Fast simulation of Grover's quantum search on classical computer
- URL: http://arxiv.org/abs/2005.04635v2
- Date: Mon, 5 Apr 2021 15:51:26 GMT
- Title: Fast simulation of Grover's quantum search on classical computer
- Authors: Ayan Chattopadhyay, Vikram Menon
- Abstract summary: Grover's search algorithm is known to be the most compute intensive.
Our approach highlights the design principles for the fast simulation of Grover's search which can be implemented on a general purpose personal computer.
The performance obtained are encouraging when compared to the existing simulators.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The research community has been actively working on the realization of
quantum computer. But the large scale commercial quantum computers are not a
reality yet quantum computing field has become richer by day with the advent of
algorithms and the avenue of its application in multiple domains. Availability
of efficient quantum simulators will enable the researchers to quickly verify
their results and concepts in order to establish a working proof of
correctness. One important algorithm that has become one of the basic
ingredients to build other algorithms and models is the Grover's search
Algorithm which is known to be the most compute intensive. Our approach
highlights the design principles for the fast simulation of Grover's search
which can be implemented on a general purpose personal computer. The
performance obtained are encouraging when compared to the existing simulators.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Comprehensive characterization of three-qubit Grover search algorithm on IBM's 127-qubit superconducting quantum computers [0.0]
We report results for the implementation and characterization of a three-qubit Grover search algorithm.
Our investigation spans the execution of the algorithm across all eight conceivable single-result oracles, alongside nine two-result oracles, employing IBM Quantum's 127-qubit quantum computers.
By connecting theoretical concepts with real-world experiments, this study shed light on the potential of NISQ computers in facilitating large-scale database searches.
arXiv Detail & Related papers (2024-06-23T05:27:46Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - Quantum Algorithm Cards: Streamlining the development of hybrid
classical-quantum applications [0.0]
The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains.
The ability of quantum computers to scale computations implies better performance and efficiency for certain algorithmic tasks than current computers provide.
To gain benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward.
arXiv Detail & Related papers (2023-10-04T06:02:59Z) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - A Herculean task: Classical simulation of quantum computers [4.12322586444862]
This work reviews the state-of-the-art numerical simulation methods that emulate quantum computer evolution under specific operations.
We focus on the mainstream state-vector and tensor-network paradigms while briefly mentioning alternative methods.
arXiv Detail & Related papers (2023-02-17T13:59:53Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem.
The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment.
We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
arXiv Detail & Related papers (2022-04-18T21:45:13Z) - Quantum Algorithms for Unsupervised Machine Learning and Neural Networks [2.28438857884398]
We introduce quantum algorithms to solve tasks such as matrix product or distance estimation.
These results are then used to develop new quantum algorithms for unsupervised machine learning.
We will also present new quantum algorithms for neural networks, or deep learning.
arXiv Detail & Related papers (2021-11-05T16:36:09Z) - Quantum Computing for Location Determination [6.141741864834815]
We introduce an example for the expected gain of using quantum algorithms for location determination research.
The proposed quantum algorithm has a complexity that is exponentially better than its classical algorithm version, both in space and running time.
We discuss both software and hardware research challenges and opportunities that researchers can build on to explore this exciting new domain.
arXiv Detail & Related papers (2021-06-11T15:39:35Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z)
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.