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As quantum computing advances, there is a growing need for sophisticated computational methods that efficiently leverage quantum resources. This paper investigates the integration of adaptive quantum circuits with the Variational Quantum Eigensolver (VQE) algorithm, proposing Adaptive VQE as an enhanced approach for dynamically constructing quantum ansätze.
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Quantum computing holds the promise of revolutionizing the way we solve complex problems by harnessing the principles of quantum mechanics. However, current noisy intermediate-scale quantum (NISQ) computers face significant limitations due to their small number of qubits and high error rates, making it challenging to execute large quantum circuits that require greater depth, size, or width.
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This dataport will be useful to those interested in visual design for complex physics phenomena. We have included two quantum physics data sample datasets and our empirical study results.
(1) evaluation results from two experiments (20 participants in each and 40 in total) to empirically validate that separable bivariate pairs of large-magnitude-range vector
magnitude representations are more efficient than integral pairs.
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Kinaesthetic data collected by Chai3D
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The dataset includes three files collected from the the CAN bus of a moving vehicle. They are:
1- CAN log with no injection of fabricated messages
2- CAN log with injection of fabricated RPM reading messages
3- CAN log with injection of fabricated speed reading messages
The description of the data collection process is available in this paper:
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The dataset includes three files collected from the the CAN bus of a moving vehicle. They are:
1- CAN log with no injection of fabricated messages
2- CAN log with injection of fabricated RPM reading messages
3- CAN log with injection of fabricated speed reading messages
The description of the data collection process is available in this paper:
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This is a simple batch of data sets of points containing only integer attributes. The data sets were generated with a randomly correlated data set generator (DOI: 10.13140/RG.2.2.34866.43200).
This batch includes a total of 12 data sets which can be used to validate implementations of clustering algorithms such as k-nearest neighbours, or k-means.
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