Benefits of Quantum Adoption Frameworks
Adoption frameworks for quantum computing offer numerous advantages to entities and
individuals. Guides within these frameworks provide a systematic approach that enables
organizations to avoid common mistakes while preparing them for the difficulties of
implementing new technology. Moreover, they outline best practices based on past
experience to spare organizations time and resources during development. Additionally, these
adoption frameworks supply access to training tools that allow individuals and organizations
alike to build their skills in quantum computing. Addressing the industry's skills gap would
greatly benefit its growth in light of the current demand for skilled professionals in quantum
computing [Assignment Help by Phddissertationhelp.org]. 
Challenges of Quantum Adoption Frameworks
Quantum adoption frameworks have several benefits, but their execution could be better.
Initially, due to the rapid development rate of quantum technology, continuous revisions in
these frameworks are necessary to keep pace with every new advancement. Updating them
involves significant investment in both effort and resources [7]. What's more? Some
organizations or individuals may discover that these frameworks do not suit them because
they are mainly designed to assist established enterprises in adopting quantum computing
advancements rather than unique entrants with varying requirements elsewhere [7]. Finally,
we must acknowledge that some domains may not require integration with quantum
computing. The frameworks aim to ease the integration of quantum computing in disciplines
where it can provide considerable advantages. Nevertheless, in areas where the usefulness of
quantum computing is more apparent, implementing the frameworks might yield little
benefits.
Despite these challenges, several papers proposed quantum adoption frameworks to help
organizations adopt quantum computing. For instance, Chen and Liu [6] proposed a quantum
computing adoption framework that consists of three phases: preparation, planning, and
execution. The preparation phase includes activities such as quantum education and research,
while the planning phase includes use case identification and technology selection. The
execution phase includes activities such as quantum solution design and development.
Quantum Impact Analysis
Recently, numerous organizations have recognized that more than traditional computers may
be needed for performing many computational applications. As such, they are exploring
quantum computing solutions because they offer significantly higher processing power than
their classical counterparts. Nonetheless, understanding how such disruptive technologies
will shape any organization's practice is still an issue unsolved today. Therefore, advances in
research led towards development rigorous methods like Quantum Impact Analysis
Frameworks to assess precisely what operational considerations arise due to employing
emerging or cutting-edge technologies like Quantum Computing solutions.
Quantum Impact Analysis Frameworks
In the development of quantum impact analysis, in order to evaluate the influence of quantum
computing on various aspects of a company, including operations, security, and
competitiveness, frameworks have become essential. One of the earliest frameworks
proposed in the literature is the Quantum Readiness Level (QRL) framework, which
evaluates the readiness of an organization to adopt quantum computing based on different
factors, such as the availability of quantum experts, the quantum hardware infrastructure, and
the quantum software ecosystem [4]. Another framework proposed in the literature is the
Quantum Impact Assessment (QIA) framework, which evaluates quantum computing’s
impact on an organization's information systems security [6]. The QIA framework evaluates
the risk of quantum attacks on the enterprise's information systems and provides measures to
mitigate the impact of such attacks.
Quantum Impact Analysis Methodologies
In addition to developing quantum impact analysis frameworks, researchers have also
proposed different methodologies for conducting quantum impact analysis. One such
methodology is the Quantum SWOT analysis, which evaluates the strengths, weaknesses,
opportunities, and threats of quantum computing for an organization [6]. The Quantum
SWOT analysis helps organizations identify the potential benefits and risks of quantum
computing and provides insights for making informed decisions about adopting quantum
computing. Another methodology proposed in the literature is the Quantum Risk Analysis
(QRA), which assesses the risk of quantum computing on an organization's operations [9].
The QRA methodology helps organizations to evaluate the potential impact of quantum
computing on their business operations and to develop risk mitigation strategies.
Applications of Quantum Impact Analysis
The development of quantum impact analysis frameworks and methodologies has led to the
emergence of different applications of quantum impact analysis. One of the applications of
quantum impact analysis is in the field of quantum cryptography, where researchers leverage
quantum impact analysis to determine the effect of quantum computing on the security of
quantum cryptographic protocols [10]. Quantum impact analysis has also been adopted in the
quantum finance sector, where researchers have used quantum impact analysis to evaluate the
potential impact of quantum computing on the financial industry [11].
Some papers proposed methods for analyzing the potential impact of quantum computing on
various industries and applications. These methods include identifying the most promising
use cases and estimating the expected performance gains.
A key challenge for organizations that want to adopt quantum computing is to analyze the
potential impact of this technology on their industry and applications. Several papers have
proposed methods for analyzing the potential impact of quantum computing. For example,
Biamonte et al. [2] proposed a method for identifying the most promising use cases for
quantum computing. They categorized the use cases into three classes: exact problems,
heuristic problems, and data-driven problems. They also provided a case study of a
pharmaceutical company that used their method to identify a use case for quantum
computing.