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pro vyhledávání: '"Pelofske, Elijah"'
Generative Pre-Trained Transformer models have been shown to be surprisingly effective at a variety of natural language processing tasks -- including generating computer code. We evaluate the effectiveness of open source GPT models for the task of au
Externí odkaz:
http://arxiv.org/abs/2408.00197
We consider the problem of computing a sparse binary representation of an image. To be precise, given an image and an overcomplete, non-orthonormal basis, we aim to find a sparse binary vector indicating the minimal set of basis vectors that when add
Externí odkaz:
http://arxiv.org/abs/2405.20525
Autor:
Pelofske, Elijah
RSA is an incredibly successful and useful asymmetric encryption algorithm. One of the types of implementation flaws in RSA is low entropy of the key generation, specifically the prime number creation stage. This can occur due to flawed usage of rand
Externí odkaz:
http://arxiv.org/abs/2405.03166
Generative pre-trained transformers (GPT's) are a type of large language machine learning model that are unusually adept at producing novel, and coherent, natural language. In this study the ability of GPT models to generate novel and correct version
Externí odkaz:
http://arxiv.org/abs/2404.15681
The task of accurate and efficient language translation is an extremely important information processing task. Machine learning enabled and automated translation that is accurate and fast is often a large topic of interest in the machine learning and
Externí odkaz:
http://arxiv.org/abs/2404.14680
We introduce JuliQAOA, a simulation package specifically built for the Quantum Alternating Operator Ansatz (QAOA). JuliQAOA does not require a circuit-level description of QAOA problems, or another package to simulate such circuits, instead relying o
Externí odkaz:
http://arxiv.org/abs/2312.06451
We show through numerical simulation that the Quantum Alternating Operator Ansatz (QAOA) for higher-order, random-coefficient, heavy-hex compatible spin glass Ising models has strong parameter concentration across problem sizes from $16$ up to $127$
Externí odkaz:
http://arxiv.org/abs/2312.00997
Autor:
Barron, Samantha V., Egger, Daniel J., Pelofske, Elijah, Bärtschi, Andreas, Eidenbenz, Stephan, Lehmkuehler, Matthis, Woerner, Stefan
In this paper, we explore the impact of noise on quantum computing, particularly focusing on the challenges when sampling bit strings from noisy quantum computers as well as the implications for optimization and machine learning applications. We form
Externí odkaz:
http://arxiv.org/abs/2312.00733
Recently, a Hamiltonian dynamics simulation was performed on a kicked ferromagnetic 2D transverse field Ising model with a connectivity graph native to the 127 qubit heavy-hex IBM Quantum architecture using ZNE quantum error mitigation. We demonstrat
Externí odkaz:
http://arxiv.org/abs/2311.01657
Autor:
Pelofske, Elijah
We consider the hypothetical quantum network case where Alice wishes to transmit one qubit of information (specifically a pure quantum state) to $M$ parties, where $M$ is some large number. The remote receivers locally perform single qubit quantum st
Externí odkaz:
http://arxiv.org/abs/2310.04920