27 citations · 50 across the 6 of their papers we have counts for
6 papers
End-to-end Optimization of Single-Shot Quantum Machine Learning for Bayesian Inference
Theodoros Ilias, Fangjun Hu, Marti Vives +1
We introduce an end-to-end optimization strategy for quantum machine learning that directly targets performance under finite measurement resources, where learning objectives are de…
Scaling Laws of Quantum Information Lifetime in Monitored Quantum Dynamics
Bingzhi Zhang, Fangjun Hu, Runzhe Mo +3
Quantum information is typically fragile under measurements and environmental coupling. Remarkably, we find that its lifetime can scale exponentially with system size when the envi…
A neural processing approach to quantum state discrimination
Saeed A. Khan, Fangjun Hu, Gerasimos Angelatos +2
Although linear quantum amplification has proven essential to the processing of weak quantum signals, extracting higher-order quantum features such as correlations in principle dem…
Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data
Fangjun Hu, Saeed A. Khan, Nicholas T. Bronn +4
Practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a mac…
Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks
Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan +6
The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems acr…
Quantifying the Expressive Capacity of Quantum Systems: Fundamental Limits and Eigentasks
Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan +6
The expressive capacity of quantum systems for machine learning is limited by quantum sampling noise incurred during measurement. Although it is generally believed that noise limit…