Abstract: A hybrid quantum classical (HQC) computer, which includes both a classical computer component and a quantum computer component, implements improvements to the quantum approximate optimization algorithm (QAOA) which enable QAOA to be applied to valuable problem instances (e.g., those including several thousand or more qubits) using near-term quantum computers.

Abstract: 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.

Abstract: A hybrid quantum classical (HQC) computer, which includes both a classical computer component and a quantum computer component, implements improvements to the quantum approximate optimization algorithm (QAOA) which enable QAOA to be applied to valuable problem instances (e.g., those including several thousand or more qubits) using near-term quantum computers.

Abstract: A method for decreasing entropy in a system includes iteratively applying a set of electromagnetic (EM) pulses to the system, the set of EM pulses effect swaps between the following pairs of system energy levels: a first system energy level in which the reset system is in a lowest energy level and the target system is in a first target system energy level that is not a lowest energy level, and a corresponding second system energy level in which the reset system is in a highest energy level and the target system is in a second target system energy level that is next lowest in energy after the first target system energy level, and waiting a time period.

Abstract: A method for decreasing entropy in a system includes iteratively applying a set of electromagnetic (EM) pulses to the system, the set of EM pulses effect swaps between the following pairs of system energy levels: a first system energy level in which the reset system is in a lowest energy level and the target system is in a first target system energy level that is not a lowest energy level, and a corresponding second system energy level in which the reset system is in a highest energy level and the target system is in a second target system energy level that is next lowest in energy after the first target system energy level, and waiting a time period.

Abstract: 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.