Quantum generative modeling is a growing area of interest for industry-relevant
applications. This work systematically compares a broad range of techniques to guide …

Circuit design for quantum machine learning remains a formidable challenge. Inspired by
the applications of tensor networks across different fields and their novel presence in the …

A number of commercially available quantum computers, such as those based on trapped-
ion or superconducting qubits, can now perform mid-circuit measurements and resets. In …

We present the NVIDIA cuQuantum SDK [1], a state-of-the-art library of composable
primitives for GPU-accelerated quantum circuit simulations. As the size of quantum devices …

Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the
principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of …

Noisy intermediate-scale quantum (NISQ) devices are restricted by their limited number of
qubits and their short decoherence times. An approach addressing these problems is …

Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has
become a promising avenue for scaling up current quantum-computing experiments. Here …

Quantum computers present a compelling platform for the study of open quantum systems,
namely, the nonunitary dynamics of a system. Here, we investigate and report digital …

A restriction in the quality and quantity of available qubits presents a substantial obstacle to
the application of near-term and early fault-tolerant quantum computers in practical tasks. To …

Circuit cutting, the decomposition of a quantum circuit into independent partitions, has
become a promising avenue towards experiments with larger quantum circuits in the noisy …