Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Hamilton, Kathleen E."'
Autor:
Islam, Md Mazharul, Alam, Shamiul, Udoy, Md Rahatul Islam, Hossain, Md Shafayat, Hamilton, Kathleen E, Aziz, Ahmedullah
Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such
Externí odkaz:
http://arxiv.org/abs/2408.01028
In machine learning, overparameterization is associated with qualitative changes in the empirical risk landscape, which can lead to more efficient training dynamics. For many parameterized models used in statistical learning, there exists a critical
Externí odkaz:
http://arxiv.org/abs/2307.03292
Autor:
Hamilton, Kathleen E., Laanait, Nouamane, Francis, Akhil, Economou, Sophia E., Barron, George S., Yeter-Aydeniz, Kübra, Morris, Titus, Cooley, Harrison, Kang, Muhun, Kemper, Alexander F., Pooser, Raphael
We introduce a volumetric benchmark for near-term quantum platforms based on the generation and verification of genuine entanglement across n-qubits using graph states and direct stabilizer measurements. Our benchmark evaluates the robustness of mult
Externí odkaz:
http://arxiv.org/abs/2209.00678
Autor:
Delgado, Andrea, Hamilton, Kathleen E., Date, Prasanna, Vlimant, Jean-Roch, Magano, Duarte, Omar, Yasser, Bargassa, Pedrame, Francis, Anthony, Gianelle, Alessio, Sestini, Lorenzo, Lucchesi, Donatella, Zuliani, Davide, Nicotra, Davide, de Vries, Jacco, Dibenedetto, Dominica, Martinez, Miriam Lucio, Rodrigues, Eduardo, Sierra, Carlos Vazquez, Vallecorsa, Sofia, Thaler, Jesse, Bravo-Prieto, Carlos, Chang, su Yeon, Lazar, Jeffrey, Argüelles, Carlos A., de Lejarza, Jorge J. Martinez
Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the
Externí odkaz:
http://arxiv.org/abs/2203.08805
Autor:
Delgado, Andrea, Hamilton, Kathleen E.
Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy
Externí odkaz:
http://arxiv.org/abs/2203.03578
Variational training of parameterized quantum circuits (PQCs) underpins many workflows employed on near-term noisy intermediate scale quantum (NISQ) devices. It is a hybrid quantum-classical approach that minimizes an associated cost function in orde
Externí odkaz:
http://arxiv.org/abs/2111.05311
Autor:
Hamilton, Kathleen E., Lynn, Emily, Leyton-Ortega, Vicente, Majumder, Swarnadeep, Pooser, Raphael C.
Quantum circuit Born machines (QCBMs) and training via variational quantum algorithms (VQAs) are key applications for near-term quantum hardware. QCBM ans\"atze designs are unique in that they do not require prior knowledge of a physical Hamiltonian.
Externí odkaz:
http://arxiv.org/abs/2111.05312
Autor:
Hamilton, Kathleen E., Kharazi, Tyler, Morris, Titus, McCaskey, Alexander J., Bennink, Ryan S., Pooser, Raphael C.
Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and substantially in
Externí odkaz:
http://arxiv.org/abs/2006.01805
Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent
Externí odkaz:
http://arxiv.org/abs/2004.08240
Application-inspired benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landsc
Externí odkaz:
http://arxiv.org/abs/1911.13289