The University of Chicago

Henry Hoffmann

Lester Teichner

Benjamin Vear

David C Schuster

Pranav Gokhale

Haryadi Gunawi

Frederic T Chong

John Lafferty

Nikita Mishra

Yunong Shi, I

Bernard Dickens, Iii

Nelson Leung

Ariel Feldman

Example embodiments allow for networks of hybrid controllers that can be computed efficiently and that can adapt to changes in the system(s) under control. Such a network includes at least one hybrid controller that includes a dynamic sub-controller and a learned system sub-controller. Information about the ongoing performance of the system under control is provided to both the hybrid controller and to an over-controller, which provides one or more control inputs to the hybrid controller in order to modify the ongoing operation of the hybrid controller. These inputs can include the set-point of the hybrid controller, one or more parameters of the dynamic controller, and an update rate or other parameter of the learned system controller. The over-controller can control multiple hybrid controllers (e.g., controlling respective sub-systems of an overall system) and can, itself, be a hybrid controller.

Combined learned and dynamic control system

A quantum computing system includes a quantum processor having a plurality of qubits, a classical memory, and a classical processor. The classical processor is configured to compile a quantum program into logical assembly instructions in an intermediate language, aggregate the logical assembly instructions together into a plurality of logical blocks of instructions, generate a logical schedule for the quantum program based on commutativity between the plurality of logical blocks, generate a tentative physical schedule based on the logical schedule, the tentative physical schedule includes a mapping of the logical assembly instructions in the logical schedule onto the plurality of qubits of the quantum processor, aggregate instructions together within the tentative physical schedule that do not reduce parallelism, thereby generating an updated physical schedule; generate optimized control pulses for the aggregated instructions, and execute the quantum program on the quantum processor with the optimized control pulses and the updated physical schedule.

System and method of optimizing instructions for quantum computers

An embodiment involves receiving a request to write data to a memory unit. The memory unit is divided into one or more logical blocks, each subdivided into groups of sub-blocks encrypted in accordance with a stream cipher. The memory unit maintains a transaction journal that marks each sub-block as dirty or clean. The memory unit stores keycount values for each of the logical blocks. The embodiment also involves: determining that the request seeks to write a portion of the data to a particular sub-block marked as dirty in the transaction journal, decrypting the particular logical block in accordance with the stream cipher, writing the portion of the data to the particular sub-block, incrementing the keycount value of the particular logical block, encrypting the particular logical block using the stream cipher, a key, and the keycount value, and writing the particular logical block to the memory unit.

Stream ciphers for digital storage encryption

An apparatus and method are provided to perform constrained optimization of a constrained property of an apparatus, which is complex due to having several components, and these components are configurable in real-time. The optimization is achieved by detecting values of the constrained property and a plurality of other properties of the apparatus when the apparatus is configured in a first subset of the plurality of configurations. A model is learned using the detected values of the constrained property. The model represents the constrained property and can also represent other properties as a function of the configurations. The model can also include estimated uncertainties of the constrained property in the model. Then, using the d model and the estimated uncertainties, the optimal configuration can be selected to minimize an error value (e.g., the difference between a desired value and an observed value of the at least one constrained property).

Apparatus and method for optimizing quantifiable behavior in configurable devices and systems

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Chicago, IL, United States of America

Henry Hoffmann