A Low-Complexity Quantum Principal Component Analysis Algorithm

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

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

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 […]

Efficient Quantum Network Communication Using Optimized Entanglement Swapping Trees

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

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

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

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

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 […]

Efficient Quantum Network Communication Using Optimized Entanglement Swapping Trees

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 […]

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 […]