JetSCI is a hybrid JAX-PETSc framework that delivers scalable differentiable finite element simulations and outperforms pure JAX implementations on heterogeneous micromechanics problems.
Boehmet al., Nature Reviews Physics 10.1038/s42254-021- 00417-4 (2022)
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Qudit encodings for EV trip assignments cut the Hilbert space dimension exponentially and match or exceed qubit-based QAOA performance on constrained uni- and bi-directional charging problems.
DistributedEstimator demonstrates that circuit cutting preserves test accuracy and robustness in QNN training on Iris and MNIST while revealing that classical reconstruction dominates runtime and exponential subcircuit growth limits scaling.
citing papers explorer
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JetSCI: A Hybrid JAX-PETSc Framework for Scalable Differentiable Simulation
JetSCI is a hybrid JAX-PETSc framework that delivers scalable differentiable finite element simulations and outperforms pure JAX implementations on heterogeneous micromechanics problems.
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Comparing Qubit and Qudit Encodings for EV Charging and Trip Assignment Problems
Qudit encodings for EV trip assignments cut the Hilbert space dimension exponentially and match or exceed qubit-based QAOA performance on constrained uni- and bi-directional charging problems.
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DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting
DistributedEstimator demonstrates that circuit cutting preserves test accuracy and robustness in QNN training on Iris and MNIST while revealing that classical reconstruction dominates runtime and exponential subcircuit growth limits scaling.
- Quantum in Biology, Quantum for Biology, and Biology for Quantum: Mapping the Evidence and the Road Ahead