Where should the quantum component sit so that the entire experiment becomes more useful?
The research thesis
My proposed approach starts with a scientific bottleneck and treats CPU, GPU and quantum processors as complementary resources. The task is to identify a small quantum subproblem whose contribution can be measured inside the larger computation.
A promising circuit is only one part of the experiment. Data preparation, optimization, repeated measurements and interpretation belong in the same accounting. This creates a concrete bridge between scientific computing and quantum algorithm design.
A first experiment
Choose one public or synthetic scientific workload. Define its classical reference, input size and required precision before introducing a quantum component. Compare an exact small simulator, a suitable approximate simulator and hardware where available.
The pilot would record circuit depth, shot budget, optimizer evaluations, transfer and queue time, final accuracy and total elapsed time. A useful result includes the conditions under which the classical method remains preferable.
What a partner would receive
A workload definition, a reproducible comparison and a technical report identifying the most informative next experiment. The proposed collaboration is suitable for scientific software teams and laboratories evaluating a specific quantum integration.
Scientific connection
This direction extends my work in scientific computation and systems architecture. High-energy physics offers a relevant setting for hybrid algorithms, distributed infrastructure and demanding reproducibility requirements; CERN QTI provides public examples of this research landscape.
Sources & scientific context
Author
Maurizio VivianiIndependent research · Robotics
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