Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Zlokapa A"'
Preparing thermal (Gibbs) states is a common task in physics and computer science. Recent algorithms mimic cooling via system-bath coupling, where the cost is determined by mixing time, akin to classical Metropolis-like algorithms. However, few metho
Externí odkaz:
http://arxiv.org/abs/2411.04300
Autor:
Hanin, Boris, Zlokapa, Alexander
We show at a physics level of rigor that Bayesian inference with a fully connected neural network and a shaped nonlinearity of the form $\phi(t) = t + \psi t^3/L$ is (perturbatively) solvable in the regime where the number of training datapoints $P$
Externí odkaz:
http://arxiv.org/abs/2405.16630
Autor:
Lykken, Joseph D., Jafferis, Daniel, Zlokapa, Alexander, Kolchmeyer, David K., Davis, Samantha I., Neven, Hartmut, Spiropulu, Maria
We extend the protocol of Gao and Jafferis arXiv:1911.07416 to allow wormhole teleportation between two entangled copies of the Sachdev-Ye-Kitaev (SYK) model communicating only through a classical channel. We demonstrate in finite $N$ simulations tha
Externí odkaz:
http://arxiv.org/abs/2405.07876
Autor:
Zlokapa, Alexander, Somma, Rolando D.
Publikováno v:
Quantum 8, 1449 (2024)
We consider the task of simulating time evolution under a Hamiltonian $H$ within its low-energy subspace. Assuming access to a block-encoding of $H'=(H-E)/\lambda$ for some $E \in \mathbb R$, the goal is to implement an $\epsilon$-approximation to $e
Externí odkaz:
http://arxiv.org/abs/2404.03644
Autor:
Vlimant Jean-Roch, Pantaleo Felice, Pierini Maurizio, Loncar Vladimir, Vallecorsa Sofia, Anderson Dustin, Nguyen Thong, Zlokapa Alexander
Publikováno v:
EPJ Web of Conferences, Vol 214, p 06025 (2019)
In recent years, several studies have demonstrated the benefit of using deep learning to solve typical tasks related to high energy physics data taking and analysis. In particular, generative adversarial networks are a good candidate to supplement th
Externí odkaz:
https://doaj.org/article/a4ff83142b184e2183fee002075f5bab
Autor:
Jafferis, Daniel, Zlokapa, Alexander, Lykken, Joseph D., Kolchmeyer, David K., Davis, Samantha I., Lauk, Nikolai, Neven, Hartmut, Spiropulu, Maria
We observe that the comment of [1, arXiv:2302.07897] is consistent with [2] on key points: i) the microscopic mechanism of the experimentally observed teleportation is size winding and ii) the system thermalizes and scrambles at the time of teleporta
Externí odkaz:
http://arxiv.org/abs/2303.15423
Autor:
Hanin, Boris, Zlokapa, Alexander
Characterizing how neural network depth, width, and dataset size jointly impact model quality is a central problem in deep learning theory. We give here a complete solution in the special case of linear networks with output dimension one trained usin
Externí odkaz:
http://arxiv.org/abs/2212.14457
Autor:
Zlokapa, Lara, Luo, Yiyue, Xu, Jie, Foshey, Michael, Wu, Kui, Agrawal, Pulkit, Matusik, Wojciech
Traditional robotic manipulator design methods require extensive, time-consuming, and manual trial and error to produce a viable design. During this process, engineers often spend their time redesigning or reshaping components as they discover better
Externí odkaz:
http://arxiv.org/abs/2204.07149
Autor:
Zlokapa, Alexander, Tan, Andrew K., Martyn, John M., Fiete, Ila R., Tegmark, Max, Chuang, Isaac L.
It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have bee
Externí odkaz:
http://arxiv.org/abs/2202.12887
Given the success of deep learning in classical machine learning, quantum algorithms for traditional neural network architectures may provide one of the most promising settings for quantum machine learning. Considering a fully-connected feedforward n
Externí odkaz:
http://arxiv.org/abs/2107.09200