MQT QMAP: Efficient Quantum Circuit Mapping

Autor: Wille, Robert, Burgholzer, Lukas

Quantum computing is an emerging technology that has the potential to revolutionize fields such as cryptography, machine learning, optimization, and quantum simulation. However, a major challenge in the realization of quantum algorithms on actual mac

Externí odkaz: http://arxiv.org/abs/2301.11935

qMAP enabled microanatomical mapping of human skin aging.

Autor: Han KS; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD., Sander IB; Department of Dermatology, Johns Hopkins University, Baltimore, MD., Kumer J; Department of Illustration Practice, Maryland Institute College of Art, Baltimore, MD., Resnick E; Department of Medicine, University of Maryland School of Medicine, Baltimore, MD., Booth C; Center for Cancer Research, National Cancer Institute, Frederick, MD., Cheng G; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD., Im Y; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD., Starich B; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD., Kiemen AL; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD., Phillip JM; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD.; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD., Reddy S; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Department of Plastic and Reconstructive Surgery, Johns Hopkins School of Medicine, Baltimore, MD., Joshu CE; Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD., Sunshine JC; Department of Dermatology, Johns Hopkins University, Baltimore, MD., Walston JD; Department of Medicine, Division of Geriatrics and Gerontology, Johns Hopkins School of Medicine, Baltimore, MD., Wirtz D; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.; Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD., Wu PH; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD.; The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD.

Publikováno v: BioRxiv : the preprint server for biology [bioRxiv] 2024 Jul 06. Date of Electronic Publication: 2024 Jul 06.

Bayesian network parameter learning algorithm based on improved QMAP

Autor: Di Ruohai, Li Ye, Wan Kaifang, Lyu Zhigang, Wang Peng

Publikováno v: Xibei Gongye Daxue Xuebao, Vol 39, Iss 6, Pp 1356-1367 (2021)

Small data sets make the statistical information in Bayesian network parameter learning inaccurate, which makes it difficult to get accurate Bayesian network parameters based on data. Qualitative maximum a posteriori estimation (QMAP) is the most acc

Externí odkaz: https://doaj.org/article/dddca6f9c3c44529a5fc579b7840cccf

Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq

Autor: Sonia Brockway, Geng Wang, Jasen M. Jackson, David R. Amici, Seesha R. Takagishi, Matthew R. Clutter, Elizabeth T. Bartom, Marc L. Mendillo

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-12 (2020)

Identifying chemical-genetic interactions in mammalian cells is limited to low-throughput or computational methods. Here, the authors present QMAP-Seq, a broadly accessible and scalable approach that uses NGS for pooled high-throughput chemical-genet

Externí odkaz: https://doaj.org/article/0bffba70303d49d6bfb6d4bae9916ae3

Comparing and combining the Saskatoon, QMAP and COBE CMB maps

Autor: Xu, Yongzhong, Tegmark, Max, de Oliveira-Costa, Angelica, Devlin, Mark J., Herbig, Thomas, Miller, Amber D., Netterfield, C. Barth, Page, Lyman

Publikováno v: Phys.Rev.D63:103002,2001

We present a method for comparing and combining maps with different resolutions and beam shapes, and apply it to the Saskatoon, QMAP and COBE/DMR data sets. Although the Saskatoon and QMAP maps detect signal at the 21 sigma and 40 sigma levels, respe

Externí odkaz: http://arxiv.org/abs/astro-ph/0010552