Zobrazeno 1 - 10
of 7 485
pro vyhledávání: '"Martin, T P"'
Autor:
Bedalov, Matt. J., Blakely, Matt, Buttler, Peter. D., Carnahan, Caitlin, Chong, Frederic T., Chung, Woo Chang, Cole, Dan C., Goiporia, Palash, Gokhale, Pranav, Heim, Bettina, Hickman, Garrett T., Jones, Eric B., Jones, Ryan A., Khalate, Pradnya, Kim, Jin-Sung, Kuper, Kevin W., Lichtman, Martin T., Lee, Stephanie, Mason, David, Neff-Mallon, Nathan A., Noel, Thomas W., Omole, Victory, Radnaev, Alexander G., Rines, Rich, Saffman, Mark, Shabtai, Efrat, Teo, Mariesa H., Thotakura, Bharath, Tomesh, Teague, Tucker, Angela K.
We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell states (12x error reduction), random circuits (15x), and a p
Externí odkaz:
http://arxiv.org/abs/2412.07670
Autor:
Barlow, Martin T.
We consider models for inference which involve observers which may have multiple copies, such as in the Sleeping Beauty problem. We establish a framework for describing these problems on a probability space satisfying Kolmogorov's axioms, and this en
Externí odkaz:
http://arxiv.org/abs/2411.13257
Autor:
Brolly, Martin T.
Stochastic parameterisations deployed in models of the Earth system frequently invoke locality assumptions such as Markovianity or spatial locality. This work highlights the impact of such assumptions on predictive performance. Both in terms of short
Externí odkaz:
http://arxiv.org/abs/2411.07041
Large generative models (LMs) are increasingly being considered for high-stakes decision-making. This work considers how such models compare to humans and predictive AI models on a specific case of recidivism prediction. We combine three datasets --
Externí odkaz:
http://arxiv.org/abs/2410.15471
Autor:
Candelori, Luca, Abanov, Alexander G., Berger, Jeffrey, Hogan, Cameron J., Kirakosyan, Vahagn, Musaelian, Kharen, Samson, Ryan, Smith, James E. T., Villani, Dario, Wells, Martin T., Xu, Mengjia
We propose a new data representation method based on Quantum Cognition Machine Learning and apply it to manifold learning, specifically to the estimation of intrinsic dimension of data sets. The idea is to learn a representation of each data point as
Externí odkaz:
http://arxiv.org/abs/2409.12805
Autor:
Ramdas, Tejas, Wells, Martin T.
In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effectiveness of our optimized neural network predictor in accurately predi
Externí odkaz:
http://arxiv.org/abs/2409.05192
In this article, we develop nonparametric inference methods for comparing survival data across two samples, which are beneficial for clinical trials of novel cancer therapies where long-term survival is a critical outcome. These therapies, including
Externí odkaz:
http://arxiv.org/abs/2409.02209
Mediation analyses allow researchers to quantify the effect of an exposure variable on an outcome variable through a mediator variable. If a binary mediator variable is misclassified, the resulting analysis can be severely biased. Misclassification i
Externí odkaz:
http://arxiv.org/abs/2407.06970
Autor:
Ronetti, F., Bertin-Johannet, B., Popoff, A., Rech, J., Jonckheere, T., Grémaud, B., Raymond, L., Martin, T.
Publikováno v:
F. Ronetti, B. Bertin-Johannet, A. Popoff, J. Rech, T. Jonckheere, B. Gr\'emaud, L. Raymond, T. Martin, Chaos 34, 042103 (2024)
in nanoscale systems in the presence of single-electron excitations generated by Lorentzian voltage drives, termed \textit{Levitons}. These excitations allow to realize the analog of quantum optics experiments using electrons instead of photons. Impo
Externí odkaz:
http://arxiv.org/abs/2405.06392
Publikováno v:
Phys. Rev. Lett. 132, 216601 (2024)
Anyons are particles intermediate between fermions and bosons, characterized by a nontrivial exchange phase, yielding remarkable braiding statistics. Recent experiments have shown that anyonic braiding has observable consequences on edge transport in
Externí odkaz:
http://arxiv.org/abs/2311.15094