Abstract: Model-free error correction in quantum processors is provided, allowing tailoring to individual devices. In various embodiments, a quantum circuit is configured according to a plurality of configuration parameters. The quantum circuit comprises an encoding circuit and a decoding circuit. Each of a plurality of training states is input to the quantum circuit. The encoding circuit is applied to each of the plurality of training states and to a plurality of input syndrome qubits to produce encoded training states. The decoding circuit is applied to each of the encoded training states to determine a plurality of outputs. A fidelity of the quantum circuit is measured for the plurality of training states based on the plurality of outputs. The fidelity is provided to a computing node. The computing node determines a plurality of optimized configuration parameters. The optimized configuration parameters maximize the accuracy of the quantum circuit for the plurality of training states.

Abstract: A method evolves a lattice of qubits in a quantum computer. The lattice of qubits includes a first plurality of qubits and a second plurality of qubits in the quantum computer. Each qubit in the first plurality of qubits is adjacent to at least one qubit in the second plurality of qubits. The method includes: (A) applying, in parallel, a first set of quantum gates between the first plurality of qubits and the second plurality of qubits to create a first set of entangled pairs of qubits; (B) after (A), swapping, in parallel, pairs of qubits, the swapping comprising: (B) (1) swapping pairs of adjacent qubits in the first plurality of qubits according to a first swap criterion; and (B) (2) swapping pairs of adjacent qubits in the second plurality of qubits according to a second swap criterion, wherein the second swap criterion differs from the first swap criterion.

Abstract: A hybrid quantum-classical (HQC) computing system, including a quantum computing component and a classical computing component, computes the inverse of a Boolean function for a given output. The HQC computing system translates a set of constraints into interactions between quantum spins; forms, from the interactions, an Ising Hamiltonian whose ground state encodes a set of states of a specific input value that are consistent with the set of constraints; performs, on the quantum computing component, a quantum optimization algorithm to generate an approximation to the ground state of the Ising Hamiltonian; and measures the approximation to the ground state of the Ising Hamiltonian, on the quantum computing component, to obtain a plurality of input bits which are a satisfying assignment of the set of constraints.

Abstract: A method for training an adversarial generator from a data set and a classifier includes: (A) training a classical noise generator whose input includes an output of a quantum generator, the classical noise generator having a first set of parameters, the training comprising: sampling from the data set to produce a first sample and a first corresponding label for the first sample; producing an output of the classical noise generator based on the output of the quantum generator and the first sample; producing a noisy example based on the output of the classical noise generator and the first sample; providing the noisy example to the classifier to produce a second corresponding label for the first sample; updating the first set of parameters such that the first corresponding label of the first sample differs from the second corresponding label of the first sample.

Abstract: A data router receives data from a data source and stores the data in a buffer of the data router. The data router analyzes the data in the buffer to identify the data source. The data router uses a routing map to identify a destination for the data based on the data source and streams the data from the buffer to the destination.

Abstract: A method for training an adversarial generator from a data set and a classifier includes: (A) training a classical noise generator whose input includes an output of a quantum generator, the classical noise generator having a first set of parameters, the training comprising: sampling from the data set to produce a first sample and a first corresponding label for the first sample; producing an output of the classical noise generator based on the output of the quantum generator and the first sample; producing a noisy example based on the output of the classical noise generator and the first sample; providing the noisy example to the classifier to produce a second corresponding label for the first sample; updating the first set of parameters such that the first corresponding label of the first sample differs from the second corresponding label of the first sample.

Abstract: Embodiments of the present invention are directed to a hybrid quantum-classical (HQC) computer which includes a classical computer and a quantum computer. The HQC computer may perform a method in which: (A) the classical computer starts from a description of a initial problem and transforms the initial problem into a transformed problem of estimating an expectation value of a function of random variables; (B) the classical computer produces computer program instructions representing a Bayesian phase estimation scheme that solves the transformed problem; and (C) the hybrid quantum-classical computer executes the computer program instructions to execute the Bayesian phase estimation scheme, thereby producing an estimate of the expectation value of the function of random variables.

Abstract: A hybrid quantum classical (HQC) computing system, including a quantum computing component and a classical computing component, computes the inverse of a Boolean function for a given output. The HQC computing system translates a set of constraints into interactions between quantum spins; forms, from the interactions, an Ising Hamiltonian whose ground state encodes a set of states of a specific input value that are consistent with the set of constraints; performs, on the quantum computing component, a quantum optimization algorithm to generate an approximation to the ground state of the Ising Hamiltonian; and measures the approximation to the ground state of the Ising Hamiltonian, on the quantum computing component, to obtain a plurality of input bits which are a satisfying assignment of the set of constraints.

Abstract: Model-free error correction in quantum processors is provided, allowing tailoring to individual devices. In various embodiments, a quantum circuit is configured according to a plurality of configuration parameters. The quantum circuit comprises an encoding circuit and a decoding circuit. Each of a plurality of training states is input to the quantum circuit. The encoding circuit is applied to each of the plurality of training states and to a plurality of input syndrome qubits to produce encoded training states. The decoding circuit is applied to each of the encoded training states to determine a plurality of outputs. A fidelity of the quantum circuit is measured for the plurality of training states based on the plurality of outputs. The fidelity is provided to a computing node. The computing node determines a plurality of optimized configuration parameters. The optimized configuration parameters maximize the accuracy of the quantum circuit for the plurality of training states.

