Quantum search in a non-Markovian environment
- URL: http://arxiv.org/abs/2303.14121v1
- Date: Thu, 23 Mar 2023 13:17:42 GMT
- Title: Quantum search in a non-Markovian environment
- Authors: Sheikh Parvez Mandal
- Abstract summary: This thesis explores the effects and origins of a 'noise with memory' in the dynamics of an open quantum system.
We show that a Markovian-correlated noise can enhance the efficiency of the algorithm over a time correlation-less noise.
A 'collisional model' is constructed that exactly reproduces the noisy evolution of the open system.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This MS thesis explores the effects and origins of a 'noise with memory' in
the dynamics of an open quantum system. The system considered here is a
multi-qubit register performing the Grover's quantum search algorithm. We show
that a Markovian-correlated noise can enhance the efficiency of the algorithm
over a time correlation-less noise. We also analytically find the set of
necessary and sufficient conditions for the algorithm's success probability to
remain invariant with respect to the number of noisy sites in the register and
point out that these conditions hold irrespective of the presence of
time-correlations in the noise. We then investigate the origins of the type of
noise considered. In this regard, a 'collisional model' is constructed that
exactly reproduces the noisy evolution of the open system. Non-Markovianity in
the system's evolution is then assessed using two well-known measures and they
are shown to be non-coincident. Our model is then slightly modified to
accommodate an elementary thermal bath. There we show that increasing the
bath's temperature increases information drainage from the system.
Related papers
- Classical-quantum correspondence in the noise-based dissipative systems [5.207420796114437]
We investigate the correspondence between classical noise and quantum environments.
We construct the so-called central spin model with its couplings fluctuating as the classical noise.
By adjusting the number of the auxiliary systems and their initial states, the noise-based model reproduces both Markovian and non-Markovian evolutions.
arXiv Detail & Related papers (2024-08-07T04:43:19Z) - Causal Layering via Conditional Entropy [85.01590667411956]
Causal discovery aims to recover information about an unobserved causal graph from the observable data it generates.
We provide ways to recover layerings of a graph by accessing the data via a conditional entropy oracle.
arXiv Detail & Related papers (2024-01-19T05:18:28Z) - A quantum algorithm for solving open system dynamics on quantum
computers using noise [0.0]
We present a quantum algorithm that uses noise as a resource.
The goal of our quantum algorithm is the calculation of operator averages of an open quantum system evolving in time.
We find that classes of open quantum systems exist where our algorithm performs very well, even with gate errors as high as 1%.
arXiv Detail & Related papers (2022-10-21T17:47:32Z) - Characterizing low-frequency qubit noise [55.41644538483948]
Fluctuations of the qubit frequencies are one of the major problems to overcome on the way to scalable quantum computers.
The statistics of the fluctuations can be characterized by measuring the correlators of the outcomes of periodically repeated Ramsey measurements.
This work suggests a method that allows describing qubit dynamics during repeated measurements in the presence of evolving noise.
arXiv Detail & Related papers (2022-07-04T22:48:43Z) - High-Order Qubit Dephasing at Sweet Spots by Non-Gaussian Fluctuators:
Symmetry Breaking and Floquet Protection [55.41644538483948]
We study the qubit dephasing caused by the non-Gaussian fluctuators.
We predict a symmetry-breaking effect that is unique to the non-Gaussian noise.
arXiv Detail & Related papers (2022-06-06T18:02:38Z) - Decoherence Effects in a Three-Level System under Gaussian Process [0.7649716717097428]
We show that the destructive nature of the Ornstein--Uhlenbeck noise toward the symmetry of the qutrit to preserve encoded purity and coherence remains large.
We find that the current qutrit system outperforms systems with multiple qubits or qutrits using purity and von Neumann entropy.
arXiv Detail & Related papers (2021-07-29T11:13:13Z) - Noisy Coherent Population Trapping: Applications to Noise Estimation and
Qubit State Preparation [0.0]
Coherent population trapping is a well-known quantum phenomenon in a driven $Lambda$ system.
When a bath is present in addition to vacuum noise, the observed trapping is no longer perfect.
We show that an optimum choice of Rabi frequencies is possible.
arXiv Detail & Related papers (2021-03-30T20:04:27Z) - Learning based signal detection for MIMO systems with unknown noise
statistics [84.02122699723536]
This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics.
In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable.
Our framework is driven by an unsupervised learning approach, where only the noise samples are required.
arXiv Detail & Related papers (2021-01-21T04:48:15Z) - Evaluating the noise resilience of variational quantum algorithms [0.0]
We simulate the effects of different types of noise in state preparation circuits of variational quantum algorithms.
We find that the inclusion of redundant parameterised gates makes the quantum circuits more resilient to noise.
arXiv Detail & Related papers (2020-11-02T16:56:58Z) - Shape Matters: Understanding the Implicit Bias of the Noise Covariance [76.54300276636982]
Noise in gradient descent provides a crucial implicit regularization effect for training over parameterized models.
We show that parameter-dependent noise -- induced by mini-batches or label perturbation -- is far more effective than Gaussian noise.
Our analysis reveals that parameter-dependent noise introduces a bias towards local minima with smaller noise variance, whereas spherical Gaussian noise does not.
arXiv Detail & Related papers (2020-06-15T18:31:02Z) - Active Model Estimation in Markov Decision Processes [108.46146218973189]
We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP)
We show that our Markov-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime.
arXiv Detail & Related papers (2020-03-06T16:17:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.