Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease

Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well

Projective Quantum Eigensolver via Adiabatically Decoupled Subsystem Evolution: a Resource Efficient Approach to Molecular Energetics in Noisy Quantum Computers

Quantum computers hold immense potential in the field of chemistry, ushering new frontiers to solve complex many body problems that are beyond the reach of classical computers. However, noise in the current quantum hardware limits their applicability to large chemical systems. This work encompasses the development of a projective formalism that aims to compute ground-state energies of molecular systems accurately using Noisy Intermediate Scale Quantum (NISQ) hardware in a resource efficient manner. Our approach is reliant upon the formulation of a bipartitely decoupled parameterized ansatz within the disentangled unitary coupled cluster (dUCC) framework based on the principles of synergetics. Such decoupling emulates the total parameter optimization in a lower dimensional manifold, while a mutual synergistic relationship among the parameters is exploited to ensure characteristic accuracy. Without any pre-circuit measurements, our method leads to a highly compact fixed-depth ansatz with shallower circuits and fewer expectation value evaluations. Through analytical and numerical demonstrations, we demonstrate the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems. This approach enables rapid exploration of emerging chemical spaces by efficient utilization of near-term quantum hardware resources.Comment: 14 pages, 3 figure