Much of recent progress in geophysics can be attributed to the adaptation of heterogeneous high-performance computing architectures. It is projected that the next major leap in many areas of science, and hence hopefully in geophysics too, will be due to the emergence of quantum computers. Finding a right combination of hardware, algorithms and a use-case, however, proves to be a very challenging task – especially when looking for a relevant application that scales efficiently on a quantum computer and is difficult to solve using classical means. We show that maximizing stack-power for residual statics correction, an NP-hard combinatorial optimization problem, appears to naturally fit a particular type of quantum computing known as quantum annealing. We express the underlying objective function as a quadratic unconstrained binary optimization, which is a quantum-native formulation of the problem. We choose some solution space, and define a proper encoding to translate the problem-variables into qubit states. We show that these choices can have a significant impact on the maximum problem size that can fit on the quantum annealer and on the fidelity of the final result. To improve the latter, we embed the quantum optimization step in a hybrid classical-quantum workflow, which aims to increase the frequency of finding the global, rather than some local, optimum of the objective function. Lastly, we show that a generic, black-box, hybrid classical-quantum solver could also be used to solve stack-power maximization problems proximal to industrial relevance, and capable to surpassing deterministic solvers prone to cycle-skipping. A custom-build workflow capable of solving larger problems with an even higher robustness and greater control of the user appears to be within reach in the very near future.

Background. Immune effector cell-associated neurotoxicity syndrome (ICANS) is a relatively common consequence of chimeric antigen receptor T-cell (CAR-T) therapy, with a wide range of possible cognitive presentations. The aim of this study was to characterise a real-word cognitive and psychological status of patients with advanced haematologic and solid organ malignancies planned for CAR-T. We also aimed to examine utility of two cognitive screening approaches. Methods. Patients underwent specialist cognitive assessment, including a self-report questionnaire of psychopathology and subjective cognitive function. A subset of individuals also completed the Montreal Cognitive Assessment (MoCA). Results. Of 60 patients included, 15-16 (25%-27%) presented with evidence of cognitive impairment, with six unique patterns of dysfunction. Impaired patients were more likely to have B-cell acute lymphoblastic leukaemia (BF10=9.30), be younger (BF10=7.76), have bone marrow involvement (BF10=5.18), report history of anxiety (BF10=4.85), or have evidence of psychopathology (BF10=31.30). Analyses did not support the utility of cognitive screening. Of those patients who completed a self-report measure of psychopathology, nine (15.8%) were elevated on at least one symptom domain. Conclusions. The findings demonstrate a broad spectrum of dysfunction and psychopathology in this cohort, emphasising the importance of baseline evaluation for detecting cognitive neurotoxicity symptoms that might arise after CAR-T infusion.

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Quantum computing could be a potential game-changer in industry sectors relying on the efficient solutions of large-scale global optimization problems. Exploration geoscience, is full of optimization problems and hence is a good candidate for application of quantum computing. It was recently suggested that quantum annealing, a form of adiabatic quantum computer, is a much better suited quantum computing platform for optimization problems than gate-based quantum computing. In this work, we show how the residual statics estimation problem can be solved on the quantum annealer and present our first results obtained on a quantum computer.