Quantum-enhanced machine learning models complex biological systems with greater accuracy.

A new hybrid classical machine learning platform addresses complex biological modelling challenges. It employs an encode-search-build approach, efficiently extracting relevant data to create progressively complex models. Initial applications demonstrate enhanced classification of biological states and prediction of their temporal evolution, overcoming limitations of classical methods.

The increasing volume and complexity of biological data, generated by techniques such as genomics, proteomics and metabolomics – collectively known as multiomics – present significant computational challenges. Classical computing methods struggle to model the intricate interactions governing biological systems and accurately predict outcomes like disease progression or responses to treatment. Researchers are now exploring the potential of quantum computing to overcome these limitations, though practical implementation has been hampered by issues of scale and accessibility. Michael Kubal and Sonika Johri, from Coherent Computing Inc, address these challenges in their work, “A Quantum Platform for Multiomics Data”, by presenting a hybrid classical-quantum machine learning platform. Their approach, utilising an ‘encode-search-build’ methodology, aims to efficiently process biological data, identify key parameters and construct increasingly complex predictive models, demonstrated through applications in phenotypic classification and temporal prediction of biological systems.

The inherent complexity of biological systems presents a considerable challenge to accurate modelling, particularly when investigating emergent behaviours crucial for understanding disease and developing effective therapies. Recent advances in multiomic profiling, which involves the comprehensive analysis of molecules such as DNA, RNA and proteins, while providing detailed measurements of biological components, are often limited by the computational capacity of classical approaches. This research presents a hybrid quantum-classical machine learning platform designed to overcome these limitations and facilitate the analysis of complex biological data.

The platform employs an ‘encode-search-build’ strategy, beginning by efficiently extracting relevant information from biological datasets and encoding it into a quantum state. Quantum states represent the probabilistic nature of quantum systems, allowing for potentially faster and more efficient computation. Subsequently, provably efficient training algorithms search for optimal model parameters, and a stacking strategy enables the systematic construction of increasingly complex models as computational resources expand. This approach aims to address scalability barriers that currently hinder the wider adoption of quantum computing within biological research, promising a new era of precision medicine and therapeutic innovation.

Central to the platform’s functionality is the innovative use of bit-bit encoding, a method for representing data in a format suitable for quantum computers, enhancing the expressive power of the machine learning models. This technique allows for a more nuanced representation of biological variables compared to traditional methods. Researchers also prioritise optimizer-free training, reducing reliance on traditional optimisation algorithms – iterative processes used to find the best model parameters – and simplifying the model training process, while sub-net initialisation further improves performance and scalability by strategically setting initial model parameters. These techniques collectively address the challenges of applying quantum algorithms to large, complex biological datasets, unlocking new insights into the complexities of life.

The platform’s utility demonstrates through two initial use cases: enhanced classification of phenotypic states – observable characteristics of an organism – from molecular variables and prediction of temporal evolution within biological systems. By integrating quantum and classical computing, this research offers a promising avenue for advancing our understanding of complex diseases and accelerating the development of targeted therapies.

Future work centres on expanding the platform’s capabilities and broadening its application to diverse biological challenges. A key area of focus involves scaling the quantum algorithms to accommodate larger and more complex datasets, thereby enhancing the platform’s predictive power. Further investigation into novel data encoding strategies will also be crucial, optimising the representation of biological information for quantum processing.

The researchers intend to explore the platform’s utility in drug discovery, specifically in predicting molecular properties and optimising drug-target interactions. Additionally, they plan to apply the platform to network medicine, modelling biological systems as complex networks and identifying potential therapeutic targets. This will involve integrating multi-omic data to create comprehensive cellular models, accelerating the development of targeted therapies.

A significant avenue for future development lies in the integration of variational quantum algorithms (VQAs) and quantum generative models. VQAs utilise classical optimisers to train quantum circuits, offering a pathway to tackle classically intractable problems. Quantum generative models, inspired by machine learning techniques, can generate new data points with similar characteristics to the training data, potentially accelerating drug design and generating novel molecules with desired properties. Furthermore, the team aims to develop open-source tools and resources to facilitate wider adoption of the platform within the biological research community, promoting collaboration and facilitating further progress.

Ultimately, this research establishes a foundation for leveraging the power of quantum computing to address fundamental challenges in biology and medicine. By bridging the gap between quantum computation and biological modelling, this platform paves the way for a new era of precision medicine and therapeutic innovation, unlocking new insights into the complexities of life.