Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Apress eBooks

Introduction: Superconducting Qubits and Their Realizations

Subhojit Halder, Kinjal A. Chauhan, Muhamad Bagher Barfar, Srinjoy Ganguly, Shalini Devendrababu

Published Jan 1, 2025• 0 citations
Publisher

Abstract

For a long time, classical computers have been used to realize complex systems, but with time, the complexity of the circuits increased. We started to witness the limitations of this type of computing, resulting in a lack of appropriate analysis and a vast runtime for the running code. Most of all, there are inaccurate results for a massive data pool. Classical computers, which rely on bits to represent data as 0 or 1, have been the foundation for handling complex applications like artificial intelligence, GPS tracking, and large-scale simulations. However, as the complexity of problems increases, especially in areas such as AI, climate modeling, and drug discovery, classical systems face limitations in processing power and efficiency with the increase in data pool and for faster processing time with more accuracy, a new method sought after a method which can provide parallel computation of data and can handle large pool of data at ease handling the unique exceptions keeping these concerns in mind many pioneers started to look after alternative to classical computation methods. In contrast, many believed in the power of classical computation and tried to improve its efficiency and usage, while others started looking into alternatives.

DOI
10.1007/979-8-8688-1504-1_1

Related papers

Solid-state qubits

M. Fernando González-Zalba

The quantum world is fascinating. It presents a description of nature that defies our most rooted concepts about what reality is. For example, quantum objects possess \lq\lq spooky\rq\rq\ properties that allow them to be in multiple places at the same time, to move in different directions simultaneously, or to exist and not to exist. In other words, to live several parallel stories. Making use of these parallel stories to compute is the realm of quantum computation, a world in which information is processed using the laws of quantum physics. Nowadays, quantum computers are an object of an extensive experimental and theoretical research, since it is known that, on paper, these can solve many of the current computational challenges. These are the problems that even the most sophisticated supercomputers of today, or tomorrow, will not be able to solve.

arXiv:1801.06722•Jan 20, 2018•1 citation

Introduction to Quantum Computing and Artificial Intelligence

Gursewak Singh, Nimisha Singh, Ramandeep Sandhu +2 more

Quantum Artificial Intelligence (QAI) combines quantum computing with artificial intelligence (AI) to surpass the traditional barriers of classical computing systems, such as their inability to solve large-scale optimization problems or simulate complex quantum systems, which require exponentially increasing computational resources. Quantum computing, on the other hand, makes use of quantum mechanical phenomena of superposition, entanglement, and interference to process millions of data points in parallel, avoiding the exponential computation needed in many complex problems. QAI combines principles of quantum computing with AI techniques, thereby accelerating model learning. Some of the most notable applications include drug discovery in terms of exact predictions of molecular interactions, due to the enhancement of quantum simulations. There are cryptographic applications where QAI challenges classical encryption but also contains more advanced, non-breakable quantum cryptographic methods. In the same way as general advances in the field of data analysis and optimization, these domains would change: financial modeling, cybersecurity, and climate science. The biggest challenges remaining in scaling quantum hardware are noise and decoherence. Solutions will require major advances in quantum error correction, along with hybrid quantum-classical systems integrating the strengths of each. At this point, added to the list of critical concerns in the development of QAI are ethical considerations such as data privacy, algorithmic bias, and transparency. But all this notwithstanding, QAI has the transformative potential to solve problems that classical AI was unable to solve and drive innovation in domains ranging from healthcare and environmental science to cryptography and beyond. Unlocking its full capabilities and overcoming the resultant technical and ethical challenges is by way of further research and collaboration.

