Testing a Quantum Computer

The problem of quantum test is formally addressed. The presented method attempts the quantum role of classical test generation and test set reduction methods known from standard binary and analog circuits. QuFault, the authors software package generates test plans for arbitrary quantum circuits using the very efficient simulator QuIDDPro[1]. The quantum fault table is introduced and mathematically formalized, and the test generation method explained.Comment: 15 pages, 17 equations, 27 tables, 8 figure

Fault Models for Quantum Mechanical Switching Networks

The difference between faults and errors is that, unlike faults, errors can be corrected using control codes. In classical test and verification one develops a test set separating a correct circuit from a circuit containing any considered fault. Classical faults are modelled at the logical level by fault models that act on classical states. The stuck fault model, thought of as a lead connected to a power rail or to a ground, is most typically considered. A classical test set complete for the stuck fault model propagates both binary basis states, 0 and 1, through all nodes in a network and is known to detect many physical faults. A classical test set complete for the stuck fault model allows all circuit nodes to be completely tested and verifies the function of many gates. It is natural to ask if one may adapt any of the known classical methods to test quantum circuits. Of course, classical fault models do not capture all the logical failures found in quantum circuits. The first obstacle faced when using methods from classical test is developing a set of realistic quantum-logical fault models. Developing fault models to abstract the test problem away from the device level motivated our study. Several results are established. First, we describe typical modes of failure present in the physical design of quantum circuits. From this we develop fault models for quantum binary circuits that enable testing at the logical level. The application of these fault models is shown by adapting the classical test set generation technique known as constructing a fault table to generate quantum test sets. A test set developed using this method is shown to detect each of the considered faults.Comment: (almost) Forgotten rewrite from 200

Fault testing quantum switching circuits

Test pattern generation is an electronic design automation tool that attempts to find an input (or test) sequence that, when applied to a digital circuit, enables one to distinguish between the correct circuit behavior and the faulty behavior caused by particular faults. The effectiveness of this classical method is measured by the fault coverage achieved for the fault model and the number of generated vectors, which should be directly proportional to test application time. This work address the quantum process validation problem by considering the quantum mechanical adaptation of test pattern generation methods used to test classical circuits. We found that quantum mechanics allows one to execute multiple test vectors concurrently, making each gate realized in the process act on a complete set of characteristic states in space/time complexity that breaks classical testability lower bounds.Comment: (almost) Forgotten rewrite from 200

Deterministic and Probabilistic Test Generation for Binary and Ternary Quantum Circuits

It is believed that quantum computing will begin to have an impact around year 2010. Much work is done on physical realization and synthesis of quantum circuits, but nothing so far on the problem of generating tests and localization of faults for such circuits. Even fault models for quantum circuits have been not formulated yet. We propose an approach to test generation for a wide category of fault models of single and multiple faults. It uses deterministic and probabilistic tests to detect faults. A Fault Table is created that includes probabilistic information. If possible, deterministic tests are first selected, while covering faults with tests, in order to shorten the total length of the test sequence. The method is applicable to both binary and ternary quantum circuits. The system generates test sequences and adaptive trees for fault localization for small binary and ternary quantum circuits

Readiness of Quantum Optimization Machines for Industrial Applications

There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current classical optimization algorithms implemented on CMOS technologies. The benchmarking of these devices has been controversial. Initially, random spin-glass problems were used, however, these were quickly shown to be not well suited to detect any quantum speedup. Subsequently, benchmarking shifted to carefully crafted synthetic problems designed to highlight the quantum nature of the hardware while (often) ensuring that classical optimization techniques do not perform well on them. Even worse, to date a true sign of improved scaling with the number of problem variables remains elusive when compared to classical optimization techniques. Here, we analyze the readiness of quantum annealing machines for real-world application problems. These are typically not random and have an underlying structure that is hard to capture in synthetic benchmarks, thus posing unexpected challenges for optimization techniques, both classical and quantum alike. We present a comprehensive computational scaling analysis of fault diagnosis in digital circuits, considering architectures beyond D-wave quantum annealers. We find that the instances generated from real data in multiplier circuits are harder than other representative random spin-glass benchmarks with a comparable number of variables. Although our results show that transverse-field quantum annealing is outperformed by state-of-the-art classical optimization algorithms, these benchmark instances are hard and small in the size of the input, therefore representing the first industrial application ideally suited for testing near-term quantum annealers and other quantum algorithmic strategies for optimization problems.Comment: 22 pages, 12 figures. Content updated according to Phys. Rev. Applied versio

