Approximate Model-Based Diagnosis Using Greedy Stochastic Search

Feldman, Alexander; Provan, Gregory; Gemund, Arjan van

doi:10.1613/jair.3025

Cited by 35 publications

(24 citation statements)

References 35 publications

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“…As another realization of the fault detection application, the QuAIL team is examining combinational digital circuits [23], a more realistic scenario used to benchmark codes devoted to solving diagnostics related problems [21]. Preliminary results look very promising and harder than any other benchmarks reported in the literature and used to address the question of quantum speedup in quantum annealers.…”

Section: Qubo Formulationmentioning

confidence: 99%

A NASA perspective on quantum computing: Opportunities and challenges

et al. 2017

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In the last couple of decades, the world has seen several stunning instances of quantum algorithms that provably outperform the best classical algorithms.For most problems, however, it is currently unknown whether quantum algorithms can provide an advantage, and if so by how much, or how to design quantum algorithms that realize such advantages. Many of the most challenging computational problems arising in the practical world are tackled today by heuristic algorithms that have not been mathematically proven to outperform other approaches but have been shown to be effective empirically. While quantum heuristic algorithms have been proposed, empirical testing becomes possible only as quantum computation hardware is built. The next few years will be exciting as empirical testing of quantum heuristic algorithms becomes more and more feasible. While large-scale universal quantum computers are likely decades away, special-purpose quantum computational hardware has begun to emerge that will become more powerful over time, as well as some small-scale universal quantum computers. * Rupak Biswas

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Section: Qubo Formulationmentioning

confidence: 99%

A NASA perspective on quantum computing: Opportunities and challenges

et al. 2017

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“…Algorithm 2 is a generalized belief propagation (GBP) that uses the Boltzmann distribution of each region's corrected penalty model to re-estimate their collective corrective biases. If this algorithm converges, then one obtains a critical point of the regional Bethe approximation (13) constrained to give consistent marginals z (R) \zi b R (z (R) ) = b i (z i ), [30]. Like belief propagation, there is generally no guarantee of convergence and standard relaxation techniques, such as bounding messages away from 0 and 1, are required.…”

Section: Sum-product Belief Propagation Is Related To Critical Pointsmentioning

confidence: 99%

“…State-of-the-art performance for deterministic diagnosis is achieved by translating the problem into a SAT instance and using a SAT solver [33], but this approach has not been as thoroughly investigated in the strong fault model [44]. Greedy stochastic search produces excellent results in the weak fault model, but is less successful in the strong fault model [13].…”

Section: Application: Fault Diagnosismentioning

confidence: 99%

“…Unfortunately, samples taken from the D-Wave hardware do not conform to a Boltzmann distribution, owing to both noise and quantum mechanical effects. In contrast with greedy stochastic search [13], min-cardinality solutions will not generally be sampled with equal probability. In practice, Gibbs sampling [16] and other postprocessing techniques may be used to make a distribution of ground states more uniform.…”

Section: Generating Diverse Solutionsmentioning

confidence: 99%

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Mapping Constrained Optimization Problems to Quantum Annealing with Application to Fault Diagnosis

et al. 2016

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Current quantum annealing (QA) hardware suffers from practical limitations such as finite temperature, sparse connectivity, small qubit numbers, and control error. We propose new algorithms for mapping boolean constraint satisfaction problems (CSPs) onto QA hardware mitigating these limitations. In particular we develop a new embedding algorithm for mapping a CSP onto a hardware Ising model with a fixed sparse set of interactions, and propose two new decomposition algorithms for solving problems too large to map directly into hardware.The mapping technique is locally-structured, as hardware compatible Ising models are generated for each problem constraint, and variables appearing in different constraints are chained together using ferromagnetic couplings. In contrast, global embedding techniques generate a hardware independent Ising model for all the constraints, and then use a minor-embedding algorithm to generate a hardware compatible Ising model. We give an example of a class of CSPs for which the scaling performance of D-Wave's QA hardware using the local mapping technique is significantly better than global embedding.We validate the approach by applying D-Wave's hardware to circuit-based fault-diagnosis. For circuits that embed directly, we find that the hardware is typically able to find all solutions from a min-fault diagnosis set of size N using 1000N samples, using an annealing rate that is 25 times faster than a leading SAT-based sampling method. Further, we apply decomposition algorithms to find min-cardinality faults for circuits that are up to 5 times larger than can be solved directly on current hardware. arXiv:1603.03111v1 [quant-ph]

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“…[17,[23][24][25][26][27][28]), fault diagnosis has been one of the leading candidates to benchmark the performance of D-Wave devices as optimizers [26,29]. From the range of circuit model-based fault-diagnosis problems [30] we restrict our attention here to combinational circuit fault diagnosis (CCFD), which in contrast to sequential arXiv:1708.09780v2 [quant-ph] 2 Jul 2019 circuits, does not have any memory components and the output is entirely determined by the present inputs.…”

Section: Introductionmentioning

confidence: 99%

Readiness of Quantum Optimization Machines for Industrial Applications

et al. 2019

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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.

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Approximate Model-Based Diagnosis Using Greedy Stochastic Search

Cited by 35 publications

References 35 publications

A NASA perspective on quantum computing: Opportunities and challenges

A NASA perspective on quantum computing: Opportunities and challenges

Mapping Constrained Optimization Problems to Quantum Annealing with Application to Fault Diagnosis

Readiness of Quantum Optimization Machines for Industrial Applications

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