IBM at IEEE Quantum Week

IBM Speaker: Kate Marshall

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IBM Speaker: Kevin Sung

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IBM Speaker: Juan Cruz Benito

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IBM Speaker: Stefan Wörner

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IBM Speaker: David Kremer, Victor Villar, Juan Cruz Benito

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IBM Speaker: Genya Crossman, Alexandre Choquette, Gavin Jones, Hiroshi Horii, Stefan Wörner, Antonio Mezzacapo, Ivano Tavernelli, Francesco Tacchino, Christa Zoufal

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IBM Speaker: Ian Hincks

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IBM Speaker: Eddie Schoute

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IBM Speaker: Jay Gambetta

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IBM Speaker: Genya Crossman, Alexandre Choquette, Gavin Jones, Hiroshi Horii, Stefan Wörner, Antonio Mezzacapo, Ivano Tavernelli, Francesco Tacchino, Christa Zoufal

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IBM Speaker: Tim Lindquist

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IBM Speaker: Caleb Johnson, Kevin Sung

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IBM Speaker: Gavin Jones

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IBM Speaker: Nicolas Dupuis

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IBM Speaker: Stefan Wörner

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IBM Speaker: Alexandre Choquette

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IBM Speaker: Tristan Müller

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Elucidating mRNA secondary structure is crucial for advancing mRNA synthesis, translation regulation, and therapeutic innovation, yet it faces formidable computational challenges. This study introduces a quantum-centric approach that significantly enhances the computational feasibility of predicting mRNA secondary structures. By effectively balancing tasks between quantum processors and classical computing nodes, our methods utilize a quantum sampling-based paradigm to arrive at the optimal solution. Here, we advance our previous work by integrating a variational CVaR-based quantum algorithm with a novel noise mitigation and problem reduction strategy. We further validate the scalability of this approach on a tensor-network simulator for problems with up to 358 qubits, demonstrating its robustness without noise. Additionally, we utilize Instantaneous Quantum Polynomial circuits, which facilitate classical computations of expectations to guide the training of variational circuits and also aid in error mitigation. These circuits support computationally intensive sampling tasks on quantum hardware. Our scheme is complemented by a lightweight classical local search that refines these samples, thereby increasing the likelihood of identifying optimal solutions. We present results that validate both techniques on IBM quantum processors by conducting hardware experiments that require up to 156 qubits and 950 nonlocal gates, effectively solving mRNA sequences of up to 60 nucleotides. These advancements not only demonstrate the potential of quantum workflows to solve complex computational problems in biology but also pave the way for groundbreaking applications in bioinformatics and therapeutic development.

Authors: Vaibhaw Kumar, Dimitris Alevras, Mihir Metkar, Eline Welling, Chris Cade, Ido Niesen, Triet Friedhoff, Jae-Eun Park, Saurabh Shivpuje, Mariana LaDue, Wade Davis and Alexey Galda

This talk is part of the session TP69::QAPP Quantum Computing for Biological systems and Biomedical Data.

IBM Speaker: Ritajit Majumdar, Nate Earnest-Noble

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Despite the amount of omics data generated in the last decade, the low data regime remains a significant challenge in healthcare, particularly in clinical trials and the study of rare diseases. In this study, we explore the application of quantum machine learning techniques to address the challenges posed by low data availability in healthcare. Leveraging the power of quantum computing, we focus on efficient selection of relevant feature and embedding, reducing the model complexity (and number of qubit needed) to enhance the performance even with limited data. In our case study, we used RNA data from epilepsy patients demonstrating how the predictive performance of QML models varies significantly depending on the feature selection approach employed, with noticeable improvements over classical counter part when domain knowledge is integrated to guide the selection process.

Authors: Emre Sahin, Matthew Madgwick, James Strudwick, Vivek Sehra, Oscar Wallis, Hamed Javidi Mostafapour Boshroyeh, Anna Paola Carrieri, Laxmi Parida, Lara Jehi, Stefano Mensa and Filippo Utro

This talk is part of the session TP84::QML Data-Efficient and Uncertainty-Aware QML.