86 citations · 130 across the 9 of their papers we have counts for
9 papers
Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits
Onur Danaci, Yash J. Patel, Riccardo Molteni +3
Learning problems involving quantum data are natural candidates for demonstrating an advantage in quantum machine learning. Recent results indicate that, for certain tasks and unde…
Curriculum reinforcement learning for quantum architecture search under hardware errors
Yash J. Patel, Akash Kundu, Mateusz Ostaszewski +3
The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational quantum algorithms (VQAs) offer a p…
Enhancing variational quantum state diagonalization using reinforcement learning techniques
Akash Kundu, Przemysław Bedełek, Mateusz Ostaszewski +4
The variational quantum algorithms are crucial for the application of NISQ computers. Such algorithms require short quantum circuits, which are more amenable to implementation on n…
ManQala: Game-Inspired Strategies for Quantum State Engineering
Onur Danaci, Wenlei Zhang, Robert Coleman +6
The ability to prepare systems in specific target states through quantum engineering is essential for realizing the new technologies promised by a second quantum revolution. Here,…
Machine learning pipeline for quantum state estimation with incomplete measurements
Onur Danaci, Sanjaya Lohani, Brian T. Kirby +1
Two-qubit systems typically employ 36 projective measurements for high-fidelity tomographic estimation. The overcomplete nature of the 36 measurements suggests possible robustness…
Machine learning assisted quantum state estimation
Sanjaya Lohani, Brian T. Kirby, Michael Brodsky +2
We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For…