A Quantum Algorithm for Computing All Diagnoses of a Switching Circuit

TL;DR

This paper introduces a quantum algorithm that leverages superposition to compute all possible diagnoses of switching circuits simultaneously, connecting probability theory with circuit diagnosis using quantum information.

Contribution

It presents a novel quantum computing approach for diagnosing switching circuits, enabling exponential parallelism in fault diagnosis.

Findings

Achieves less than 1% error in fault probability estimation on benchmarks

Demonstrates quantum advantage over classical SAT-based methods

Provides empirical comparison showing efficiency of the quantum approach

Abstract

Faults are stochastic by nature while most man-made systems, and especially computers, work deterministically. This necessitates the linking of probability theory with mathematical logics, automata, and switching circuit theory. This paper provides such a connecting via quantum information theory which is an intuitive approach as quantum physics obeys probability laws. In this paper we provide a novel approach for computing diagnosis of switching circuits with gate-based quantum computers. The approach is based on the idea of putting the qubits representing faults in superposition and compute all, often exponentially many, diagnoses simultaneously. We empirically compare the quantum algorithm for diagnostics to an approach based on SAT and model-counting. For a benchmark of combinational circuits we establish an error of less than one percent in estimating the true probability of faults.