Sold by: Mphasis
Deployed on AWS
Quantum simulator based solution for selecting optimum locations for delivery points.
Overview
This solution solves a problem where physical locations need to be equipped with services or products to satisfy demand within a certain area, while minimizing costs. For example, selecting branch and ATM sites in banking domain, selecting locations for CCTVs or Air Quality sensors in cities, etc. For large instances(number of locations) this problem cannot be optimally solved classically as it becomes computationally complex and time-consuming. Simulated Quantum Annealing provides a better alternative to classical methods by drastically reducing the computing time while minimizing cost. It delivers the required parallelization to explore many possible combinations simultaneously.
Highlights
- This solution helps to solve facility location problems where multiple services or products need to be equipped at certain locations to satisfy demand while minimizing cost.
- This solution is applicable across various industries like logistics, retail, banking, healthcare, etc. and can be modified according to the problem at hand.
- Need customized Quantum Computing solutions? Get in touch!
Details
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Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug fixes and performance improvement.
Additional details
Inputs
- Summary
Rows must be arranged in the manner as explained by following example. service location point opening_cost equip_cost 0 0 30 4807 457 0 0 48 4396 380 0 1 11 4029 0 1 14 4671 0 1 19 4323 ... | 1 | 2 | 13 | | 1 | 2 | 17 | ...
Service 0 can be equipped at location 0 satifying demand points 30 and 48. Service 0 can be equipped at location 1 satifying demand points 11, 14 and 19. Service 1 can be equipped at location 2 satifying demand points 13 and 17.
- Input MIME type
- text/csv
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Location-Selector/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Location-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 opening costs for locations 0 to 49 (for a problem with 50 locations) | Type: Categorical
Allowed values: 1,2,3,4,5 | Yes |
equip_costs | contains equipping costs for services 0 and 1 (for a problem with 2 services). | Type: Continuous | Yes |
Resources
Vendor resources
Support
Vendor support
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