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meQuanics - QSI@UTS Seminar Series - S08 - Guillaume Verdon (X)

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Contenuto fornito da meQuanics. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da meQuanics o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.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.

Building better deep learning representations for quantum mixed states by adding quantum layers to classical probabilistic models.

TITLE: Quantum-probabilistic Generative Models and Variational Quantum Thermalization

SPEAKER: Guillaume Verdon

AFFILIATION: X (formerly Google X), California, USA

HOSTED BY: A/Prof. Chris Ferrie, Centre for Quantum Software and Information

ABSTRACT: We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for quantum-probabilistic hybrid variational learning of quantum mixed states, where we efficiently decompose the tasks of learning classical and quantum correlations in a way which maximizes the utility of both classical and quantum processors. In addition, we introduce the Variational Quantum Thermalizer (VQT) algorithm for generating the thermal state of a given Hamiltonian and target temperature, a task for which QHBMs are naturally well-suited. The VQT can be seen as a generalization of the Variational Quantum Eigensolver (VQE) to thermal states: we show that the VQT converges to the VQE in the zero temperature limit. We provide numerical results demonstrating the efficacy of these techniques in several illustrative examples. In addition to the introduction to the theory and applications behind these models, we will briefly walk through their numerical implementation in TensorFlow Quantum.

RELATED ARTICLES: Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm: https://arxiv.org/abs/1910.02071

TensorFlow Quantum: A Software Framework for Quantum Machine Learning: https://arxiv.org/abs/2003.02989

OTHER LINKS: X: https://x.company/

  continue reading

82 episodi

Artwork
iconCondividi
 
Manage episode 305948170 series 1277392
Contenuto fornito da meQuanics. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da meQuanics o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.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.

Building better deep learning representations for quantum mixed states by adding quantum layers to classical probabilistic models.

TITLE: Quantum-probabilistic Generative Models and Variational Quantum Thermalization

SPEAKER: Guillaume Verdon

AFFILIATION: X (formerly Google X), California, USA

HOSTED BY: A/Prof. Chris Ferrie, Centre for Quantum Software and Information

ABSTRACT: We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for quantum-probabilistic hybrid variational learning of quantum mixed states, where we efficiently decompose the tasks of learning classical and quantum correlations in a way which maximizes the utility of both classical and quantum processors. In addition, we introduce the Variational Quantum Thermalizer (VQT) algorithm for generating the thermal state of a given Hamiltonian and target temperature, a task for which QHBMs are naturally well-suited. The VQT can be seen as a generalization of the Variational Quantum Eigensolver (VQE) to thermal states: we show that the VQT converges to the VQE in the zero temperature limit. We provide numerical results demonstrating the efficacy of these techniques in several illustrative examples. In addition to the introduction to the theory and applications behind these models, we will briefly walk through their numerical implementation in TensorFlow Quantum.

RELATED ARTICLES: Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm: https://arxiv.org/abs/1910.02071

TensorFlow Quantum: A Software Framework for Quantum Machine Learning: https://arxiv.org/abs/2003.02989

OTHER LINKS: X: https://x.company/

  continue reading

82 episodi

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