robustness of our method. Last but not least, we provide the full translation of our analysis to a trapped-ion native gate set that makes it suitable for experimental applications.

Open‐circuit fault‐tolerant operation of permanent magnet synchronous generator drives for wind turbine systems using a computationally efficient model predictive current control

Model predictive fault-tolerant current control (MPFTCC) of permanent magnet synchronous generator (PMSG) drives can make a valuable contribution to improving the reliability and availability levels of wind turbines, because back-to-back (BTB) converters are prone to failure. However, MPFTCC suffers from excessive computational burden, because the BTB converter is treated as one system where all feasible voltage vectors (VVs) are used for prediction and evaluation. Accordingly, a computationally efficient MPFTCC algorithm for a PMSG drive is developed and proposed with the ability to handle insulated-gate bipolar transistor open-circuit faults. The candidate VVs of both machine- and grid-side converters are separately predicted and evaluated, which significantly reduces calculation effort. The proposed reconfigurable converter is a five-leg power converter with a common leg that connects the generator first phase to the grid three-phase, ensuring proper postfault reconfiguration of the grid-side inverter. Moreover, a three-switch rectifier is adopted to achieve fault tolerance of the PMSG-side rectifier. Performance of the considered MPFTCC strategies is evaluated by experimental means.

Fault-Tolerant Based Group Key Servers with Enhancement of Utilizing the Contributory Server for Cloud Storage Applications

Group key management in cloud computing has become a trending topic in recent decades. This paper deals with the implementation of clustered structure based on centralized key management. In a centralized key management scheme, the Key Server is responsible for generating user keys and group keys of the cloud. The proposed work consists of a single Central Server and a number of sub-servers and each sub-server can manage multiple different groups. To achieve fault tolerance, each group is replicated into multiple different sub-servers. Users are allocated to a specific group and the size of the group is limited. Each group has a separate group key to avoid internal attacks and they can communicate through a central server. The proposed work encourages intergroup communication and essentially reduces the computations and storage complexity of the central server. Also, we propose to remove the rekeying process upon a new user joining by maintaining a file access list for each file, which will reduce the impact of the security features that even there is a leakage of the group key after the user leaves. Further, this paper proposes a contributory key management scheme where each member of the group is responsible for generating the group key.

Fail-fast Is Failing... Fast!

For more than 40 years, fail-fast has been the dominant way of achieving fault tolerance. In this approach, some mechanism is responsible for ensuring that each component is up, functioning, and responding to work. As the industry moves to leverage cloud computing, this is getting more challenging. The way we create robust solutions is under pressure as the individual components don't fail fast but instead, starts running slow, which is far worse The slow component may be healthy enough to say, "I'm still here!" but slow enough to clog up all the work. This makes fail-fast schemes vulnerable.

Measurement-free preparation of grid states

Quantum computing potentially offers exponential speed-ups over classical computing for certain tasks. A central, outstanding challenge to making quantum computing practical is to achieve fault tolerance, meaning that computations of any length or size can be realized in the presence of noise. The Gottesman-Kitaev-Preskill code is a promising approach toward fault-tolerant quantum computing, encoding logical qubits into grid states of harmonic oscillators. However, for the code to be fault tolerant, the quality of the grid states has to be extremely high. Approximate grid states have recently been realized experimentally, but their quality is still insufficient for fault tolerance. Current implementable protocols for generating grid states rely on measurements of ancillary qubits combined with either postselection or feed forward. Implementing such measurements take up significant time during which the states decohere, thus limiting their quality. Here, we propose a measurement-free preparation protocol, which deterministically prepares arbitrary logical grid states with a rectangular or hexagonal lattice. The protocol can be readily implemented in trapped-ion or superconducting-circuit platforms to generate high-quality grid states using only a few interactions, even with the noise levels found in current systems.

