Abstract: Event-based replenishment simulation for an enterprise supply chain is described. On a per item, per node, per epoch basis, the simulation may generate a stream of action events based on forecasted demand, supply chain logic, and policy inputs that are applied to a current-run state of the supply chain in order to yield a stream of observation events. Requested metrics may be received, and the observation events may then be transformed to predict values for the metrics as output of the simulation. The simulation may be repeated for a given epoch using discrete demand values from a demand distribution, for a plurality of epochs, and/or across a plurality of items at a plurality of nodes. Resultantly, the simulation output can be used for predicting a future run-state of the supply chain across items and nodes.

Abstract: In some implementations, a method performed by data processing apparatuses includes receiving order data that represents a plurality of ordered items for delivery to a location; selecting a first policy from a store of first policies; transforming at least a portion of the order data into a plurality of item units, based on rules associated with the selected first policy; selecting a second policy from a store of second policies; for each item unit, based on rules associated with the selected second policy, modifying the item unit to include annotated information that corresponds to operations to be performed on the item unit; and generating instructions for grouping the plurality of item units for delivery to the location, based on the annotated information for the item units.

Abstract: Event-based replenishment simulation for an enterprise supply chain is described. On a per item, per node, per epoch basis, the simulation may generate a stream of action events based on forecasted demand, supply chain logic, and policy inputs that are applied to a current-run state of the supply chain in order to yield a stream of observation events. Requested metrics may be received, and the observation events may then be transformed to predict values for the metrics as output of the simulation. The simulation may be repeated for a given epoch using discrete demand values from a demand distribution, for a plurality of epochs, and/or across a plurality of items at a plurality of nodes. Resultantly, the simulation output can be used for predicting a future run-state of the supply chain across items and nodes.

Abstract: In some implementations, a method performed by data processing apparatuses includes receiving order data that represents a plurality of ordered items for delivery to a location; selecting a first policy from a store of first policies; transforming at least a portion of the order data into a plurality of item units, based on rules associated with the selected first policy; selecting a second policy from a store of second policies; for each item unit, based on rules associated with the selected second policy, modifying the item unit to include annotated information that corresponds to operations to be performed on the item unit; and generating instructions for grouping the plurality of item units for delivery to the location, based on the annotated information for the item units.

Abstract: In some implementations, a method performed by data processing apparatuses includes receiving order data that defines one or more orders for items to be transported, selecting a first combination of policies for a plurality of sub-processes, each policy representing a strategy for performing a respective sub-process included in an overall process for transporting the items, performing a first simulation based on the first selected policy combination, selecting a second, different combination of policies for the plurality of sub-processes, performing a second simulation based on the second selected policy combination, comparing results of the first simulation and results of the second simulation, and based on the comparison, selecting one of the first combination of policies or the second combination of policies as an optimized policy combination.

Abstract: This disclosure relates to preserving a quantum state in a quantum memory. A controller of the quantum memory determines based on a characteristic of noise that causes deterioration of the quantum state a dynamical decoupling base sequence. The duration of the base sequence is shorter than or equal to an access latency time of the quantum memory to allow access to the quantum state within the access latency time. Further, the deterioration of the quantum state is bounded to an upper deterioration limit when the base sequence is repeatedly applied to the quantum system. This provides acceptable access times while simultaneously allowing long term storage of data in the quantum state with low error rates. Repeatedly applying the base sequence to the quantum system will first yield an increasing deterioration but that deterioration will eventually reach the upper limit. As a result, over time the error rates will not exceed that upper limit and the quantum state is stable.

Abstract: This disclosure relates to preserving a quantum state in a quantum memory. A controller of the quantum memory determines based on a characteristic of noise that causes deterioration of the quantum state a dynamical decoupling base sequence. The duration of the base sequence is shorter than or equal to an access latency time of the quantum memory to allow access to the quantum state within the access latency time. Further, the deterioration of the quantum state is bounded to an upper deterioration limit when the base sequence is repeatedly applied to the quantum system. This provides acceptable access times while simultaneously allowing long term storage of data in the quantum state with low error rates. Repeatedly applying the base sequence to the quantum system will first yield an increasing deterioration but that deterioration will eventually reach the upper limit. As a result, over time the error rates will not exceed that upper limit and the quantum state is stable.