Linear algebra checksums have been previously applied to high performance computing (HPC) workflows, leading to low-overhead detection and recovery from silent errors. In this work, we implement similar resilience strategies in a non-HPC setting, specifically targeting the extended Kalman filter for inertial navigation. These applications are often deployed in embedded systems operating in adversarial environments (e.g. in presence of ionizing radiations), leading to higher rates of silent errors compared to HPC. This work presents a checksum based implementation of resilient extended Kalman filter for inertial navigation that withstands emulated errors at rates as high as 5 bit-flips per 10,000 floating point operations, but at a small fraction (less than 15%) of the cost of double modular redundancy. We posit that, beyond Kalman filters, linear algebra checksum based fault tolerance is a viable path to silent error resilience in a variety of embedded applications.

The variational quantum Monte Carlo (VQMC) method received significant attention in the recent past because of its ability to overcome the curse of dimensionality inherent in many-body quantum systems. Close parallels exist between VQMC and the emerging hybrid quantum-classical computational paradigm of variational quantum algorithms. VQMC overcomes the curse of dimensionality by performing alternating steps of Monte Carlo sampling from a parametrized quantum state followed by gradient-based optimization. While VQMC has been applied to solve high-dimensional problems, it is known to be difficult to parallelize, primarily owing to the Markov Chain Monte Carlo (MCMC) sampling step. In this work, we explore the scalability of VQMC when autoregressive models, with exact sampling, are used in place of MCMC. This approach can exploit distributed-memory, shared-memory and/or GPU parallelism in the sampling task without any bottlenecks. In particular, we demonstrate GPU-scalability of VQMC for solving up to ten-thousand dimensional combinatorial optimization problems.