Lecture

Hardware Technologies and Applications

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In course

QUANT-400: Introduction to quantum science and technology

A broad view of the diverse aspects of the field is provided: quantum physics, communication, quantum computation, simulation of physical systems, physics of qubit platforms, hardware technologies. Students will grasp the field as a whole and better orient ...

Description

This lecture covers hardware technologies and applications, focusing on the quantum stack, fidelity to electronic circuit specifications, cryogenic electronics, quantum sensing, interfacing qubits with classical processors, quantum computing stack, cryo-CMOS modeling, cryo-CMOS parameters, mobility over temperature, and modern processes. It delves into choosing technologies like single-electron transistors and superconducting devices, discussing cryo-CMOS characterization, transistor mismatch, low-noise amplifiers, quantum-classical interface, amplifier classification, performance, impedance matching, coaxial cables, and ADC examples. The lecture also explores sampling, quantization, signal-to-noise ratio, effective number of bits, quantization noise, ADC limitations, INL, DNL, ADC architectures, and more.

Instructors (7)

Giuseppe Carleo

Giuseppe Carleo is a computational quantum physicist, whose main focus is the development of advanced numerical algorithms tostudy challenging problems involving strongly interacting quantum systems.He is best known for the introduction of machine learning techniques to study both equilibrium and dynamical properties,based on a neural-network representations of quantum states, as well for the time-dependent variational Monte Carlo method.He earned a Ph.D. in Condensed Matter Theory from the International School for Advanced Studies (SISSA) in Italy in 2011.He held postdoctoral positions at the Institut d’Optique in France and ETH Zurich in Switzerland, where he alsoserved as a lecturer in computational quantum physics.In 2018, he joined the Flatiron Institute in New York City in 2018 at the Center for Computational Quantum Physics (CCQ), working as a Research Scientist and project leader, and also leading the development of the open-source project NetKet.Since September 2020 he is an assistant professor at EPFL, in Switzerland, leading the Computational Quantum Science Laboratory (CQSL).

Zoë Holmes

Teaching & PhD PhD Students Ricard Puig, Sacha Lerch, Reyhaneh Aghaei Saem, Shao Hen Chiew, Weijie Xiong, Marc Sanz Drudis, Mehrad Sahebi, Manuel Rudolph Courses Quantum information theory PHYS-550 After recapping the basics of quantum theory from an informationtheoretic perspective, we will cover more advanced topics inquantum information theory. This includes introducing measures of quantum information, and developing a more advanced understanding quantum states, channels and measurement

Vincenzo Savona

Vincenzo Savona studied physics in Pisa at the Scuola Normale Superiore and the University of Pisa, prior to completing his PhD at the EPFL's Institute of Theoretical Physics. Subsequently he did post-doctoral work, first at the EPFL and then in the physics department of the Humboldt University of Berlin. In 2002, he returned to the EPFL to create his own research group, receiving a "professeur boursier" fellowship from the Swiss National Science Foundation. In 2006, he was appointed tenure-track assistant professor at the EPFL and joined the NCCR for Quantum Photonics. In 2010 he was appointed associate professor. Currently he directs the Laboratory of Theoretical Physics of Nanosystems.

Official source

Ontological neighbourhood

Information engineering

Signal processing: Digital image processing

Physics

Electromagnetism: Electrical networks

Related concepts (32)

Quantum computing

A quantum computer is a computer that exploits quantum mechanical phenomena. At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer.

Noise (electronics)

In electronics, noise is an unwanted disturbance in an electrical signal. Noise generated by electronic devices varies greatly as it is produced by several different effects. In particular, noise is inherent in physics and central to thermodynamics. Any conductor with electrical resistance will generate thermal noise inherently. The final elimination of thermal noise in electronics can only be achieved cryogenically, and even then quantum noise would remain inherent. Electronic noise is a common component of noise in signal processing.

Signal-to-noise ratio

Signal-to-noise ratio (SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to noise power, often expressed in decibels. A ratio higher than 1:1 (greater than 0 dB) indicates more signal than noise. SNR is an important parameter that affects the performance and quality of systems that process or transmit signals, such as communication systems, audio systems, radar systems, imaging systems, and data acquisition systems.

Input impedance

The input impedance of an electrical network is the measure of the opposition to current (impedance), both static (resistance) and dynamic (reactance), into a load network that is external to the electrical source network. The input admittance (the reciprocal of impedance) is a measure of the load network's propensity to draw current. The source network is the portion of the network that transmits power, and the load network is the portion of the network that consumes power.

Quantization (signal processing)

Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms.

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Quantum and nanocomputing

Covers quantum dots, qubit states, RF readout, electron spin resonance, charge transport, Rabi cycles, chopper amplifiers, and impedance matching.

Noise Reduction Techniques in Analog Circuits

Explores noise and offset reduction techniques in analog circuits, focusing on Autozero and Chopper Stabilization.

Noise Reduction Techniques

Explores noise reduction techniques in electrical systems, covering concepts like Fourier transform, impedance matching, and dithering.

Brush-up on Electronics Design

Covers fundamental concepts in electronics design, including Maxwell's equations, Kirchoff's equations, active components, PCB fabrication, impedance matching, and ADC architectures.

Pasquale Scarlino

I obtained my master's degree in Physics at the University of Salento, Lecce (Italy) in February 2011. During 2006-2011, I have also been a student of Scuola Superiore ISUFI (SSI). SSI is one of six schools of excellence established in Italy to develop the intellectual capital in technological and social sciences. I conducted an external Master thesis project during an 8 months internship in the Quantum Transport Group at TU Delft, under the supervision of Prof. L.M.K. Vandersypen. There, I implemented the Quantum Point Contact Radio-Frequency Reflectometry technique, which allows increasing the single-shot electron spin readout bandwidth and is currently routinely used in the group.I obtained my Ph.D. degree in February 2016, in the Spin Qubits group of Prof. L.M.K. Vandersypen at the Kavli Institute of Nanoscience-Qutech (TU Delft). During my Ph.D. I have been leading the Si/SiGe spin qubits project, collaborating with the M. Eriksson Group at Wisconsin University. In parallel, I have been working on other different projects, in particular with GaAs depletion quantum dots, high impedance superconducting resonators, and surface acoustic wave resonators. I have been working as a Postdoc fellow in the group of Prof. A. Wallraff (Quantum Device Lab) at ETH Zurich. My main project, in collaboration with the group of Prof. K. Ensslin and Prof. T. Ihn, consisted in integrating semiconductor and superconductor technologies. Realizing a well-controlled interface between the semiconductor and superconductor-based quantum information technologies may allow harnessing the best of both device architectures, for example by providing an interface between strongly coupled charge state and high coherence spin states. Furthermore, it enables the possibility to explore light/matter hybridization in a class of solid-state systems and regimes that are new in the context of quantum optics.From June 2019 till September 2020, I have been a Senior Researcher at Microsoft Station Q Copenhagen and at the Center for Quantum Devices in Copenhagen, focusing on developing semiconductor-superconducting hybrid hardware for topologically protected quantum computation.Since October 2020, I am a tenure track Assistant Professor of Physics in the School of Basic Sciences at the EPFL where I founded the Hybrid Quantum Circuit (HQC) laboratory.

Related concepts (32)

Quantum computing

Noise (electronics)

Signal-to-noise ratio

Input impedance

Quantization (signal processing)