The Art of Fault Injection

Classical greek philosopher considered the foremost virtues to be temperance, justice, courage, and prudence. In this paper we relate these cardinal virtues to the correct methodological approaches that researchers should follow when setting up a fault injection experiment. With this work we try to understand where the "straightforward pathway" lies, in order to highlight those common methodological errors that deeply influence the coherency and the meaningfulness of fault injection experiments. Fault injection is like an art, where the success of the experiments depends on a very delicate balance between modeling, creativity, statistics, and patience

Mixed-level identification of fault redundancy in microprocessors

A new high-level implementation independent functional fault model for control faults in microprocessors is introduced. The fault model is based on the instruction set, and is specified as a set of data constraints to be satisfied by test data generation. We show that the high-level test, which satisfies these data constraints, will be sufficient to guarantee the detection of all non-redundant low level faults. The paper proposes a simple and fast simulation based method of generating test data, which satisfy the constraints prescribed by the proposed fault model, and a method of evaluating the high-level control fault coverage for the proposed fault model and for the given test. A method is presented for identification of the high-level redundant faults, and it is shown that a test, which provides 100% coverage of non-redundant high-level faults, will also guarantee 100% non-redundant SAF coverage, whereas all gate-level SAF not covered by the test are identified as redundant. Experimental results of test generation for the execution part of a microprocessor support the results presented in the paper.Comment: 2019 IEEE Latin American Test Symposium (LATS

Analysis and Test of the Effects of Single Event Upsets Affecting the Configuration Memory of SRAM-based FPGAs

SRAM-based FPGAs are increasingly relevant in a growing number of safety-critical application fields, ranging from automotive to aerospace. These application fields are characterized by a harsh radiation environment that can cause the occurrence of Single Event Upsets (SEUs) in digital devices. These faults have particularly adverse effects on SRAM-based FPGA systems because not only can they temporarily affect the behaviour of the system by changing the contents of flip-flops or memories, but they can also permanently change the functionality implemented by the system itself, by changing the content of the configuration memory. Designing safety-critical applications requires accurate methodologies to evaluate the system’s sensitivity to SEUs as early as possible during the design process. Moreover it is necessary to detect the occurrence of SEUs during the system life-time. To this purpose test patterns should be generated during the design process, and then applied to the inputs of the system during its operation. In this thesis we propose a set of software tools that could be used by designers of SRAM-based FPGA safety-critical applications to assess the sensitivity to SEUs of the system and to generate test patterns for in-service testing. The main feature of these tools is that they implement a model of SEUs affecting the configuration bits controlling the logic and routing resources of an FPGA device that has been demonstrated to be much more accurate than the classical stuck-at and open/short models, that are commonly used in the analysis of faults in digital devices. By keeping this accurate fault model into account, the proposed tools are more accurate than similar academic and commercial tools today available for the analysis of faults in digital circuits, that do not take into account the features of the FPGA technology.. In particular three tools have been designed and developed: (i) ASSESS: Accurate Simulator of SEuS affecting the configuration memory of SRAM-based FPGAs, a simulator of SEUs affecting the configuration memory of an SRAM-based FPGA system for the early assessment of the sensitivity to SEUs; (ii) UA2TPG: Untestability Analyzer and Automatic Test Pattern Generator for SEUs Affecting the Configuration Memory of SRAM-based FPGAs, a static analysis tool for the identification of the untestable SEUs and for the automatic generation of test patterns for in-service testing of the 100% of the testable SEUs; and (iii) GABES: Genetic Algorithm Based Environment for SEU Testing in SRAM-FPGAs, a Genetic Algorithm-based Environment for the generation of an optimized set of test patterns for in-service testing of SEUs. The proposed tools have been applied to some circuits from the ITC’99 benchmark. The results obtained from these experiments have been compared with results obtained by similar experiments in which we considered the stuck-at fault model, instead of the more accurate model for SEUs. From the comparison of these experiments we have been able to verify that the proposed software tools are actually more accurate than similar tools today available. In particular the comparison between results obtained using ASSESS with those obtained by fault injection has shown that the proposed fault simulator has an average error of 0:1% and a maximum error of 0:5%, while using a stuck-at fault simulator the average error with respect of the fault injection experiment has been 15:1% with a maximum error of 56:2%. Similarly the comparison between the results obtained using UA2TPG for the accurate SEU model, with the results obtained for stuck-at faults has shown an average difference of untestability of 7:9% with a maximum of 37:4%. Finally the comparison between fault coverages obtained by test patterns generated for the accurate model of SEUs and the fault coverages obtained by test pattern designed for stuck-at faults, shows that the former detect the 100% of the testable faults, while the latter reach an average fault coverage of 78:9%, with a minimum of 54% and a maximum of 93:16%