Quantum state Archives - Page 2 of 3 - IEEE Transactions on Quantum Engineering

Efficient Quantum Network Communication Using Optimized Entanglement Swapping Trees

November 2, 2022Mohammad GhaderibanehOpen Access, Quantum InternetDegradation, Photonics, Quantum entanglement, Quantum networks, Quantum state, Qubit, Teleportation

Quantum network communication is challenging, as the no-cloning theorem in the quantum regime makes many classical techniques inapplicable; in particular, the direct transmission of qubit states over long distances is infeasible due to unrecoverable errors. For the long-distance communication of unknown quantum states, the only viable communication approach (assuming local operations and classical communications) is […]

Model-Predictive Quantum Control via Hamiltonian Learning

November 2, 2022MAISON CLOUÂTRÉOpen Access, Quantum InternetComputational modeling, Control systems, Optimization, Quantum state, Quantum system, Qubit, Tomography

This article proposes an end-to-end framework for the learning-enabled control of closed quantum systems. The proposed learning technique is the first of its kind to utilize a hierarchical design, which layers probing control, quantum state tomography, quantum process tomography, and Hamiltonian learning to identify both the internal and control Hamiltonians. Within this context, a novel […]

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

November 2, 2022Qi YuEnabling Technologies, Open AccessEstimation, Filtering, Filtering theory, Kalman filters, Quantum computing, Quantum state, Quantum 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 […]

A High-Resolution Single-Photon Arrival-Time Measurement With Self-Antithetic Variance Reduction in Quantum Applications: Theoretical Analysis and Performance Estimation

November 2, 2022Tony WuEnabling Technologies, Open AccessClocks, Generators, Photonics, Quantum state, Random variables, Single-photon avalanche diodes, Voltage control

An almost all-digital time-to-digital converter (TDC) possessing subpicosecond resolutions, scalable dynamic ranges, high linearity, high noise immunity, and moderate conversion rates can be achieved by a random sampling-and-averaging (RSA) approach with the self-antithetic variance reduction (SAVR) technique for time-correlated single-photon counting (TCSPC) quantum measurements. This article presents detailed theoretical analysis and behavior-model verifications of the […]

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

June 23, 2022Qi Yu; Shota Yokoyama; Daoyi Dong; David McManus; Hidehiro YonezawaOpen Access, Quantum InformationEstimation, Filtering, Filtering theory, Kalman filters, Quantum computing, Quantum state, Quantum system

Efficient Quantum Network Communication Using Optimized Entanglement Swapping Trees

April 19, 2022Mohammad Ghaderibaneh; Caitao Zhan; Himanshu Gupta; C. R. RamakrishnanOpen Access, Quantum InformationDegradation, Photonics, Quantum entanglement, Quantum networks, Quantum state, Qubit, Teleportation

Machine-Learning-Based Parameter Estimation of Gaussian Quantum States

December 23, 2021Neel Kanth Kundu; Matthew R. McKay; Ranjan K. MallikOpen Access, Quantum InformationBayes methods, Estimation, Machine learning, Metrology, Parameter estimation, Phase estimation, Quantum state

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 […]

New Single-Preparation Methods for Unsupervised Quantum Machine Learning Problems

October 21, 2021Yannick Deville; Alain DevilleOpen Access, Quantum InformationBlind source separation, Machine learning, Parameter estimation, Quantum computing, Quantum state, Random variables, Tomography

The term “machine learning” especially refers to algorithms that derive mappings, i.e., input–output transforms, by using numerical data that provide information about considered transforms. These transforms appear in many problems related to classification/clustering, regression, system identification, system inversion, and input signal restoration/separation. We here analyze the connections between all these problems in the classical and […]

On the Experimental Feasibility of Quantum State Reconstruction via Machine Learning

August 24, 2021Sanjaya Lohani; Thomas A. Searles; Brian T. Kirby; Ryan T. GlasserOpen Access, Quantum InformationMachine learning, Matrix decomposition, Maximum likelihood estimation, Noise measurement, Quantum state, Qubit, Training

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 […]

Efficient Discrete Feature Encoding for Variational Quantum Classifier

August 10, 2021Hiroshi Yano; Yudai Suzuki; Kohei M. Itoh; Rudy Raymond; Naoki YamamotoOpen Access, Quantum InformationEncoding, Machine learning, Quantum computing, Quantum mechanics, Quantum state, Qubit, Task analysis

Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational quantum classification (VQC) is one of such methods with possible quantum advantage in using […]