Grover on KATAN: Quantum Resource Estimation

This article presents the cost analysis of mounting Grover’s key search attack on the family of KATAN block cipher. Several designs of the reversible quantum circuit of KATAN are proposed. Owing to the National Insitute of Standards and Technology’s (NIST) proposal for postquantum cryptography standardization, the circuits are designed focusing on minimizing the overall depth. […]

Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification

In this work, we aim to solve a practical use-case of unsupervised clustering that has applications in predictive maintenance in the energy operations sector using quantum computers. Using only cloud access to quantum computers, we complete thorough performance analysis of what some current quantum computing systems are capable of for practical applications involving nontrivial mid-to-high-dimensional […]

Machine-Learning-Based Parameter Estimation of Gaussian Quantum States

In this article, we propose a machine-learning framework for parameter estimation of single-mode Gaussian quantum states. Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data. For phase-space displacement and squeezing parameter estimation, this is achieved by introducing expectation–maximization (EM)-based algorithms, while for phase parameter estimation, an empirical Bayes method […]

Simultaneous Estimation of Parameters and the State of an Optical Parametric Oscillator System

In this article, we consider the filtering problem of an optical parametric oscillator (OPO). The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously. We formulate this problem using a state-space representation of the […]

Simultaneous Estimation of Parameters and the State of an Optical Parametric Oscillator System

In this article, we consider the filtering problem of an optical parametric oscillator (OPO). The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously. We formulate this problem using a state-space representation of the […]

Machine-Learning-Based Parameter Estimation of Gaussian Quantum States

In this article, we propose a machine-learning framework for parameter estimation of single-mode Gaussian quantum states. Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data. For phase-space displacement and squeezing parameter estimation, this is achieved by introducing expectation–maximization (EM)-based algorithms, while for phase parameter estimation, an empirical Bayes method […]