Chapman & Hall/CRC eBooks•Apr 30, 2026•0 citations

QUANTUM COMPUTING AND ITS PLACE IN HIGHER EDUACTION: NAVIGATING OPPORTUNITIES AND CHALLENGES

H M Dhanush & Dr. Omprakash H M

Quantum computing represents a shift in how we process information, rooted not in classical binary logic but in the principles of quantum mechanics. While traditional computers operate with bits (binary units of information that are either 0 or 1) quantum computers use qubits, which can exist in multiple states simultaneously thanks to phenomena of superposition and entanglement. This allows quantum machines to explore vast solution spaces in parallel, making them exponentially more powerful than classical computers for certain types of problems. Quantum computing allows fundamentally different approaches to computation that excels at simulating complex systems, solving optimization problems, and processing probabilistic data. Fields such as cryptography, materials science, climate modeling, and drug discovery are already exploring its potential. As quantum hardware matures, it is expected to complement classical systems, unlocking capabilities that were previously out of reach, even for the most powerful supercomputers. The implications for education, both in terms of what is taught and how learning systems operate, are far-reaching. While artificial intelligence has already introduced significant disruption to educational systems, quantum computing stands to exponentially amplify these shifts. Quantum-AI systems, which leverage probabilistic computing to process high-dimensional data and explore vast solution spaces in parallel, may redefine how learning environments adapt, how decisions are optimized, and how institutional systems are structured. This convergence of quantum computing and AI introduces fundamentally new computational logics that challenge conventional models of curriculum design, assessment, and pedagogical agency.

Scholarly Research Journal for Interdisciplinary Studies•Jan 29, 2026•0 citations

Evaluating the performance of classical electronics for quantum computing tasks in superconducting qubit systems

Maika Takita, Antonio D. Corcoles, Ken Inoue +4 more

Bulletin of the American Physical Society•Mar 7, 2019•0 citations

Integration of classical electronics for quantum computing tasks in superconducting qubit systems

Antonio D. Corcoles, Maika Takita, Ken Inoue +4 more

Bulletin of the American Physical Society•Mar 7, 2019•1 citation

Transforming Data Engineering with Quantum Computing: A New Frontier for AI Models

Sudheer Singamsetty

The convergence of quantum computing and artificial intelligence (AI) represents a transformative opportunity to advance data engineering processes. Traditional data engineering models face significant challenges in handling the increasing complexity and volume of data required for modern AI applications. Quantum computing, with its ability to process vast amounts of data simultaneously, offers a promising solution to these challenges. This paper explores how quantum computing can revolutionize data engineering by providing unprecedented computational power and efficiency. We present an overview of the core principles of quantum computing and examine its potential applications in AI models, focusing specifically on optimization, data processing, and machine learning. The integration of quantum algorithms into existing data engineering workflows is discussed, highlighting their impact on improving the efficiency, speed, and scalability of AI systems. Through an experimental case study, the performance of quantum-enhanced AI models is evaluated against classical models, showcasing significant improvements in processing time and model accuracy. The paper concludes by outlining future research directions and the challenges that need to be addressed to fully leverage the potential of quantum computing in data engineering.

