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We propose a high efficiency tomographic scheme to reconstruct an unknown quantum state of the qubits by using a series of quantum nondemolition (QND) measurements. The proposed QND measurements of the qubits are implemented by probing the…

Quantum Physics · Physics 2015-05-20 J. S. Huang , L. F. Wei , C. H. Oh

Quantum annealing (QA) is one of the efficient methods to calculate the ground-state energy of a problem Hamiltonian. In the absence of noise, QA can accurately estimate the ground-state energy if the adiabatic condition is satisfied.…

Quantum Physics · Physics 2022-10-18 Yuta Shingu , Tetsuro Nikuni , Shiro Kawabata , Yuichiro Matsuzaki

We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized…

Quantum Physics · Physics 2021-07-13 Carlos Bravo-Prieto

The Gaussian state description of continuous variables is adapted to describe the quantum interaction between macroscopic atomic samples and continuous-wave light beams. The formalism is very efficient: a non-linear differential equation…

Quantum Physics · Physics 2007-05-23 L. B. Madsen , K. Mølmer

We investigate critical quantum metrology,that is the estimation of parameters in many-body systems close to a quantum critical point, through the lens of Bayesian inference theory. We first derive a no-go result stating that any…

Quantum Physics · Physics 2023-10-31 Raffaele Salvia , Mohammad Mehboudi , Martí Perarnau-Llobet

Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep neural networks. However, when paired with strong…

Machine Learning · Computer Science 2020-06-02 Qile Zhu , Jianlin Su , Wei Bi , Xiaojiang Liu , Xiyao Ma , Xiaolin Li , Dapeng Wu

We consider a controlled quantum system whose finite dimensional state is governed by a discrete-time nonlinear Markov process. In open-loop, the measurements are assumed to be quantum non-demolition (QND). The eigenstates of the measured…

Optimization and Control · Mathematics 2015-03-19 Hadis Amini , Abhinav Somaraju , Igor Dotsenko , Clement Sayrin , Mazyar Mirrahimi , Pierre Rouchon

We characterize the set of generalized quantum measurements that can be decomposed into a continuous measurement process using a stream of probe qubits and a tunable interaction Hamilto- nian. Each probe in the stream interacts weakly with…

Quantum Physics · Physics 2015-12-16 Jan Florjanczyk , Todd A. Brun

In this paper we present a new approach for testing QM against the realism aspect of hidden variable theory (HVT). We consider successive measurements of non-commuting operators on a input spin $s$ state. The key point is that, although…

Quantum Physics · Physics 2007-05-23 Ali Ahanj , Pramod Joag

For the standard Quantum Brownian Motion (QBM) model, we point out the occurrence of simultaneous (parallel), mutually irreducible and autonomous decoherence processes. Besides the standard, one Brownian particle, we show there is at least…

Quantum Physics · Physics 2012-09-05 M. Dugic , J. Jeknic-Dugic

A quantum state is fully characterized by its density matrix or equivalently by its quasiprobabilities in phase space. A scheme to identify the quasiprobabilities of a quantum state is an important tool in the recent development of quantum…

Quantum Physics · Physics 2018-05-04 Dingshun Lv , Shuoming An , Mark Um , Junhua Zhang , Jing -Ning Zhang , M. S. Kim , Kihwan Kim

A model of quantum measurement is proposed, which aims to describe statistical mechanical aspects of this phenomenon, starting from a purely Hamiltonian formulation. The macroscopic measurement apparatus is modeled as an ideal Bose gas, the…

Statistical Mechanics · Physics 2016-08-31 Armen E. Allahverdyan , Roger Balian , Theo M. Nieuwenhuizen

Quantum Analogical Modeling (QAM) works under the assumption that the correct exemplar-based description for a system of behavior minimizes the overall uncertainty of the system. The measure used in QAM differs from the traditional…

Quantum Physics · Physics 2010-06-17 Royal Skousen

Beginning in abstract space and dislodging the representational form paves a way to formulate a version of a quantum physical measurement scheme. With materiality playing sustainment roles with respect to q-states, these latter control…

Quantum Physics · Physics 2020-02-21 O. Tapia

Variational quantum eigensolver (VQE), which combines quantum systems with classical computational power, has been arisen as a promising candidate for near-term quantum computing applications. However, the experimental resources such as the…

Measurements in the quantum domain can exceed classical notions. This concerns fundamental questions about the nature of the measurement process itself, as well as applications, such as their function as building blocks of quantum…

Quantum Physics · Physics 2023-02-02 Jan Sperling , Ilaria Gianani , Marco Barbieri , Elizabeth Agudelo

Based on empirical evidence, quantum systems appear to be strictly linear and gauge invariant. This work uses concise mathematics to show that quantum eigenvalue equations on a one dimensional ring can either be gauge invariant or have a…

Quantum Physics · Physics 2014-07-15 Arthur Davidson

We consider discrete-time quantum systems subject to Quantum Non-Demolition (QND) measurements and controlled by an adjustable unitary evolution between two successive QND measures. In open-loop, such QND measurements provide a…

Optimization and Control · Mathematics 2011-08-09 Hadis Amini , Pierre Rouchon , Mazyar Mirrahimi

We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation. In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically…

Quantum Physics · Physics 2025-09-17 Alexandra Ramôa , Luis Paulo Santos

We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs…

Machine Learning · Computer Science 2025-11-25 Shichen Cao , Jingjing Li , Kenric P. Nelson , Mark A. Kon