66 citations · 145 across the 19 of their papers we have counts for
5 papers · 2 filters
Privacy-preserving quantum federated learning via gradient hiding
Changhao Li, Niraj Kumar, Zhixin Song +2
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed…
Blind quantum machine learning with quantum bipartite correlator
Changhao Li, Boning Li, Omar Amer +11
Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quan…
Expressive variational quantum circuits provide inherent privacy in federated learning
Niraj Kumar, Jamie Heredge, Changhao Li +3
Federated learning has emerged as a viable distributed solution to train machine learning models without the actual need to share data with the central aggregator. However, standar…
Des-q: a quantum algorithm to provably speedup retraining of decision trees
Niraj Kumar, Romina Yalovetzky, Changhao Li +2
Decision trees are widely adopted machine learning models due to their simplicity and explainability. However, as training data size grows, standard methods become increasingly slo…
The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ansätze
Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti +5
Using tools from the representation theory of compact Lie groups, we formulate a theory of Barren Plateaus (BPs) for parameterized quantum circuits whose observables lie in their d…