Boltzmann machines are trained using an objective function that is evaluated by sampling quantum states that approximate a Gibbs state. Classical processing is used to produce the objective function, and the approximate Gibbs state is based on weights and biases that are refined using the sample results. In some examples, amplitude estimation is used. A combined classical/quantum computer produces suitable weights and biases for classification of shapes and other applications.

The present disclosure describes a chuck-based device and a method for cleaning a semiconductor manufacturing system. The semiconductor manufacturing system can include a chamber with the chuck-based device configured to clean the chamber, a loading port coupled to the chamber and configured to hold one or more wafer storage devices, and a control device configured to control a translational displacement and a rotation of the chuck-based device. The chuck-based device can include a based stage, one or more supporting rods disposed at the base stage and configured to be vertically extendable or retractable, and a padding film disposed on the one or more supporting rods.

Boltzmann machines are trained using an objective function that is evaluated by sampling quantum states that approximate a Gibbs state. Classical processing is used to produce the objective function, and the approximate Gibbs state is based on weights and biases that are refined using the sample results. In some examples, amplitude estimation is used. A combined classical/quantum computer produces suitable weights and biases for classification of shapes and other applications.

Boltzmann machines are trained using an objective function that is evaluated by sampling quantum states that approximate a Gibbs state. Classical processing is used to produce the objective function, and the approximate Gibbs state is based on weights and biases that are refined using the sample results. In some examples, amplitude estimation is used. A combined classical/quantum computer produces suitable weights and biases for classification of shapes and other applications.

The present disclosure describes a chuck-based device and a method for cleaning a semiconductor manufacturing system. The semiconductor manufacturing system can include a chamber with the chuck-based device configured to clean the chamber, a loading port coupled to the chamber and configured to hold one or more wafer storage devices, and a control device configured to control a translational displacement and a rotation of the chuck-based device. The chuck-based device can include a based stage, one or more supporting rods disposed at the base stage and configured to be vertically extendable or retractable, and a padding film disposed on the one or more supporting rods.

A method, a UE and a communications system for releasing resources are disclosed. When the UE is in a CELL_FACH state, a resource release indication is transmitted from a network equipment to the UE to instruct the UE to release HS-RACH resources on the UE. Upon receiving the resource release indication, the UE releases previously allocate HS-RACH resources on the UE.