10 citations · 14 across the 7 of their papers we have counts for
4 papers · 2 filters
Enhancing the reachability of variational quantum algorithms via input-state design
Shaojun Wu, Shan Jin, Abolfazl Bayat +1
Variational quantum algorithms (VQAs) face an inherent trade-off between expressivity and trainability: deeper circuits can represent richer states but suffer from noise accumulati…
Hybrid boson sampling-neural network architecture for enhanced classification
Mohammad Sharifian, Abolfazl Bayat
Demonstration of quantum advantage for classical machine learning tasks remains a central goal for quantum technologies and artificial intelligence. Two major bottlenecks to this g…
Quantum reservoir computing for predicting and characterizing chaotic maps
Qingyu Li, Chiranjib Mukhopadhyay, Ludovico Minati +1
Quantum reservoir computing has emerged as a promising paradigm for harnessing quantum systems to process temporal data efficiently by bypassing the costly training of gradient-bas…
Quantum Reservoir Computing for Realized Volatility Forecasting
Qingyu Li, Chiranjib Mukhopadhyay, Abolfazl Bayat +1
Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir…