Abstract: Principal component analysis is commonly used for dimensionality reduction, feature extraction, denoising, and visualization. The most commonly used principal component analysis method is based upon optimization of the L2-norm; however, the L2-norm is known to exaggerate the contribution of errors and outliers. When optimizing over the L1-norm, the components generated are known to exhibit […]
The structured matrix completion problem (SMCP) is ubiquitous in several signal processing applications. In this article, we consider a fixed pattern, namely, the Hankel-structure for the SMCP under quantum formalism. By exploiting its structure, a lower-gate-complexity quantum circuit realization of a Hankel system is demonstrated. Further, we propose a quantum simulation algorithm for the Hankel-structured […]
We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of inference and training, for systems of up to four qubits when constrained to pure states. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state […]
Exact requirement of controlled NOT (CNOT) and single-qubit gates to implement a quantum algorithm in a given architecture is one of the central problems in this computational paradigm. In this article, we take a tutorial approach in explaining the preparation of Dicke states (|D k n 〉) using concise realizations of partially defined unitary transformations. We show how […]