Reliability-Aware Multipath Routing of Time-Triggered Traffic in Time-Sensitive Networks

With the development of industrial networks, the demands for strict timing requirements and high reliability in transmission become more essential, which promote the establishment of a Time-Sensitive Network (TSN). TSN is a set of standards with the intention of extending Ethernet for safety-critical and real-time applications. In general, frame replication is used to achieve fault-tolerance, while the increased load has a negative effect on the schedule synthesis phase. It is necessary to consider schedulability and reliability jointly. In this paper, a heuristic-based routing method is proposed to achieve fault tolerance by spatial redundancy for TSNs containing unreliable links. A cost function is presented to evaluate each routing set, and a heuristic algorithm is applied to find the solution with higher schedulability. Compared to the shortest path routing, our method can improve the reliability and the success rate of no-wait scheduling by 5–15% depending on the scale of topology.

CPU: Cross-Rack-Aware Pipelining Update for Erasure-Coded Storage

Erasure coding is widely used in distributed storage systems (DSSs) to efficiently achieve fault tolerance. However, when the original data need to be updated, erasure coding must update every encoded block, resulting in long update time and high bandwidth consumption. Exiting solutions are mainly focused on coding schemes to minimize the size of transmitted update information, while ignoring more efficient utilization of bandwidth among update racks. In this article, we propose a parallel Cross-rack Pipelining Update scheme ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU</i> ), which divides the update information into small-size units and transmits these units in parallel along with an update pipeline path among multiple racks. The performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU</i> is mainly determined by slice size and update path. More slices bring finer-grained parallel transmissions over cross-rack links, but also introduces more overheads. An update path that traverses all racks with large-bandwidth links provide short update time. We formulate the proposed pipelining update scheme as an optimization problem, based on a new theoretical pipelining update model. We prove the optimization problem is NP-hard and develop a heuristic algorithm to solve it based on the features of practical DSSs and our implementations, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Big chunk</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Small overhead</i> . Specifically, we determine the best update path first by solving a max-min problem and then decide the slice size. We further simplify the slice size selection by offline learning a range of interesting ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoI</i> ), in which all slice sizes provide similar performance. We implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU</i> and conduct experiments on Amazon EC2 under a variety of scenarios. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU</i> can reduce the average update time by 48.2 percent, compared with the state-of-the-art update schemes.

Fault-Tolerant Privacy-Preserving Data Aggregation for Smart Grid

In smart grids (SG), data aggregation is widely used to strike a balance between data usability and privacy protection. The fault tolerance is an important requirement to improve the robustness of data aggregation protocols, which enables normal execution of the protocols even with failures on some entities. However, to achieve fault tolerance, most schemes either sacrifice the aggregation accuracy due to the use of differential privacy or substitution strategy or need to rely on an online trusted entity to manage all user blinding factors. In this paper, a (k,n) threshold privacy-preserving data aggregation scheme named (k,n)-PDA is proposed, which reconciles data usability and data privacy through the BGN cryptosystem and achieves fault tolerance with accurate aggregation using Shamir’s secret sharing without any online trusted entity. Besides, our scheme supports the efficient changing of users’ membership. Specifically, the dynamic secrete key is distributed to n smart meters (SMs) through the threshold secret sharing algorithm. When k or more meters participate in the aggregation, the data service center (DSC) can reconstruct the key to compute the aggregate results, and less than k SMs cannot recover the key. Thus, our solution still works functionally even if up to n−k SMs fail; also, it resists attacks from the collusion of less than k SMs. Moreover, system and performance analyses demonstrate that our scheme achieves privacy, fault tolerance, and membership dynamics with high efficiency.

Real-time processing of stabilizer measurements in a bit-flip code

Although qubit coherence times and gate fidelities are continuously improving, logical encoding is essential to achieve fault tolerance in quantum computing. In most encoding schemes, correcting or tracking errors throughout the computation is necessary to implement a universal gate set without adding significant delays in the processor. Here, we realize a classical control architecture for the fast extraction of errors based on multiple cycles of stabilizer measurements and subsequent correction. We demonstrate its application on a minimal bit-flip code with five transmon qubits, showing that real-time decoding and correction based on multiple stabilizers is superior in both speed and fidelity to repeated correction based on individual cycles. Furthermore, the encoded qubit can be rapidly measured, thus enabling conditional operations that rely on feed forward, such as logical gates. This co-processing of classical and quantum information will be crucial in running a logical circuit at its full speed to outpace error accumulation.