Quantum Computing Toolkit From Nuts and Bolts to Sack of Tools
- URL: http://arxiv.org/abs/2302.08884v1
- Date: Fri, 17 Feb 2023 14:08:44 GMT
- Title: Quantum Computing Toolkit From Nuts and Bolts to Sack of Tools
- Authors: Himanshu Sahu and Dr. Hariprabhat Gupta
- Abstract summary: Quantum computing has the potential to provide exponential performance benefits in processing over classical computing.
It utilizes quantum mechanics phenomena (such as superposition, entanglement, and interference) to solve a computational problem.
Quantum computers are in the nascent stage of development and are noisy due to decoherence, i.e., quantum bits deteriorate with environmental interactions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Quantum computing has the potential to provide exponential performance
benefits in processing over classical computing. It utilizes quantum mechanics
phenomena (such as superposition, entanglement, and interference) to solve a
computational problem. It can explore atypical patterns over data that
classical computers can't perform efficiently. Quantum computers are in the
nascent stage of development and are noisy due to decoherence, i.e., quantum
bits deteriorate with environmental interactions. It will take a long time for
quantum computers to achieve fault tolerance although quantum algorithms can be
developed in advance. Heavy investment in developing quantum hardware, software
development kits, and simulators has led to multiplicity of quantum development
tools. Selection of a suitable development platform requires a proper
understanding of the capabilities and limitations of these tools. Although a
comprehensive comparison of the different quantum development tools would be of
great value, to the best of our knowledge, no such extensive study is currently
available.
Related papers
- Digital-Analog Quantum Machine Learning [0.0]
Machine learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products.
dealing with an increasing amount of data poses difficulties for classical devices.
Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts.
arXiv Detail & Related papers (2024-11-16T08:54:52Z) - How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities [3.864855748348313]
Small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits.
Despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown.
We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits.
arXiv Detail & Related papers (2024-11-15T18:22:46Z) - The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - Quantum algorithms for scientific computing [0.0]
Areas that are likely to have the greatest impact on high performance computing include simulation of quantum systems, optimization, and machine learning.
Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, aerospace engineering, and the design of "green" materials for sustainable development.
arXiv Detail & Related papers (2023-12-22T18:29:31Z) - The QUATRO Application Suite: Quantum Computing for Models of Human
Cognition [49.038807589598285]
We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling.
We release QUATRO, a collection of quantum computing applications from cognitive models.
arXiv Detail & Related papers (2023-09-01T17:34:53Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Architectures for Quantum Information Processing [5.190207094732672]
Quantum computing is changing the way we think about computing.
Quantum phenomena like superposition, entanglement, and interference can be exploited to solve issues that are difficult for traditional computers.
IBM's first public access to true quantum computers through the cloud, as well as Google's demonstration of quantum supremacy, are among the accomplishments.
arXiv Detail & Related papers (2022-11-11T19:18:44Z) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - The Future of Quantum Computing with Superconducting Qubits [2.6668731290542222]
We see a branching point in computing paradigms with the emergence of quantum processing units (QPUs)
Extracting the full potential of computation and realizing quantum algorithms with a super-polynomial speedup will most likely require major advances in quantum error correction technology.
Long term, we see hardware that exploits qubit connectivity in higher than 2D topologies to realize more efficient quantum error correcting codes.
arXiv Detail & Related papers (2022-09-14T18:00:03Z) - Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
Quantum generative learning models (QGLMs) may surpass their classical counterparts.
We review the current progress of QGLMs from the perspective of machine learning.
We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
arXiv Detail & Related papers (2022-06-07T07:32:57Z)
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.