npj Quantum Information volume 9, Article number: 67 (2023) Cite this article 6285 Accesses 2 Citations 43 Altmetric Metrics details Abstract In this work, we present a quantum circuit model for inferring gene regulatory networks (GRNs) from single-cell transcriptomic data. The model employs ...
Specifically, we investigate the impact of PEPSI device parameters on state transfer fidelity (see “Effects of device imperfections”), the rate-fidelity trade-off in a quantum network link (see “Quantum state transfer rate”), and extensions to a scalable photonic integrated circuit (PIC) ...
The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies. Fields covered include, but are not limited to, quantum computing and quantum communication, includ...
We design a quantum method for classical information compression that exploits the hidden subgroup quantum algorithm. We consider sequence data in a database with a priori unknown symmetries of the hidden subgroup type. We prove that data with a given gr
We reconstitute quantum circuit mapping using tools from quantum information theory, showing that a lower bound, which we dub the lightcone bound, emerges for a circuit executed on hardware. We also develop an initial placement algorithm based on graph similarity search, aiding us in optimally ...
npj Quantum Materials is an open access journal that publishes works that significantly advance the understanding of quantum materials, including their fundamental properties, fabrication and applications. The set of journals have been ranked according to their SJR and divided into four equal groups, fou...
A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models Article Open access 28 February 2024 Synergic quantum generative machine learning Article Open access 09 August 2023 The Born supremacy: quantum advantage and training of an Ising Born machi...
Variational quantum algorithms represent a powerful approach for solving optimization problems on noisy quantum computers, with a broad spectrum of potential applications ranging from chemistry to machine learning. However, their performances in practica
understanding its role and impact for couplings to the genuine quantum matter will be a first and decisive step in probing the interface between Quantum Theory and GR in a way guided by experiments, with possible far-reaching implications as regards possible reconciliations of the incompatible foundat...
Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine learning, with