Sold by: Mphasis
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Quantum simulator based solution to select optimum locations for Covid-19 Vaccination centers based on requirement in affected areas.
Overview
Covid-19 vaccines must be readily available in areas most affected by the pandemic. While setting up vaccination centers it is very important to select locations which can fulfill the demand from the affected areas. Covid Vaccination center locator selects optimal locations for the centers from a given set of locations satisfying demand while minimizing cost. The solution works using Quantum Optimization Algorithms to help design the network of centers from a given locations. As compared to classical methods of optimization, it uses Simulated Quantum Annealing to lower the distribution/operations cost and compute time.
Highlights
- This solution helps to find optimum locations for vaccination centers for Covid-19 vaccines by fulfilling demand from affected areas while minimizing cost.
- This solution is applicable across various industries like healthcare, logistics, retail, banking, etc. and can be modified according to the problem at hand.
- Need customized Quantum Computing solutions? Get in touch!
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $40.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $40.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $40.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $40.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $40.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $40.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $40.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $40.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $40.00 |
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Delivery details
Amazon SageMaker model
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Additional details
Inputs
- Summary
Rows must be arranged in the manner as explained by following example. location point opening_cost equip_cost 0 30 4807 457 0 48 4396 1 11 4029 1 14 4671 1 19 4323 ... | 2 | 13 | | 2 | 17 | ...
Center can be built at location 0 satifying demand points 30 and 48. Center can be built at location 1 satifying demand points 11, 14 and 19. Center can be built at location 2 satifying demand points 13 and 17. Columns 'opening_costs' and 'equip_costs' are unrelated to first 2 columns.
- Limitations for input type
- Input file should be a csv file with not more then 200 datapoints. File size should not exceed 300 KB
- Input MIME type
- text/csv
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Vaccine-Site-Selector/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Vaccine-Site-Selector/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
opening_costs | contains costs to build Centers at locations 0 to 49 (for a problem with 50 locations). | Type: Categorical
Allowed values: 1,2,3,4,5 | Yes |
equip_costs | contains cost of readying a Center for vaccination. | Type: Integer | Yes |
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