A quantum processor comprises a first set of qubits comprising a first plurality of substantially parallel qubits; a second set of qubits comprising N successive groups of a plurality of qubits (1, 2, . . . , N), wherein N is greater than or equal to two; wherein each group of qubits comprises a plurality of substantially parallel qubits; wherein each qubit of the first plurality of substantially parallel qubits of the first set of qubits crosses substantially perpendicularly a portion of the plurality of substantially parallel qubits of a first group of the second set of qubits; wherein each qubit of any given group of the second set of qubits crosses substantially perpendicularly a portion of the plurality of substantially parallel qubits of a successive group of the second set of qubits and a plurality of couplers, each coupler for providing a communicative coupling at a crossing of two qubits.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A quantum processor comprising: N successive groups of a plurality of qubits (1, 2, . . . , N), wherein N is greater than or equal to three; wherein each group of qubits of the N successive groups of a plurality of qubits comprises a plurality of substantially parallel qubits; wherein each qubit of a first group of the N successive groups of a plurality of qubits is sized and shaped so that it crosses substantially perpendicularly a portion of each qubit of only a second group of qubits of the N successive groups of a plurality of qubits; wherein each qubit of a last group of the N successive groups of a plurality of qubits is sized and shaped so that it crosses substantially perpendicularly a portion of each qubit of only a second to last group of the N successive groups of a plurality of qubits; wherein each qubit of any given group of the N-2 successive groups of a plurality of qubits, not including the first group and the last group, is sized and shaped so that it crosses substantially perpendicularly a portion of each qubit of only a corresponding successive group and a corresponding preceding group of the N successive groups of a plurality of qubits; and a plurality of couplers, each coupler for providing a communicative coupling at a crossing of two qubits. 2. The quantum processor as claimed in claim 1, wherein the quantum processor is used for implementing a neural network comprising a plurality of neurons and a plurality of synapses; wherein each neuron of the plurality of neurons is associated to a qubit and each synapse of the plurality of synapses is associated to a coupler of the quantum processor. 3. A method for training the neural network implemented in the quantum processor claimed in claim 2, the method comprising: providing initialization data for initializing the plurality of couplers and the plurality of qubits of the quantum processor; until a criterion is met: performing a quantum sampling of the quantum processor to provide first empirical means; obtaining at least one training data instance for training the neural network; performing a quantum sampling of the quantum processor; wherein no bias is assigned to the qubits of the first group of the N successive groups of a plurality of qubits of the quantum processor; wherein the couplings of the first group of qubits of the N successive groups of a plurality of qubits and the second group of the N successive groups of a plurality of qubits are switched off; further wherein the biases of the second group of qubits of the N successive groups of a plurality of qubits are altered using the biases on a first group of neurons associated with the first group of qubits, the weights of the switched off couplings, and the at least one training data instance, to determine second empirical means; updating corresponding weights and biases of the couplers and the qubits of the quantum processor using the first and second empirical means; and providing final weights and biases of the couplers and the qubits of the quantum processor indicative of data representative a trained neural network. 4. The method as claimed in claim 3, wherein the initialization data comprise a plurality of biases, each for a qubit of the plurality of qubits; a plurality of weights, each weight for a coupler of the plurality of couplers and a learning rate schedule. 5. The method as claimed in claim 4, wherein the providing of the initialization data is performed using an analog computer comprising the quantum processor and a digital computer operatively connected to the analog computer. 6. The method as claimed in claim 3, wherein the at least one training data instance is obtained from a previously generated data set. 7. The method as claimed in claim 3, wherein the at least one training data instance is obtained from a real-time source. 8. The method as claimed in claim 6, wherein the generated data set is stored in a digital computer operatively connected to an analog computer comprising the quantum processor. 9. The method as claimed in claim 7, wherein the real-time source originates from a digital computer operatively connected to an analog computer comprising the quantum processor. 10. The method as claimed in claim 3, wherein the criterion comprises a stopping condition; wherein the stopping condition comprises determining if there is no further training data instance available. 11. The method as claimed in claim 5, wherein the digital computer comprises a memory; further wherein the providing of the final weights and biases of the couplers and the qubits of the quantum processor comprises storing the final weights and biases of the couplers and the qubits of the quantum processor in the memory of the digital computer. 12. The method as claimed in claim 5, wherein the providing of the final weights and biases of the couplers and the qubits of the quantum processor comprises providing the final weights and biases of the couplers and the qubits of the quantum processor to another processing unit operatively connected to the digital computer. 13. A digital computer comprising: a central processing unit; a display device; a communication port for operatively connecting the digital computer to an analog computer comprising a quantum processor used for implementing a neural network as claimed in claim 2; a memory unit comprising an application for training the neural network, the application comprising: instructions for providing initialization data for initializing the plurality of couplers and the plurality of biases of the qubits of the quantum processor; instructions for, until a criterion is met: performing a quantum sampling of the quantum processor to provide first empirical means; obtaining at least one training data instance for training the neural network; performing a quantum sampling of the quantum processor; wherein no bias is assigned to the qubits of the first group of the N successive groups of a plurality of qubits of the quantum processor; wherein the couplings of the first group of qubits of the N successive groups of a plurality of qubits and the second group of qubits of the N successive groups of a plurality of qubits are switched off; further wherein the biases of the second group of qubits of the N successive groups of a plurality of qubits are altered using the biases on a first group of neurons associated with the first group of qubits, the weights of the switched off couplings, and the at least one training data instance, to determine second empirical means; and updating corresponding weights and biases of the couplers and the qubits of the quantum processor using the first and the second empirical means; and instructions for providing final weights and biases of the couplers and the qubits of the quantum processor as data representative of a trained neural network. 14. A non-transitory computer readable storage medium for storing computer-executable instructions which, when executed, cause a digital computer to perform a method for training the neural network implemented in the quantum processor claimed in claim 2, the method comprising: providing initialization data for initializing the plurality of couplers and the plurality of qubits of the quantum processor; until a criterion is met: performing a quantum sampling of the quantum processor to provide first empirical means; obtaining at least one training data instance for training the neural network; performing a quantum sampling of the quantum processor; wherein no bias is assigned to the qubits of the first group of the N successive groups of a plurality of qubits of the quantum processor; wherein the couplings of the first group of qubits of the N successive groups of a plurality of qubits and the second group of qubits of the N successive groups of a plurality of qubits are switched off; further wherein the biases of the second group of qubits of the N successive groups of a plurality of qubits are altered using the biases on a first group of neurons associated with the first group of qubits, the weights of the switched off couplings, and the at least one training data instance, to determine second empirical means; and updating corresponding weights and biases of the couplers and the qubits of the quantum processor using the first and second empirical means; and providing final weights and biases of the couplers and the qubits of the quantum processor indicative of data representative a trained neural network. 15. The method as claimed in claim 2, wherein the neural network is a Deep Boltzmann Machine.