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meQuanics - QSI@UTS Seminar Series - S18 - Chris Ferrie (University of Technology Sydney)

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Contenido proporcionado por meQuanics. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente meQuanics o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series. With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk. I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/OfY7lFIBTGY

Self-Guided Quantum Learning: Estimation via optimisation applied to quantum estimation

TITLE: Self-Guided Quantum Learning

SPEAKER: Associate Professor Chris Ferrie

AFFILIATION: Centre for Quantum Software and Information, University of Technology Sydney, Australia

HOSTED BY: Dr Clara Javaherian, UTS Centre for Quantum Software and Information, Australia

ABSTRACT: Quantum state learning is often understood as a data analytics problem—large amounts of data collected from many prior repetitions of incompatible measurements need to be churned into a single estimate of a quantum state or channel. In this talk, I will present an adaptive optimisation algorithm which achieves the same goal, but at a drastic reduction in time and space complexity.

RELATED ARTICLES: Experimental realization of self-guided quantum process tomography: https://arxiv.org/abs/1908.01082Experimental Demonstration of Self-Guided Quantum Tomography: https://arxiv.org/abs/1602.04194Self-guided quantum tomography: https://arxiv.org/abs/1406.4101

OTHER LINKS: Chris Ferrie: csferrie.com/

  continue reading

82 episodios

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Manage episode 306370603 series 1277392
Contenido proporcionado por meQuanics. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente meQuanics o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series. With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk. I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/OfY7lFIBTGY

Self-Guided Quantum Learning: Estimation via optimisation applied to quantum estimation

TITLE: Self-Guided Quantum Learning

SPEAKER: Associate Professor Chris Ferrie

AFFILIATION: Centre for Quantum Software and Information, University of Technology Sydney, Australia

HOSTED BY: Dr Clara Javaherian, UTS Centre for Quantum Software and Information, Australia

ABSTRACT: Quantum state learning is often understood as a data analytics problem—large amounts of data collected from many prior repetitions of incompatible measurements need to be churned into a single estimate of a quantum state or channel. In this talk, I will present an adaptive optimisation algorithm which achieves the same goal, but at a drastic reduction in time and space complexity.

RELATED ARTICLES: Experimental realization of self-guided quantum process tomography: https://arxiv.org/abs/1908.01082Experimental Demonstration of Self-Guided Quantum Tomography: https://arxiv.org/abs/1602.04194Self-guided quantum tomography: https://arxiv.org/abs/1406.4101

OTHER LINKS: Chris Ferrie: csferrie.com/

  continue reading

82 episodios

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