Abstract: A system for determining vehicle loading and unloading comprises an acceleration detector provided on a vehicle, a location detector for detecting the location of the vehicle and a processor that receives information detected by the acceleration detector and location detector and determines whether the detected acceleration is due to vehicle loading, unloading or travel, based on the received information. A timer may be started, when vehicle loading or unloading is determined, such that the amount of time spent loading or unloading the vehicle may be obtained. Also, a condition of a traveled surface may be determined based on information received from the acceleration detector and location detector.

Abstract: The system contains a central computer unit having a first database controlled by a first logic unit. At least one mobile computer unit is joined with the mobile equipment. Each of the mobile computer units has a second database controlled by a second logic unit. At least one wireless communication device enables communication between said first logic unit and said second logic unit, wherein said mobile computer unit is operable independent of the first logic unit.

Abstract: A system for determining vehicle loading and unloading comprises an acceleration detector provided on a vehicle, a location detector for detecting the location of the vehicle and a processor that receives information detected by the acceleration detector and location detector and determines whether the detected acceleration is due to vehicle loading, unloading or travel, based on the received information. A timer may be started, when vehicle loading or unloading is determined, such that the amount of time spent loading or unloading the vehicle may be obtained. Also, a condition of a traveled surface may be determined based on information received from the acceleration detector and location detector.

Abstract: The system contains a central computer unit having a first database controlled by a first logic unit. At least one mobile computer unit is joined with the mobile equipment. Each of the mobile computer units has a second database controlled by a second logic unit. At least one wireless communication device enables communication between said first logic unit and said second logic unit, wherein said mobile computer unit is operable independent of the first logic unit.

Abstract: The system contains a central computer unit having a first database controlled by a first logic unit. At least one mobile computer unit is joined with the mobile equipment. Each of the mobile computer units has a second database controlled by a second logic unit. At least one wireless communication device enables communication between said first logic unit and said second logic unit, wherein said mobile computer unit is operable independent of the first logic unit.

Abstract: A distributed mine management system contains a central computer having a first database controlled by a first controller. At least one mobile computer is in communication with a piece of mobile equipment. Each of the mobile computers has a second database controlled by a second controller. The mobile computers store high, medium and low priority data about the status of the mobile equipment in the second database according to the priority of the information. The mine management system includes a remote worksite computer and an intermittent communication path between the remote worksite computer and the central computer. A wireless communication network enables communication between the first controller and the second controller, wherein said mobile computer is operable independent of the first logic unit.

Abstract: A Dynamically Reconfigurable Dynamic Wireless Network for connecting a local area network (“LAN”) to wireless Mobile Stations. Backbone Access Points (“Backbone APs”) are physically connected to the LAN. Levels of Wireless Access Points (“Wireless APs”) are daisy-chained together and connected to the Backbone AP, providing an extended area of network coverage. Mobile stations are connected to either Backbone APs or Wireless APs. Dynamic Reconfiguration prevents single point failures. Each AP contains a router, Address Resolution Protocol (“ARP”) cache, and Distributed Routing Table (“DR Table”). The DR Table maintains the Media Access Control (“MAC”) address and the Internet Protocol (“IP”) address of each AP below it in the Distributed Routing Tree. Additionally, each DR Table also maintains the IP address for the device each AP is connected. The Distributed Routing Tree is dynamically reconfigured to minimize transmission hops or to maximize signal strength between Mobile Stations and the LAN.

Abstract: A Dynamically Reconfigurable Dynamic Wireless Network for connecting a local area network (“LAN”) to wireless Mobile Stations. Backbone Access Points (“Backbone APs”) are physically connected to the LAN. Levels of Wireless Access Points (“Wireless APs”) are daisy-chained together and connected to the Backbone AP, providing an extended area of network coverage. Mobile stations are connected to either Backbone APs or Wireless APs. Dynamic Reconfiguration prevents single point failures. Each AP contains a router, Address Resolution Protocol (“ARP”) cache, and Distributed Routing Table (“DR Table”). The DR Table maintains the Media Access Control (“MAC”) address and the Internet Protocol (“IP”) address of each AP below it in the Distributed Routing Tree. Additionally, each DR Table also maintains the IP address for the device each AP is connected.

Abstract: A slope monitoring device includes a cable anchored at one end to the sloped wall of an open pit mine, dump, or the like. The cable extends over an idler pulley at the top of the sloped wall and passes around a further pulley coupled to an optical shaft encoder which generates a digital signal corresponding to the angular position of the pulley. The cable is engaged by a clutch and is wound about a play-off reel, each secured to a weighted sleeve slidingly extending about a vertical post. The weighted sleeve maintains tension upon the cable, while the clutch allows additional cable to be played off of the reel when tension is increased due to movement of the wall. The shaft encoder information, along with the temperature, battery condition, and cable condition, are transmitted by radio to a remote location for processing.