K-sparse Pure State Tomography with Phase Estimation
Burhan Gulbahar

TL;DR
This paper introduces a polynomial-time quantum state tomography method for pure states with K-sparsity, utilizing phase estimation and specific measurement setups, reducing resource requirements compared to traditional exponential methods.
Contribution
It presents a novel quantum tomography algorithm for K-sparse pure states that operates efficiently without prior knowledge, leveraging phase estimation and specialized unitaries.
Findings
Achieves polynomial-time reconstruction of K-sparse pure states.
Requires fewer measurement settings independent of the number of qubits.
Provides practical optical and circuit implementations for the proposed method.
Abstract
Quantum state tomography (QST) for reconstructing pure states requires exponentially increasing resources and measurements with the number of qubits by using state-of-the-art quantum compressive sensing (CS) methods. In this article, QST reconstruction for any pure state composed of the superposition of different computational basis states of qubits in a specific measurement set-up, i.e., denoted as -sparse, is achieved without any initial knowledge and with quantum polynomial-time complexity of resources based on the assumption of the existence of polynomial size quantum circuits for implementing exponentially large powers of a specially designed unitary operator. The algorithm includes repetitions of conventional phase estimation algorithm depending on the probability of the least possible basis state in the…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Sparse and Compressive Sensing Techniques
