Ali Rad Email and Phone Number

Graduate Researcher affiliated with QuICS

•In collaboration with the National Lab (NIST) and as a project in NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) I devised a novel quantum simulator platform for (2+1)-D native fermionic simulations using a dopant array, with a focus on Lattice Gauge Theories( LGTs). Authored an open-source Python and MATLAB repository and improved the Mean Field Hartree-Fock method's convergence rate by two orders of magnitude.

•Designed an algorithm for efficient quantum simulation, achieving a 50 percent reduction in ion trap qubits parameters. This was achieved through the use of Optimal Brain Damage pruning and Hessian Learning machine learning methods, implemented using Python and TensorFlow.•Designed a novel platform for analog quantum simulation with Python and enhanced convergence by two orders of magnitude•Developed quantum Bayesian learning algorithms and published 3 papers on overpramatization of quantum neural networks•Developed an open-source algorithm to accelerate hybrid classical-quantum computations on a trapped-ion quantum computer. Enhanced performance by reducing inquiries by 45 percent through the use of a global Bayesian learning algorithm to mitigate the barren plateau. Implementation was done using PyTorch, PennyLane, Quantum TensorFlow, and Qiskit.

Machine Learning Approach To Control the Quantum States In 2D Arrays of Quantum Dots:•Developed a Convolutional Neural Network model to optimize gate voltages for controlling quantum states in 2D quantum dot arrays.•Addressed the complex mapping between gate voltages and quantum states, enhancing the tuning process for quantum computing applications.•Achieved over 90% F1 score in state prediction accuracy, demonstrating significant advancements in quantum dot manipulation

Researched on the aspects of entanglement entropy in the gauge/gravity duality. Introduced the Quantum Null Energy Condition and the Curved Space‑time in Holographic Conformal Field TheoriesAs a graduate researcher, In collaboration with PI and postdocs of founded by this program did my thesis on:The Strong Subadditivity of Holographic Entanglement Entropy; From Boundary to Bulk

Software Developer and Researcher Intern at CMS Detector of Large Hadron Collider Analyzed large LHC data samples using statistical tools by R and ROOT in C++ and C for data processingWe have studied generic properties of neutral Pions in HGCal such as the Energy depositions, number of hits, and how collimated the showers are. If we compare the behavior of Neutral Pions with single photons we can find that the single photons are expected to produce collimated, high energy showers but in the other hand Neutral Pions tend to produce multiple-core showers of lower energy. The variables here explored have the potential to be used in the future to develop a particle id for HGCal.