Quantum state Archives - IEEE Transactions on Quantum Engineering

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

February 20, 2024Ryan L'AbbateQuantum ComputingLogic gates, Principal component analysis, Quantum circuit, Quantum computing, Quantum state, Qubit, Tensors

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to […]

Continuous-Variable Quantum Secret Sharing in Fast-Fluctuating Channels

October 9, 2023Fangli YangQuantum Networking & CommunicationsChannel estimation, Detectors, Particle measurements, Probability distribution, Protocols, Quantum state, Security

Recently, several continuous-variable quantum secret sharing (CV-QSS) protocols were proposed, while most of them are limited to the fiber channel systems with a relatively stable transmissivity. However, by means of complex channels, the transmissivity fluctuates dramatically in time with a probability distribution, which will lead to a fast-fluctuating attack. Therefore, the security analysis of CV-QSS […]

Private Product Computation Using Quantum Entanglement

September 27, 2023René Bødker ChristensenQuantum ComputingCryptography, Encoding, Galois fields, Indexes, Protocols, Quantum state, Qubit

In this article, we show that a pair of entangled qubits can be used to compute a product privately. More precisely, two participants with a private input from a finite field can perform local operations on a shared, Bell-like quantum state, and when these qubits are later sent to a third participant, the third participant […]

Corrections to “Linear Quantum State Observers”

June 14, 2023Maison ClouâtréElectromagneticsObservers, Quantum state

Presents corrections to the paper, Corrections to “Linear Quantum State Observers”. For more about this article see link below. https://ieeexplore.ieee.org/document/10148092 For the open access PDF link of this article please click.

A Low-Complexity Quantum Simulation Framework for Toeplitz-Structured Matrix and Its Application in Signal Processing

June 14, 2023Mostafizur Rahaman LaskarEnabling TechnologiesComputational modeling, Covariance matrices, Eigenvalues and eigenfunctions, Estimation, Quantum computing, Quantum state, Sparse matrices

Toeplitz matrix reconstruction algorithms (TMRAs) are one of the central subroutines in array processing for wireless communication applications. The classical TMRAs have shown excellent accuracy in the spectral estimation for both uncorrelated and coherence sources in the recent era. However, TMRAs incorporate the classical eigenvalue decomposition technique for estimating the eigenvalues of the Toeplitz-structured covariance […]

Perfect and Quasi-Perfect Codes for the Bosonic Classical-Quantum Channel

March 30, 2023Andreu Blasco CollQuantum InformationCodes, Eigenvalues and eigenfunctions, Error probability, Indexes, Quantum channels, Quantum state, Testing

In this article, we explore perfect and quasi-perfect codes for the Bosonic channel, where information is generated by a laser and conveyed in the form of coherent states. In particular, we consider the phase-modulation codebook for coherent states in a Bosonic channel. We show that these phase-modulation codes are quasi-perfect as long as the cardinality […]

Fundamentals of Quantum Fourier Optics

March 30, 2023Mohammad RezaiQuantum InformationHolography, Optical imaging, Optical signal processing, Optics, Photonics, Quantum computing, Quantum state

All-quantum signal processing techniques are at the core of the successful advancement of most information-based quantum technologies. This article develops coherent and comprehensive methodologies and mathematical models to describe Fourier optical signal processing in full quantum terms for any input quantum state of light. We begin this article by introducing a spatially 2-D quantum state […]

A Low-Complexity Quantum Principal Component Analysis Algorithm

November 2, 2022Chen HeOpen Access, Quantum ComputingEigenvalues and eigenfunctions, Phase estimation, Principal component analysis, Quantum circuit, Quantum state, Registers, Runtime

In this article, we propose a low-complexity quantum principal component analysis (qPCA) algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting principal components of the data matrix, rather than all components of the data matrix, to quantum registers, so that the samples of measurement required can be reduced considerably. Both our qPCA […]

A Divide-and-Conquer Approach to Dicke State Preparation

November 2, 2022Shamminuj AktarOpen Access, Quantum ComputingHamming weight, Logic gates, Probabilistic logic, Quantum entanglement, Quantum state, Qubit, Weight measurement

We present a divide-and-conquer approach to deterministically prepare Dicke states |Dnk⟩ (i.e., equal-weight superpositions of all n -qubit states with Hamming weight k ) on quantum computers. In an experimental evaluation for up to n=6 qubits on IBM Quantum Sydney and Montreal devices, we achieve significantly higher state fidelity compared to previous results. The fidelity gains are achieved through several techniques: our circuits […]

Machine-Learning-Based Parameter Estimation of Gaussian Quantum States

November 2, 2022Neel Kanth KunduOpen Access, Quantum Sensing & MetrologyBayes 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 […]