Here's some stuff we've been up to at work lately: One of the main bottlenecks when using variational quantum algorithms to solve problems in quantum chemistry is that you need to call your quantum computer a lot of times.
A ton of fixes have been suggested in the past; we gather many of them in a single framework, show how to combine them and how to compare what comes out: https://arxiv.org/abs/2603.20029
And here's an animation that illustrates what the problem is about: https://kvantify-assets.s3.eu-west-1.amazonaws.com/anim-arxiv-2603-20029.mp4
Variance reduction methods in the estimation of Pauli sums
Accurately estimating expectation values of quantum observables with as few measurements as possible is crucial to many quantum computing applications. We introduce a framework that covers many of existing measurement strategies and introduce heuristics that can be used to enhance randomized schemes, including those based on Pauli grouping with inverse probability weighting and variants of the classical shadow algorithm. We show how to maximize information gain from such schemes, while carefully optimizing the distribution of possible measurements, and show that simple grouping algorithms can get close to, and in some cases exceed, state-of-the-art accuracy for unbiased estimation of expectation values on a standard quantum chemistry benchmark. We show how these randomized methods may be compared to more recent measurement schemes, such as shadow grouping, derandomized shadow, and overlapped grouping measurement, we show how the same strategies can be used to augment these schemes, and we demonstrate that we can reduce measurement costs by up to a factor of two by allowing Clifford measurement circuits for otherwise Clifford-less methods.
Craig S. Kaplan