As we stand on the cusp of a new era in computing, coders and users must become more comfortable with cloud computing and AI technologies. Embracing these powerful tools today will pave the way for a smooth transition into quantum computing, the next big computational wave.

Without AI and a cloud platform, organisations are unlikely to succeed in an age with quantum.

Quantum computing, based on the principles of quantum mechanics, is a fundamentally different paradigm compared to classical computing. It uses qubits instead of classical bits to store and process information, allowing for parallel processing and the potential to solve problems much more efficiently than classical computers. However, the unique properties of quantum computing present several challenges, such as working with quantum states, developing new algorithms, and dealing with noise and errors in quantum hardware.

Quantum systems, like molecules and materials, are governed by the laws of quantum mechanics, which are inherently probabilistic and involve complex interactions between particles. People mistakenly believe that quantum computers are just accelerated classical computers. Yet specific problems are quantum solvable. An example problem that quantum computers can solve more efficiently than classical computers is the simulation of quantum systems.

Classical computers can struggle with simulating quantum systems due to the exponential growth in the complexity of the quantum state space as the number of particles increases. This is known as the “exponential scaling problem”, making accurate simulation of large quantum systems computationally infeasible using classical methods.

Quantum computers, on the other hand, can inherently represent and manipulate quantum states due to their quantum nature. This makes them well-suited for simulating quantum systems efficiently. Simulating quantum systems more effectively will advance fields including material science, chemistry, and drug discovery. Scientists could design new materials with tailored properties or discover new drugs by understanding the complex quantum interactions at the molecular level.

To realise these breakthroughs will need AI support. The current excitement around Generative AI is just the start, where Large Language Models can help debug or write code in various languages. Google Bard, for instance, codes in over 20 languages.

Yet coding for quantum computing is significantly more complex than classical coding. A good developer will still need a strong foundation in programming languages, data structures, algorithms, problem-solving, and critical thinking abilities. Being adept at understanding requirements, breaking down complex tasks into manageable components, and debugging code effectively will still distinguish better developers.

Additionally, good developers demonstrate strong communication and collaboration skills, allowing them to work effectively in an agile team setting. They possess a growth mindset, remaining open to learning new technologies and adapting to changes in their field.

In an age with quantum, developers will need to be comfortable with the following:

• Qubits and quantum states: Qubits can exist in a superposition of states, enabling parallel information processing. However, this also makes them more challenging to work with, as programmers must consider quantum superposition, entanglement, and other quantum phenomena when coding.

• Quantum logic gates: Quantum computing relies on quantum gates to perform operations on qubits. These gates are different from classical logic gates and have unique properties, such as reversibility. Programmers need to learn these new gates and their properties to perform computations on a quantum computer.

• Error correction and noise: Quantum computers are highly sensitive to noise and errors, which can result from their interactions with the environment or imperfect hardware. This sensitivity makes it challenging to develop error-correcting codes and algorithms that can mitigate the effects of noise and maintain the integrity of quantum computations.

• Quantum algorithms: Quantum computing requires the development of new algorithms that take advantage of quantum parallelism, superposition, and entanglement. This involves rethinking existing classical algorithms and developing new ones from scratch to exploit the power of quantum computing.

• Hybrid computing: Many quantum algorithms are designed to work alongside classical algorithms in a hybrid computing approach. This requires programmers to deeply understand classical and quantum computing principles to design and integrate algorithms for both platforms effectively.

• Learning curve: Quantum computing involves many complex physics, mathematics, and computer science concepts. This steep learning curve can be challenging for new programmers, as they need to develop a deep understanding of these concepts to write code for quantum computers effectively.

• Software tools and languages: While there are emerging software tools and programming languages designed explicitly for quantum computing, such as Qiskit, Q#, and Cirq, these tools are still evolving and can be limited in functionality compared to mature classical programming tools.

Overall, the challenges associated with coding for quantum computers mainly stem from the fundamentally different principles and concepts of quantum computing. As the field matures and more resources become available, these challenges may become more manageable for programmers. Yet, for most, help will be needed, especially during the quantum adoption phase when current programmers transition to quantum programmers.

AI will play an essential role in addressing these challenges, making it a critical tool in unlocking the power of quantum computers. Useful examples include:

• Quantum error correction: to identify and correct errors in quantum systems more efficiently. By analysing and learning from patterns of errors and noise in quantum hardware, AI can help improve the robustness and reliability of quantum computations.

• Algorithm development: to identify more efficient or novel ways to perform quantum computations, leading to better algorithms for various applications, such as cryptography, optimisation, and quantum simulations.

• Quantum control: optimises the sequences of quantum gates and operations, which is crucial for achieving high-fidelity quantum computations. By learning the best control parameters for a given quantum system, AI can help improve the performance and precision of quantum operations.

• Hybrid algorithms identify the most efficient way to partition tasks between the classical and quantum subsystems. This ensures that the overall algorithm is effective and efficient, combining classical and quantum computing resources to solve complex problems.

Developers will still need access to cloud computing, which has significantly contributed to the widespread adoption of AI technologies by providing access to powerful computational resources and facilitating collaboration among researchers and will play a similar role in developing and adopting quantum computing. Some of the ways cloud computing can contribute to overcoming the challenges associated with quantum computing will include:

• Access to quantum hardware: Quantum computers are still in the early stages of development and are expensive to build and maintain. Cloud computing enables researchers and developers to access quantum hardware remotely without investing in their own quantum infrastructure. Companies like IBM and Google offer access to their quantum hardware through cloud-based platforms, allowing users to experiment with and test their quantum algorithms.

• Scalability: Cloud computing provides a scalable platform for running quantum simulations and algorithms. Users can request additional resources to run complex simulations or test larger-scale quantum algorithms. This flexibility allows for faster development and testing of quantum algorithms without needing dedicated, on-premise hardware.

• Collaboration: Cloud-based platforms can facilitate cooperation between researchers and developers on quantum computing projects. These platforms can promote knowledge exchange and accelerate the development of new quantum algorithms and applications by providing a centralised platform for sharing code, data, and results.

• Integration with classical computing: Quantum computing often involves hybrid algorithms that combine classical and quantum resources and data. Cloud computing platforms can seamlessly integrate classical and quantum computing resources, enabling users to develop and test hybrid algorithms more quickly.

• Data security and storage: Cloud computing platforms can offer secure storage and data processing solutions for quantum computing applications. This can be particularly important for applications that involve sensitive information, such as cryptography or data analysis.

By embracing cloud computing technologies, organisations will be better prepared to understand and leverage the benefits of quantum computing as it becomes more widely available. Cloud computing enables seamless integration with AI technologies, which is essential for overcoming the unique challenges associated with quantum computing and maximising its potential across various industries and applications.

As we grapple with AI adoption and, in many sectors, only just truly embracing cloud platforms, why is this important now?

Gaining proficiency in cloud computing and AI technologies today is essential in preparing for tomorrow’s quantum computing revolution. As quantum computing emerges, AI will be crucial in overcoming its unique challenges and maximising its potential across various industries and applications.

Those organisations and teams that are familiar with these technologies now, and have regular access to emerging developments, will be well-prepared to capitalise on the opportunities that quantum computing will offer soon.

Now is the time to invest effort into understanding and mastering cloud computing and AI with the intent to embrace the transformative potential of quantum computing as it becomes more accessible. Integrating AI and cloud computing will play a crucial role in addressing the challenges of quantum computing, enabling faster development, greater collaboration, and more effective solutions. Successful organisations will be well-versed in these areas to prepare for the future of computing and ensure that they remain at the forefront of innovation and progress.