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New Approaches to Control, Calibrati...

Krastanov, Stefan Ivanov.

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New Approaches to Control, Calibration, and Optimization of Quantum Hardware.

Record Type:	Electronic resources : Monograph/item
Title/Author:	New Approaches to Control, Calibration, and Optimization of Quantum Hardware./
Author:	Krastanov, Stefan Ivanov.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	132 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
Subject:	Quantum physics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13902563
ISBN:	9798607310370

New Approaches to Control, Calibration, and Optimization of Quantum Hardware.
Krastanov, Stefan Ivanov.

New Approaches to Control, Calibration, and Optimization of Quantum Hardware. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 132 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--Yale University, 2019.

This item must not be sold to any third party vendors.

For almost a century humanity has been building machinery capable of universal computation as modeled by Turing machines. During that time, scientists have been captivated by the question of whether the laws of the physical universe allow for more computational power than what we can currently get from these classical machines. For nearly half that time we have known that, in principle, this is possible by exploiting the resources that quantum mechanics provides. However, only recently we have been able to actually build machines refined enough to have the hope to manipulate the fragile quantum states required in the implementation of a quantum computer.In this dissertation I present a number of techniques that form building blocks for the aforementioned quantum manipulations spanning various levels of the quantum technology stack. We begin at the very bottom, with techniques for the universal control of a quantum harmonic oscillators. Oscillators such as microwave and optical cavities are among some of the more promising physical systems on top of which to implement quantum logic. However, most of our technology until recently has been focused on manipulating pseudo-classical states of light, restricted to operations that preserve the Gaussian profile of the quantum states. Much more general unitary operations are necessary to unlock the computation power of quantum mechanics and we will see a number of protocols enabling such operations.However, the precise control and evaluation of the hardware requires a well calibrated model of the dynamical laws governing it. Methods like state and process tomography permit such calibration in principle, but they require a very large number of measurements and dealing with the noise inherent to the hardware makes them fragile. Instead of these methods, we will see how tools borrowed from compressed sensing and machine learning provide for cheaper, more robust, and higher fidelity calibration procedure.Going one step higher in the technology stack, we need to use these control techniques to actually prepare non-classical resources for use in quantum computation. One of the most ubiquitous such resource is quantum entanglement. We will see how one can optimize the entanglement distillation circuits for the error model of the actual hardware. The optimized circuits perform substantially better than a general distillation circuit by virtue of being optimized for the particularities of the hardware -- this way the results from the previously discussed calibration procedure inform the design of upper layers of the technology stack.We can continue this optimization journey at still higher layers of the stack. On top of the physical qubits we have created we still need to implement an error correcting code that can asymptotically suppress errors. We will study a black-box decoder for such codes, based on a neural network architecture, that permits us to work with advanced codes for which individual decoders have not been designed yet. This enables us to both optimize the decoder for the particular hardware and even optimize the code structure itself, without worrying that the new code will be difficult to decode.

ISBN: 9798607310370Subjects--Topical Terms:

726746
Quantum physics.
Subjects--Index Terms:

Quantum computing

New Approaches to Control, Calibration, and Optimization of Quantum Hardware.
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520 $a For almost a century humanity has been building machinery capable of universal computation as modeled by Turing machines. During that time, scientists have been captivated by the question of whether the laws of the physical universe allow for more computational power than what we can currently get from these classical machines. For nearly half that time we have known that, in principle, this is possible by exploiting the resources that quantum mechanics provides. However, only recently we have been able to actually build machines refined enough to have the hope to manipulate the fragile quantum states required in the implementation of a quantum computer.In this dissertation I present a number of techniques that form building blocks for the aforementioned quantum manipulations spanning various levels of the quantum technology stack. We begin at the very bottom, with techniques for the universal control of a quantum harmonic oscillators. Oscillators such as microwave and optical cavities are among some of the more promising physical systems on top of which to implement quantum logic. However, most of our technology until recently has been focused on manipulating pseudo-classical states of light, restricted to operations that preserve the Gaussian profile of the quantum states. Much more general unitary operations are necessary to unlock the computation power of quantum mechanics and we will see a number of protocols enabling such operations.However, the precise control and evaluation of the hardware requires a well calibrated model of the dynamical laws governing it. Methods like state and process tomography permit such calibration in principle, but they require a very large number of measurements and dealing with the noise inherent to the hardware makes them fragile. Instead of these methods, we will see how tools borrowed from compressed sensing and machine learning provide for cheaper, more robust, and higher fidelity calibration procedure.Going one step higher in the technology stack, we need to use these control techniques to actually prepare non-classical resources for use in quantum computation. One of the most ubiquitous such resource is quantum entanglement. We will see how one can optimize the entanglement distillation circuits for the error model of the actual hardware. The optimized circuits perform substantially better than a general distillation circuit by virtue of being optimized for the particularities of the hardware -- this way the results from the previously discussed calibration procedure inform the design of upper layers of the technology stack.We can continue this optimization journey at still higher layers of the stack. On top of the physical qubits we have created we still need to implement an error correcting code that can asymptotically suppress errors. We will study a black-box decoder for such codes, based on a neural network architecture, that permits us to work with advanced codes for which individual decoders have not been designed yet. This enables us to both optimize the decoder for the particular hardware and even optimize the code structure itself, without worrying that the new code will be difficult to decode.
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