Abstract: A system and method for a quantum-enhanced optimizer (QEO) using quantum generative models to achieve lower minimum cost functions than classical or other known optimizers. In a first embodiment, the QEO operates as a booster to enhance the performance of known stand-alone optimizers in complex instances where known optimizers have limitations. In a second embodiment, the QEO operates as a stand-alone optimizer for finding a minimum with the least number of cost-function evaluations. The disclosed QEO methods outperform known optimizers, including Bayesian optimizers. The disclosed quantum-enhanced optimization methods may be based on tensor networks. The generative models may also be based on classical, quantum, or hybrid quantum-classical approaches, including Quantum Circuit Associative Adversarial Networks (QC-AAN) and Quantum Circuit Born Machines (QCBM).

Abstract: A system and method for generating higher-resolution datasets including handwritten numerical digits, color images, and video using generative adversarial networks (GANs) and quantum computing methods and components. A GAN includes a generator and discriminator and a quantum component, which provides input to the generator and accepts a sequence of instructions to evolve a quantum state based on a series of quantum gates to generate a higher resolution dataset. The quantum component may be in the form of quantum computer born machine (QCBM), implemented using a quantum computing associating adversarial network (QC-AAN) model using a multi-basis technique. The quantum computer elements may be implemented as a trapped-ion quantum device and use at least 8-qubits.

Abstract: Fault tree analysis is performed using a combination of digital computer systems and quantum processing devices. For example, quantum annealers may be configured to analyze a fault tree for minimal cut sets. The quantum annealer may be particular good at identifying smaller minimal cut sets. Digital computer systems may be used to identify the remaining minimal cut sets. If the quantum annealer identifies one of the minimal cut sets of smallest size (i.e., lowest cardinality), this can be used as a constraint for the digital computer system, thus speeding up its search for other minimal cut sets.

Abstract: Fault tree analysis is performed using a combination of digital computer systems and quantum processing devices. For example, quantum annealers may be configured to analyze a fault tree for minimal cut sets. The quantum annealer may be particular good at identifying smaller minimal cut sets. Digital computer systems may be used to identify the remaining minimal cut sets. If the quantum annealer identifies one of the minimal cut sets of smallest size (i.e., lowest cardinality), this can be used as a constraint for the digital computer system, thus speeding up its search for other minimal cut sets.