Aspects of the disclosure provide a method for configuring a Quantum Annealing (QA) device. Then QA device has a plurality of qubits and a plurality of couplers at overlapping intersections of the qubits. The method includes mapping a node of a neural network that have a plurality of nodes and connections between the nodes to a qubit in the QA device, and mapping a connection of the neural network to a coupler at an intersection in the QA device where two qubits corresponding to two nodes connected by the connection intersect. The method further includes mapping a node of the neural network to a chain of qubits. In an embodiment, a coupling between qubits in the chain is configured to be a ferromagnetic coupling in order to map the node of the neural network to the chain of qubits.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for configuring a Quantum Annealing (QA) device, the QA device having a plurality of qubits and a plurality of couplers at overlapping intersections of the qubits, the method comprising: mapping a node of a neural network that have a plurality of nodes and connections between the nodes to a qubit in the QA device; and mapping a connection of the neural network to a coupler at an intersection in the QA device where two qubits corresponding to two nodes connected by the connection intersect. 2. The method of claim 1, further comprising: mapping a node of the neural network to a chain of qubits. 3. The method of claim 2, wherein mapping the node of the neural network to the chain of qubits includes: configuring a coupling between qubits in the chain to be a ferromagnetic coupling. 4. The method of claim 1, wherein the neural network is a deep learning neural network. 5. The method of claim 1, further comprising: configuring a coupler associated with a faulty qubit in the QA device with a zero weight; and setting a connection associated with a node in the neural network that is mapped to the faulty qubit with a zero weight. 6. The method of claim 2, further comprising: discarding quantum samples that include states of qubits in a chain of qubits that disagree with each other when a sample average is computed. 7. The method of claim 2, further comprising: using a state value of majority qubits that agree with each other in a chain of qubits including a faulty qubit as a state value of the chain of qubits in a quantum sample when a percentage of qubits in each chain of qubits that agree is greater than a voting threshold parameter in the quantum sample. 8. The method of claim 1, further comprising: applying a gauge transformation to qubits of the QA device. 9. The method of claim 8, wherein the gauge transformation is a basket weave gauge transformation. 10. The method of claim 8, wherein applying a gauge transformation to qubits of the QA device includes: generating quantum samples from qubits in the QA device with multiple different gauge transformation arrangements; and averaging the quantum samples to calculate a model expectation. 11. The method of claim 10, wherein the multiple different gauge transformation arrangements includes one of: an identity transformation where no qubits are inverted; a basket weave gauge transformation where a first half of qubits in the QA device are inverted and a second half of qubits are not inverted; a complement of the above basket weave gauge transformation where the second half of the qubits in the QA device are inverted and the first half of the qubits are not inverted; and a negative of the identity transformation where all qubits are inverted. 12. The method of claim 1, further including: calibrating a scale factor βeff for generating quantum samples from a quantum annealing process. 13. The method of claim 12, wherein calibrating the scale factor βeff includes: constructing a restricted Boltzmann machine (RBM) of a particular size; choosing a particular value for the scale factor βeff; performing the quantum annealing process to generate the quantum samples using a quotient of an energy functional of the RBM being divided by the scale factor βeff as a final Hamiltonian; repeating choosing a particular value, performing a quantum annealing process for multiple times; and determining a value of the scale factor βeff that leads to the smallest difference between model expectations of the RBM based on the quantum samples and model expectations of the RBM based on the energy functional of the RBM. 14. The method of claim 13, wherein calibrating the scale factor βeff further includes: calculating model expectations of the RBM based on the quantum samples; calculating model expectations of the RBM based on the energy functional of the RBM; and comparing model expectations of the RBM based on the quantum samples with model expectations of the RBM based on the energy functional of the RBM. 15. A method for training a neural network using a quantum annealing (QA) device including qubits configured with biases and couplers configured with weights, where an original restricted Boltzmann machine (RBM) of one layer of the neural network is mapped onto the QA device that is configured to act as a quantum RBM, the method comprising: generating quantum samples at the QA device; calculating an update to biases and weights for the original RBM and the quantum RBM with a classical computer based on the quantum samples; and using the update to biases and weights to perform a next iteration of training the neural network. 16. The method of claim 15, further comprising: initializing the biases and the weights of the original RBM and the quantum RBM to random values. 17. The method of claim 15, wherein generating quantum samples at the QA device includes: using a quotient of an energy functional of the RBM being divided by the scale factor βeff as a final Hamiltonian for a quantum annealing process at the QA device; running the quantum annealing process for multiple times to generate multiple quantum samples. 18. The method of claim 15, wherein calculating the update to biases and weights for the original RBM and the quantum RBM includes: averaging multiple quantum samples to calculate a model expectation that is consequently used for calculating updates to the biases and weights. 19. The method of claim 15, wherein using the update to biases and weights to perform the next iteration of training the neural network includes: configuring the biases and the weights of the original RBM and the quantum RBM with values of the update to biases and weights for the next iteration of training the neural network; and repeating the steps of generating quantum samples, calculating an update to biases and weights, and using the update to biases and weights to perform the next iteration.