{"doi":"10.1109/ipdpsw59300.2023.00087","title":"Q-CASA Invited Speakers Quantum-Centric Supercomputing Strategies for Neuroscience problems: Challenges and Progress","abstract":"For decades high-performance computing systems have allowed researchers, scientists and engineers to solve complex data- and compute-intensive problems. These parallel and distributed computing systems provided researchers to develop state of the art solutions in areas such as machine learning, health, and life science. While successful for a large number of problems, these systems face limitations when handling intractable problems which require either an enormous number of resources (i.e. number of bits, or power) or time to solve a problem (i.e. factoring integers). In this work we describe how quantum computers can enhance these systems by incorporating quantum computational principles to create a quantum-centric supercomputer. Recent developments include quantum middleware, error mitigation, and error suppression techniques to curtail these issues in near-term quantum devices and enable researchers to create quantum useful applications without having to wait for full fault-tolerant quantum computers. We also give an overview of dynamic circuits, circuit knitting, and various error suppression techniques recently developed to help minimize noise and execute quantum circuits on multiple quantum systems. Our preliminary work shows recent challenges and advancements in quantum-centric supercomputing when trying to solve neuroscience problems. We focus on functional MRI (fMRI) and diffusion MRI problems related to mental health and the potential areas where quantum computers can alleviate some of these challenges. We also present preliminary solutions to connectomics, tractography, and brain network analysis that can be used to help researchers identify neurodegenerative diseases or injuries.","journal":null,"year":2023,"id":410061,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9546,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":109046,"name":"Fahad Saeed","orcid":"0000-0002-3410-9552","position":1,"is_corresponding":false},{"id":1189866,"name":"Robert Loredo","orcid":"0000-0001-7231-1068","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T01:21:31.144858Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}