Quantum Adoption Frameworks

Quantum computing is an emerging technology that promises to revolutionize various industries by solving challenges that classical computers struggle with. However, the adoption of quantum computing faces various challenges, including the need for a more skilled workforce, the high cost of hardware, and the complexity of software development. Quantum adoption frameworks have been proposed to overcome the various challenges and facilitate the integration of quantum computing into different fields. In this paper, relevant peer-reviewed articles have been researched to study quantum adoption frameworks thoroughly. For more information contact PhD Dissertations Helpers online

 

A comprehensive table is included in this paper that offers an overview of the primary components of a Quantum Adoption Framework and their descriptions. Organizations can use it as a reference while developing their framework for adopting quantum technologies.

 

Table 3: Components of a Quantum Adoption Framework

 

Component	Description
Business  Strategy	Defines the business objectives and goals for adopting quantum technologies
Technology Strategy	Determines the quantum technology landscape and identifies relevant use cases
Capability Assessment	Evaluates the organization’s readiness for quantum adoption, including technical and human resources
Roadmap Development	Develops a phased approach to implementing quantum technologies in the organization

 

Receiving its inception from IBM lies a revolutionary pathway known as the Quantum Ready program – a useful framework for adopting quantum technology. Companies that utilize this structure are provisioned with valuable resources such as software tools, training sessions, and accessibility to various forms of hardware, which empower them to develop high-tech applications [7][12]. The model includes five stages: exploration, experimentation, implementation, expansion, and optimization. Each stage includes activities and deliverables to prepare the organization for the next stage [7].

3.8 Applications of Quantum Machine Learning

QML is a subfield of quantum computing that applies machine learning algorithms to quantum data. It combines the fundamental principles of quantum mechanics, such as entanglement and superposition, with classical machine learning techniques to create a powerful hybrid approach. Biamonte et al. [2] state that “QML provides a new paradigm for solving pattern recognition and optimization problems by exploiting the power of quantum computing.”

Below is a table illustrating the variations between classical and quantum machine learning. 

Table 4: Difference between Classical and Quantum Machine Learning

Classical Machine Learning	Quantum Machine Learning
Works with classical bits	Works with quantum bits
Processing power grows linearly with the number of bits	Processing power rises exponentially with the quantity of qubits
Limited by the curse of dimensionality	Can handle high-dimensional data efficiently
Cannot solve certain optimization problems efficiently	Can provide exponential speedup for certain optimization problems
Limited by the no-cloning theorem	Can perform quantum teleportation and quantum state transfer

One of the main applications of QML is in quantum-enhanced data analysis, where quantum algorithms are used to analyze large datasets. 

Harrow and Montanaro [13] proposed a quantum algorithm that can perform principal component analysis (PCA) exponentially faster than classical algorithms. Their paper showed that the quantum algorithm could extract principal components of a dataset with high accuracy and reduced computational complexity.

Another important application of QML is in quantum-enhanced optimization, where quantum algorithms are used to solve optimization problems. Farhi et al. [14] proposed a quantum mechanism for solving combinatorial optimization problems that can provide exponential speedup compared to classical algorithms. Their algorithm uses a quantum annealer to find the ground state, which can be utilized to solve optimization problems such as the traveling salesperson problem.

QML also has applications in quantum chemistry, where it can simulate the behavior of molecules and chemical reactions. McClean et al. [15] proposed a quantum model for simulating the electronic structure of molecules, which can provide exponential speedup compared to classical algorithms. In their paper, they showed that their algorithm could accurately simulate the behavior of small molecules, paving the way for quantum simulations of more complex molecules.

Similarly, Carleo et al. [16] reviewed the intersection between physical sciences and machine learning, including quantum machine learning. They discussed the potential applications of quantum machine learning in areas such as quantum chemistry, materials science, and condensed matter physics.

• Gaps and Opportunities 

Quantum transformation and adoption strategies and frameworks research is a rapidly rising field with great potential for innovation and advancement. However, there are still several gaps and opportunities that researchers and practitioners can explore. Here are some possible areas for consideration:

1. Lack of standardized frameworks: Researchers including Kaur and Jha [4], Chen and Liu [6], Mohapatra et al. [8] propose different frameworks, which contribute to the incompatibility and disintegration. There needs to be standardized frameworks for quantum transformation and adoption, which makes it difficult for organizations to know where to start and how to measure progress. Developing and implementing standardized frameworks could help organizations navigate the complexities of quantum transformation and make more informed decisions.
2. Limited understanding of quantum technologies: Many organizations still need to gain a greater understanding of quantum technologies and their potential applications. There is an opportunity to educate decision-makers about quantum technologies, their benefits, and their limitations and to explore the potential applications in various industries.
3. Integration with existing infrastructure: Integrating quantum technologies with existing IT infrastructure can be challenging. Organizations must develop strategies for integrating quantum technologies with their existing systems and processes.
4. Regulatory and ethical considerations: Quantum technologies raise several regulatory and ethical considerations, particularly in fields like healthcare and finance. Researchers and practitioners need to explore these issues and develop guidelines for the ethical and responsible use of quantum technologies.