International Journal of Computational Mathematical Ideas•Jan 1, 2024•2 citations
For a long time, classical computers have been used to realize complex systems, but with time, the complexity of the circuits increased. We started to witness the limitations of this type of computing, resulting in a lack of appropriate analysis and a vast runtime for the running code. Most of all, there are inaccurate results for a massive data pool. Classical computers, which rely on bits to represent data as 0 or 1, have been the foundation for handling complex applications like artificial intelligence, GPS tracking, and large-scale simulations. However, as the complexity of problems increases, especially in areas such as AI, climate modeling, and drug discovery, classical systems face limitations in processing power and efficiency with the increase in data pool and for faster processing time with more accuracy, a new method sought after a method which can provide parallel computation of data and can handle large pool of data at ease handling the unique exceptions keeping these concerns in mind many pioneers started to look after alternative to classical computation methods. In contrast, many believed in the power of classical computation and tried to improve its efficiency and usage, while others started looking into alternatives.
The quantum world is fascinating. It presents a description of nature that defies our most rooted concepts about what reality is. For example, quantum objects possess \lq\lq spooky\rq\rq\ properties that allow them to be in multiple places at the same time, to move in different directions simultaneously, or to exist and not to exist. In other words, to live several parallel stories. Making use of these parallel stories to compute is the realm of quantum computation, a world in which information is processed using the laws of quantum physics. Nowadays, quantum computers are an object of an extensive experimental and theoretical research, since it is known that, on paper, these can solve many of the current computational challenges. These are the problems that even the most sophisticated supercomputers of today, or tomorrow, will not be able to solve.
Quantum Artificial Intelligence (QAI) combines quantum computing with artificial intelligence (AI) to surpass the traditional barriers of classical computing systems, such as their inability to solve large-scale optimization problems or simulate complex quantum systems, which require exponentially increasing computational resources. Quantum computing, on the other hand, makes use of quantum mechanical phenomena of superposition, entanglement, and interference to process millions of data points in parallel, avoiding the exponential computation needed in many complex problems. QAI combines principles of quantum computing with AI techniques, thereby accelerating model learning. Some of the most notable applications include drug discovery in terms of exact predictions of molecular interactions, due to the enhancement of quantum simulations. There are cryptographic applications where QAI challenges classical encryption but also contains more advanced, non-breakable quantum cryptographic methods. In the same way as general advances in the field of data analysis and optimization, these domains would change: financial modeling, cybersecurity, and climate science. The biggest challenges remaining in scaling quantum hardware are noise and decoherence. Solutions will require major advances in quantum error correction, along with hybrid quantum-classical systems integrating the strengths of each. At this point, added to the list of critical concerns in the development of QAI are ethical considerations such as data privacy, algorithmic bias, and transparency. But all this notwithstanding, QAI has the transformative potential to solve problems that classical AI was unable to solve and drive innovation in domains ranging from healthcare and environmental science to cryptography and beyond. Unlocking its full capabilities and overcoming the resultant technical and ethical challenges is by way of further research and collaboration.
Quantum computing represents a shift in how we process information, rooted not in classical binary logic but in the principles of quantum mechanics. While traditional computers operate with bits (binary units of information that are either 0 or 1) quantum computers use qubits, which can exist in multiple states simultaneously thanks to phenomena of superposition and entanglement. This allows quantum machines to explore vast solution spaces in parallel, making them exponentially more powerful than classical computers for certain types of problems. Quantum computing allows fundamentally different approaches to computation that excels at simulating complex systems, solving optimization problems, and processing probabilistic data. Fields such as cryptography, materials science, climate modeling, and drug discovery are already exploring its potential. As quantum hardware matures, it is expected to complement classical systems, unlocking capabilities that were previously out of reach, even for the most powerful supercomputers. The implications for education, both in terms of what is taught and how learning systems operate, are far-reaching. While artificial intelligence has already introduced significant disruption to educational systems, quantum computing stands to exponentially amplify these shifts. Quantum-AI systems, which leverage probabilistic computing to process high-dimensional data and explore vast solution spaces in parallel, may redefine how learning environments adapt, how decisions are optimized, and how institutional systems are structured. This convergence of quantum computing and AI introduces fundamentally new computational logics that challenge conventional models of curriculum design, assessment, and pedagogical agency.
The convergence of quantum computing and artificial intelligence (AI) represents a transformative opportunity to advance data engineering processes. Traditional data engineering models face significant challenges in handling the increasing complexity and volume of data required for modern AI applications. Quantum computing, with its ability to process vast amounts of data simultaneously, offers a promising solution to these challenges. This paper explores how quantum computing can revolutionize data engineering by providing unprecedented computational power and efficiency. We present an overview of the core principles of quantum computing and examine its potential applications in AI models, focusing specifically on optimization, data processing, and machine learning. The integration of quantum algorithms into existing data engineering workflows is discussed, highlighting their impact on improving the efficiency, speed, and scalability of AI systems. Through an experimental case study, the performance of quantum-enhanced AI models is evaluated against classical models, showcasing significant improvements in processing time and model accuracy. The paper concludes by outlining future research directions and the challenges that need to be addressed to fully leverage the potential of quantum computing in data engineering.