### Machine Learning

- Studied the effects of noise on the performance of reservoir computing, a non-linear recurrent neural network. Presented findings on how noise influences the capacity of the system to learn a function. Find Details Here.
- Explored the possibility of utilising a quantum circuit as a kernel in quantum machine learning. This probe into the dynamics of n qubits offers a non-linear mapping for better understanding quantum machine learning. Find Details Here.
- Worked on optimizing a parameterized quantum circuit using a classical computer in the context of quantum machine learning. Devised strategies to make optimal use of the available sample space. Find Details Here.
- Demonstrated that physical, noisy analog reservoir computers have limited capabilities in computing deterministic functions, but indicated these can learn functions with outputs as random variables. Find Details Here.

### Asymptotic Characterization

- Delineates a "no free lunch" theorem for sensor performance across various frequencies, with the help of entanglement. Find Details Here.

### Control, Numerical Methods and Optimization

- Explored using convex optimization to design controls for a quantum computer with randomized errors, shifting from systematic errors. Demonstrated possibilities of using the objective function towards favoring sparsity and error maps conducive to Monte Carlo simulation. Find Details Here.
- Analyzed the feasibility of deformable mirrors in addressing single ions using analytical models and numerical solvers. This adds to the understanding of quantum computers' scaling problem. Find Details Here.

### Randomization and Statistical Analysis

- Developed a characterization technique for quantum computers, extending randomized benchmarking, which was a significant part of my Ph.D. thesis. Find Details Here.
- Further extended these results to estimate errors under a Markovian error model using non-Hermitian perturbation theory. Find Details Here.