6 citations · 20 across the 8 of their papers we have counts for
8 papers
Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data
Gabriele Agliardi, Giorgio Cortiana, Anton Dekusar +6
Fidelity quantum kernels have shown promise in classification tasks, particularly when a group structure in the data can be identified and exploited through a covariant feature map…
A Machine Learning Approach to Boost the Vehicle-2-Grid Scheduling
Gabriele Agliardi, Giorgio Cortiana, Anton Dekusar +6
Electric Vehicles (EVs) are emerging as battery energy storage systems (BESSs) of increasing importance for different power grid services. However, the unique characteristics of EV…
A Survey of Classical And Quantum Sequence Models
I-Chi Chen, Harshdeep Singh, V L Anukruti +2
Our primary objective is to conduct a brief survey of various classical and quantum neural net sequence models, which includes self-attention and recurrent neural networks, with a…
Energy risk analysis with Dynamic Amplitude Estimation and Piecewise Approximate Quantum Compiling
Kumar J. B. Ghosh, Kavitha Yogaraj, Gabriele Agliardi +6
We generalize the Approximate Quantum Compiling algorithm into a new method for CNOT-depth reduction, which is apt to process wide target quantum circuits. Combining this method wi…
Conditions for a quadratic quantum speedup in nonlinear transforms with applications to energy contract pricing
Gabriele Agliardi, Corey O'Meara, Kavitha Yogaraj +8
Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis. For instance in the domain of energy economics, accurate and timely ri…
Gamma-hadron Separation in Imaging Atmospheric Cherenkov Telescopes using Quantum Classifiers
Jashwanth S, Sudeep Ghosh, Neha Shah +2
In this paper we have introduced a novel method for gamma hadron separation in Imaging Atmospheric Cherenkov Telescopes (IACT) using Quantum Machine Learning. IACTs captures images…
Study of Feature Importance for Quantum Machine Learning Models
Aaron Baughman, Kavitha Yogaraj, Raja Hebbar +3
Predictor importance is a crucial part of data preprocessing pipelines in classical and quantum machine learning (QML). This work presents the first study of its kind in which feat…
Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football Player Trades
Aaron Baughman, Daniel Bohm, Micah Forster +9
Even skilled fantasy football managers can be disappointed by their mid-season rosters as some players inevitably fall short of draft day expectations. Team managers can quickly di…