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pro vyhledávání: '"Guerreschi P"'
Databases are an essential component of modern computing infrastructures and allow efficient access to data stored persistently. Their structure depends on the type and relationships of the stored data elements and on the access pattern. Extending th
Externí odkaz:
http://arxiv.org/abs/2405.14947
Autor:
Schmitz, Albert T., Ibrahim, Mohannad, Sawaya, Nicolas P. D., Guerreschi, Gian Giacomo, Paykin, Jennifer, Wu, Xin-Chuan, Matsuura, A. Y.
The Pauli-based Circuit Optimization, Analysis and Synthesis Toolchain (PCOAST) was recently introduced as a framework for optimizing quantum circuits. It converts a quantum circuit to a Pauli-based graph representation and provides a set of optimiza
Externí odkaz:
http://arxiv.org/abs/2305.09843
Autor:
Moreira, M. S., Guerreschi, G. G., Vlothuizen, W., Marques, J. F., van Straten, J., Premaratne, S. P., Zou, X., Ali, H., Muthusubramanian, N., Zachariadis, C., van Someren, J., Beekman, M., Haider, N., Bruno, A., Almudever, C. G., Matsuura, A. Y., DiCarlo, L.
Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum circuits.
Externí odkaz:
http://arxiv.org/abs/2212.10742
Autor:
Khalate, Pradnya, Wu, Xin-Chuan, Premaratne, Shavindra, Hogaboam, Justin, Holmes, Adam, Schmitz, Albert, Guerreschi, Gian Giacomo, Zou, Xiang, Matsuura, A. Y.
Variational algorithms are a representative class of quantum computing workloads that combine quantum and classical computing. This paper presents an LLVM-based C++ compiler toolchain to efficiently execute variational hybrid quantum-classical algori
Externí odkaz:
http://arxiv.org/abs/2202.11142
Autor:
M. S. Moreira, G. G. Guerreschi, W. Vlothuizen, J. F. Marques, J. van Straten, S. P. Premaratne, X. Zou, H. Ali, N. Muthusubramanian, C. Zachariadis, J. van Someren, M. Beekman, N. Haider, A. Bruno, C. G. Almudever, A. Y. Matsuura, L. DiCarlo
Publikováno v:
npj Quantum Information, Vol 9, Iss 1, Pp 1-7 (2023)
Abstract Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum
Externí odkaz:
https://doaj.org/article/c6b733651ed3421f812c27499c272c26
Publikováno v:
J. R. Soc. Interface 19:196 (2022) 20220541
Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulate
Externí odkaz:
http://arxiv.org/abs/2112.00760
Quantum Error Correction (QEC) is essential for fault-tolerant quantum copmutation, and its implementation is a very sophisticated process involving both quantum and classical hardware. Formulating and verifying the decomposition of logical operation
Externí odkaz:
http://arxiv.org/abs/2111.13728
In this paper, we formally describe the three challenges of mapping surface code on superconducting devices, and present a comprehensive synthesis framework to overcome these challenges. The proposed framework consists of three optimizations. First,
Externí odkaz:
http://arxiv.org/abs/2111.13729
Publikováno v:
In Annales de chirurgie plastique esthétique November 2024 69(6):603-610
Autor:
Guerreschi, Gian Giacomo
Quadratic Unconstrained Binary Optimization (QUBO) is a broad class of optimization problems with many practical applications. To solve its hard instances in an exact way, known classical algorithms require exponential time and several approximate me
Externí odkaz:
http://arxiv.org/abs/2101.07813