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A17.00001: Challenges and opportunities for hybrid quantum-classical machine learning and optimization
Invited Speaker:
Masoud Mohseni
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A17.00002: Variational Quantum Unsampling on an Photonic Quantum Processor
Jacques Carolan, Masoud Mohseni, Jonathan P Olson, Mihika Prabhu, Changchen Chen, Darius Bunandar, Murphy Yuezhen Niu, Nicholas C Harris, Franco N. C. Wong, Michael Hochberg, Seth Lloyd, Dirk R. Englund
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A17.00003: Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
Guillaume Verdon, Jacob Marks, Sasha Nanda, Stefan Leichenauer, Jack Hidary
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A17.00004: Variational Fast Forwarding for Quantum Simulation Beyond the Coherence Time
Andrew Sornborger, Cristina Cirstoiu, Zoe Holmes, Joseph Iosue, Lukasz Cincio, Patrick Coles
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A17.00005: Stochastic Gradient Descent for Hybrid Quantum-Classical Optimization
Frederik Wilde, Ryan Sweke, Johannes Jakob Meyer, Maria Schuld, Paul K. Fährmann, Barthélémy Meynard-Piganeau, Jens Eisert
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A17.00006: Robust and efficient algorithms for high-dimensional black-box quantum optimization
Zhaoqi Leng, Pranav Mundada, Saeed Ghadimi, Andrew Houck
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A17.00007: A Hybrid Quantum-Classical Algorithm for Training Quantum Boltzmann Machines
Christa Zoufal, Aurélien Lucchi, Stefan Woerner
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A17.00008: Quantum classifier with tailored quantum kernel
Kyungdeock Park, Carsten Blank, June-Koo(KEVIN) RHEE, Francesco Petruccione
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A17.00009: Machine learning with solid-state NMR using quantum kernel
Takeru Kusumoto, Kosuke Mitarai, Makoto Negoro, Keisuke Fujii, Masahiro Kitagawa
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A17.00010: Hybrid quantum-classical algorithms for generative models
Teng Bian, Sabre Kais
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A17.00011: Quantum-tailored machine-learning architectures
Elie Genois, Agustin Di Paolo, Alexandre Blais, Jonathan Gross
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A17.00012: Scalable Quantum State Tomography with Attention Network
Peter Cha, Juan Carrasquilla, Paul Ginsparg, Eun-Ah Kim
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A17.00013: Implementing perceptron models with qubits
Roeland Wiersema, Hilbert Johan Kappen
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