Quantum neural nets, which utilize quantum effects to model complex data sets, represent a major focus of quantum machine learning and quantum computing in general. In this application, example methods of training a quantum Boltzmann machine are described. Also, examples for using quantum Boltzmann machines to enable a form of quantum state tomography that provides both a description and a generative model for the input quantum state are described. Classical Boltzmann machines are incapable of this. Finally, small non-stoquastic quantum Boltzmann machines are compared to traditional Boltzmann machines for generative tasks, and evidence presented that quantum models outperform their classical counterparts for classical data sets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: configuring a quantum computer to implement a Boltzmann machine in accordance with a first set of Hamiltonian terms; computing gradient values for adjusting the first set of Hamiltonian terms using a positive-operator-value-measure-based approach; computing a second set of Hamiltonian terms based at least in part on the computed gradient values; and configuring the quantum computer to implement the Boltzmann machine in accordance with the second set of Hamiltonian terms, wherein the second set of Hamiltonian terms causes the Boltzmann machine to implement a second Hamiltonian that more likely reproduces a training set of data than a first Hamiltonian resulting from the first set of Hamiltonian terms. 2. The method of claim 1, wherein the computing the second set of Hamiltonian terms comprises multiplying the gradient values by a selected learning rate. 3. The method of claim 1, wherein the computing the gradient values comprises, for a respective Hamiltonian term, comprises: computing a first thermal expectation of the respective Hamiltonian term; computing a second thermal expectation of the respective Hamiltonian term constrained to a training example; computing a difference between the first thermal expectation and the second thermal expectation; and computing the gradient value for the respective Hamiltonian by multiplying the difference by a uniform probability distribution. 4. The method of claim 1, wherein the gradient values are computed at least in part from a first thermal expectation of the respective Hamiltonian term and a second thermal expectation the respective Hamiltonian term constrained to a training set, wherein the first thermal expectation of the respective Hamiltonian term is computed by preparing a thermal state for a Hamiltonian model represented by the first Hamiltonian terms, and measuring an expectation value for the respective Hamiltonian term from the prepared thermal state, and wherein the second thermal expectation of the respective Hamiltonian term is computed by preparing a thermal state for a modified Hamiltonian model represented by the first Hamiltonian terms with an added positive-operator-value-measure constraint, and measuring an expectation value for the respective Hamiltonian term from the prepared thermal state. 5. The method of claim 1, wherein the Hamiltonians implemented by the first Hamiltonian terms and the second Hamiltonian are non-stoquastic Hamiltonians. 6. The method of claim 1, wherein the method is performed using measurement records that result from using measurement operators that do not solely project onto the computational basis. 7. The method of claim 1, wherein the method is performed at least one POVM constraint that does not solely project onto the computational basis. 8. A system, comprising: a quantum computing device; and a classical computer configured to communicate with and control the quantum computer, the classical computer comprising memory and a processor, the classical computer being programmed to perform the method of claim 1. 9. A method, comprising: configuring a quantum computer to implement a Boltzmann machine in accordance with a first set of Hamiltonian terms; computing gradient values for adjusting the first set of Hamiltonian terms using a relative entropy approach; computing a second set of Hamiltonian terms based at least in part on the computed gradient values; and configuring the quantum computer to implement the Boltzmann machine in accordance with the second set of Hamiltonian terms, wherein the second set of Hamiltonian to terms cause the Boltzmann machine to implement a second Hamiltonian that more likely reproduces a training set of data than a first Hamiltonian resulting from the first set of Hamiltonian terms. 10. The method of claim 9, wherein the relative entropy approach uses a relative entropy between an input state and the state output by the Boltzmann machine. 11. The method of claim 9, wherein the computing the second set of Hamiltonian terms comprises multiplying the gradient values by a selected learning rate. 12. The method of claim 9, wherein the computing the gradient values comprises, for a respective Hamiltonian term, comprises: preparing a quantum data distribution; computing an expectation of the respective Hamiltonian term from the quantum data distribution; computing a thermal expectation of the respective Hamiltonian term; computing a difference between the expectation and the thermal expectation; and computing the gradient value for the respective Hamiltonian by multiplying the difference by a probability distribution, wherein the quantum data distribution corresponds to a target quantum state selected by a user. 13. The method of claim 9, wherein the gradient values are computed at least in part from a data-distribution expectation of the respective Hamiltonian term and a thermal expectation of the respective Hamiltonian term, wherein the data distribution expectation is computed by taking a thermal average agaisnt a state that is not a mixed state in the computational basis, and wherein the thermal expectation is computed by a Szeggedy walk designed to prepare an approximation to the thermal distribution, or a method based on quantum rejection sampling or linear-combinations of unitaries to refine an initial approximation to the thermal state into the exact thermal state or a close approximation thereof. 14. The method of claim 13, wherein the data-distribution expectation of the respective Hamiltonian term is computed by preparing a quantum data distribution, and measuring an expectation value for the respective Hamiltonian term from the quantum data distribution, and wherein the thermal expectation of the respective Hamiltonian term is computed by preparing a thermal state for a Hamiltonian model represented by the first Hamiltonian terms, and measuring an expectation value for the respective Hamiltonian term from the prepared thermal state. 15. A system, comprising: a quantum computing device; and a classical computer configured to communicate with and control the quantum computer, the classical computer comprising memory and a processor the classical computer being programmed to perform the method of claim 9. 16. A method, comprising: configuring a quantum computer to implement a Boltzmann machine in accordance with a first set of Hamiltonian terms; computing gradient values for adjusting the first set of Hamiltonian terms using a commutator series approximation to the gradient of a training objective function; computing a second set of Hamiltonian terms based at least in part on the computed gradient values; and configuring the quantum computer to implement the Boltzmann machine in accordance with the second set of Hamiltonian terms. 17. The method of claim 16, wherein the second set of Hamiltonian terms cause the Boltzmann machine to implement a second Hamiltonian that more likely reproduces a training set of data than a first Hamiltonian resulting from the first set of Hamiltonian terms. 18. The method of claim 16, wherein the commutator series approximation approach uses commutator expansions of a derivative of an average log-likelihood. 19. The method of claim 16, wherein the computing the gradient values comprises: computing commutator expansions of the commutator series approximation; for a respective term in a respective one of the commutator expansions: preparing a thermal state for a Hamiltonian model represented by the first Hamiltonian terms; and measuring an expectation value for the respective Hamiltonian term from the prepared thermal state. 20. A system, comprising: a quantum computing device; and a classical computer configured to communicate with and control the quantum computer, the classical computer comprising memory and a processor, the classical computer being programmed to perform the method of claim 16.