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If you thought all generative AI could do was produce text and images, think again. It can do far more about way more serious and complex problems all it needs is a quantum boost. This new field of learning called quantum generative AI is a perfect marriage between the very buzzy area of generative AI and quantum computing and brings together the benefits of both.

In an exclusive chat with Prateek Jain, Lead Researcher and Architect of Quantum Computing at Fractal, we discuss the applications of quantum generative AI in critical sectors like healthcare, the associated challenges and how to get around them.

Fractal: Quantum generative AI (QGAI) is a subfield of quantum computing that focuses on developing algorithms and models that can generate new data with the help of quantum computers.

The main difference between QGAI and classical generative AI is the underlying computational platform. While classical generative AI relies on classical computers, QGAI is a novel approach which relies on quantum computers to perform operations on quantum bits (qubits) to generate new data. QGAI algorithms are designed to exploit the quantum mechanical properties of qubits to generate data with unique and potentially useful features.

Some examples of QGAI applications include:

Fractal: Quantum generative AI can be used to optimize drug and material properties by generating new compounds with specific characteristics that are required for various applications. Here are some ways in which QGAI can be used to optimize drug and material properties:

Drug Optimization: QGAI can be used to optimize the properties of existing drugs to enhance their efficacy and reduce side effects. By simulating the behaviour of molecules on quantum computers, QGAI algorithms can predict the binding affinity of a molecule to a target protein, which is a key factor in determining drug efficacy. QGAI can also be used to optimize drug properties, such as solubility and bioavailability, to improve their pharmacokinetics.

You can find some of our research at Fractal.ai here https://arxiv.org/abs/2212.07826

Material Optimization: QGAI can be used to optimize the properties of materials for specific applications, such as energy storage or catalysis. By simulating the behaviour of atoms and molecules on quantum computers, QGAI algorithms can predict the properties of materials, such as their conductivity or reactivity, malleability, radioactivity etc.

Chemical Reaction Optimization: QGAI can also be used to optimize chemical reactions by predicting the optimal conditions and reactants required for a specific reaction. This can lead to more efficient and sustainable chemical processes, such as the production of pharmaceuticals or materials.

Fractal: Quantum generative AI has the potential to revolutionize drug and material design by helping researchers to generate new and innovative compounds that could not be discovered using traditional methods. QGAI algorithms use quantum computers to simulate the behaviour of molecules and atoms and help researchers to predict their properties and generate new structures that meet specific criteria.

Here are some examples of how QGAI can be applied to drug and material design:

Drug Discovery: QGAI can be used to generate new compounds that are more effective at targeting specific diseases than existing drugs.

Material Design: QGAI can be used to design new materials with unique properties, such as superconductors or catalysts. By simulating the behaviour of atoms and molecules on quantum computers, QGAI algorithms can predict the properties of new materials, such as their conductivity or reactivity. This can help researchers design materials with specific properties that are required for various applications, such as energy storage or catalysis.

The potential benefits of using QGAI for drug and material design are significant. QGAI can accelerate the drug discovery and material design process by reducing the time and cost required to synthesize and test new compounds. QGAI can also enable researchers to discover compounds that would be difficult or impossible to discover using classical methods, leading to new treatments for diseases and innovative materials for a variety of applications. Additionally, QGAI can enable researchers to design compounds with specific properties that are required for various applications and pave the way for more effective drugs and materials.

There have been several research projects that have already applied quantum generative AI (QGAI) to drug and material design.

For instance, in 2019, researchers from IBM used QGAI to design a new molecule that could potentially be used to create more efficient potential drug molecules. The designed molecule was then synthesized and tested and was found to have high drug-like properties.

These examples demonstrate the potential of QGAI to accelerate the drug and material design process by generating new compounds that have desirable properties. While these projects are still in the early stages of development, they provide a glimpse into the exciting possibilities of QGAI in the field of drug and material design.

Fractal: Even though the emerging fields of quantum machine learning & newer quantum generative modelling are in their infancy, fast-moving research will push these areas to the fore. For starters, organizations can ensure that their quantum generative AI models are accurate and reliable by following some established approaches:

Data quality control: High-quality data is crucial for models to produce accurate results, especially in the healthcare sector. Organizations can ensure data quality by validating and cleaning up the data before using it to train QGAI models. They can also use statistical methods to identify and remove any outliers or irrelevant data points.

Model validation and testing: Organizations should validate their models by testing them on independent data sets. This can help identify any errors or biases in the models and provide insights into how they can be improved.

Explainability and transparency: Organizations should ensure that their models are transparent and explainable, which means that the models should be able to provide a clear explanation of their decision-making process. This can help identify any potential biases ahead and reduce them before they pop up in the predictions.

Regular updates and maintenance: Models should be regularly updated and maintained to ensure their accuracy and reliability.

In addition to these measures, organizations can also take the following steps to mitigate any potential errors or biases in their models:

Diversity and inclusivity: Organizations should ensure that their QGAI models are trained on diverse and inclusive data sets to avoid any biases that may arise from underrepresented groups.

Robustness testing: Organizations can test the robustness of their models by intentionally introducing errors or biases into the data and observing how the models respond.

Ethical considerations: Organizations should consider the ethical implications of their QGAI models and ensure that they do not cause harm or discrimination to individuals or groups.

Fractal: Using quantum generative AI brings along several challenges:

Scalability: As the size of the molecule or material being designed increases, so does the complexity of the calculations required. This can limit the scalability of QGAI for designing larger and more complex materials and drugs especially when the quantum processors are very small in the NISQ era.

Noise and decoherence: The inherent noise and decoherence in quantum computing is a major problem & can affect the accuracy of the models by a wide margin. This can lead to several errors in the predictions made.

Data quality and quantity: QGAI models require high-quality and diverse data to accurately predict properties and generate new compounds. However, acquiring such data can be challenging and costly, especially for rare or newly discovered compounds.

Interpreting results: QGAI models may generate novel compounds that exhibit desired properties, but it can be challenging to interpret why these compounds have these properties, making it difficult to optimize their performance further.

To address these challenges, researchers are exploring various approaches, such as:

Developing more efficient algorithms: Researchers are developing new algorithms and techniques to improve the efficiency of QGAI calculations and reduce the computing resources required for large-scale designs.

Quantum error correction: Researchers are developing quantum error correction techniques to mitigate the impact of noise and decoherence in quantum computing.

Integration with classical computing: Hybrid quantum-classical computing approaches are being developed to address the current limitations by utilizing the strengths of classical computing to supplement quantum computing.

Enhanced data collection and processing: Researchers are exploring ways to improve data quality and quantity by leveraging advances in data collection and processing technologies, such as machine learning and high-throughput screening techniques.

By addressing these challenges, researchers can further advance the use of QGAI for drug and material design and unlock its full potential for revolutionizing these industries.

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