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Two posters by @UniBasel researchers on machine learning for quantum technologies during #MLQT workshop @UniBasel_en https://t.co/KEEMluKkBF https://t.co/jlHTkAxl8S https://t.co/qqSRtBGSbL

Two posters by @UniBasel researchers on machine learning for quantum technologies during #MLQT workshop @UniBasel_en https://t.co/KEEMluKkBF https://t.co/jlHTkAxl8S pic.twitter.com/qqSRtBGSbL — Alexey Melnikov (@alexeyamelnikov) May 13, 2019 from Twitter https://twitter.com/alexeyamelnikov

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Renato Renner from @ETH_physics about SciNet, a network that enables the extraction of learned physical laws https://t.co/hOrM6e5Cty Renato’s motivation is https://t.co/e20tpflui0 #MLQT https://t.co/r3C3D8kjiW

Renato Renner from @ETH_physics about SciNet, a network that enables the extraction of learned physical laws https://t.co/hOrM6e5Cty Renato’s motivation is https://t.co/e20tpflui0 #MLQT pic.twitter.com/r3C3D8kjiW — Alexey Melnikov (@alexeyamelnikov) May 12, 2019 from Twitter https://twitter.com/alexeyamelnikov

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Machine learning in quantum control. @bcsanders unconfuses us by describing a general framework for learning in classical and quantum control #MLQT https://t.co/Ws4aSWdw30 https://t.co/4uWlsbazVe

Machine learning in quantum control. @bcsanders unconfuses us by describing a general framework for learning in classical and quantum control #MLQT https://t.co/Ws4aSWdw30 pic.twitter.com/4uWlsbazVe — Alexey Melnikov (@alexeyamelnikov) May 11, 2019 from Twitter https://twitter.com/alexeyamelnikov

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Alexander Alodjants from @spbifmo_en is talking about our work on classifying graps for observing quantum speedup by quantum walks #MLQT https://t.co/DWtfUijEat https://t.co/juzv5PShzi

Alexander Alodjants from @spbifmo_en is talking about our work on classifying graps for observing quantum speedup by quantum walks #MLQT https://t.co/DWtfUijEat pic.twitter.com/juzv5PShzi — Alexey Melnikov (@alexeyamelnikov) May 11, 2019 from Twitter https://twitter.com/alexeyamelnikov

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Many exciting experimental results in photonics, all analyzed with machine learning, presented by @FabioSciarrino at #MLQT including recent work with @expQuantum https://t.co/zORnarXq1b https://t.co/FY4AzhxdUx

Many exciting experimental results in photonics, all analyzed with machine learning, presented by @FabioSciarrino at #MLQT including recent work with @expQuantum https://t.co/zORnarXq1b pic.twitter.com/FY4AzhxdUx — Alexey Melnikov (@alexeyamelnikov) May 10, 2019 from Twitter https://twitter.com/alexeyamelnikov

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A good lesson and demonstration from @jenseisert: The fault tolerant decoding problem is much suitable to be tackled by reinforcement learning #MLQT https://t.co/V6ZVNx0GMe also with @Evert_v_N https://t.co/EamNyNtMQa

A good lesson and demonstration from @jenseisert: The fault tolerant decoding problem is much suitable to be tackled by reinforcement learning #MLQT https://t.co/V6ZVNx0GMe also with @Evert_v_N pic.twitter.com/EamNyNtMQa — Alexey Melnikov (@alexeyamelnikov) May 10, 2019 from Twitter https://twitter.com/alexeyamelnikov

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A talk of @rgmelko about learning wave functions and density matrices with his favorite tool – RBM #MLQT https://t.co/aalET5vPzg

A talk of @rgmelko about learning wave functions and density matrices with his favorite tool – RBM #MLQT pic.twitter.com/aalET5vPzg — Alexey Melnikov (@alexeyamelnikov) May 10, 2019 from Twitter https://twitter.com/alexeyamelnikov

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”Integrating neural networks and quantum simulators” talk by @Evert_v_N on efficiently reconstructing quantum states from experimental data #MLQT https://t.co/LSAyF69vjO

”Integrating neural networks and quantum simulators” talk by @Evert_v_N on efficiently reconstructing quantum states from experimental data #MLQT pic.twitter.com/LSAyF69vjO — Alexey Melnikov (@alexeyamelnikov) May 9, 2019 from Twitter https://twitter.com/alexeyamelnikov

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Mauro Paternostro on his work with @linnocenti0 on quantum gate synthesis and quantum state engineering with help from machine learning #MLQT https://t.co/U0n5GJaojV https://t.co/0ZEdIM4TmK https://t.co/9g4AMoFoEP

Mauro Paternostro on his work with @linnocenti0 on quantum gate synthesis and quantum state engineering with help from machine learning #MLQT https://t.co/U0n5GJaojV https://t.co/0ZEdIM4TmK pic.twitter.com/9g4AMoFoEP — Alexey Melnikov (@alexeyamelnikov) May 9, 2019 from Twitter https://twitter.com/alexeyamelnikov

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A talk of @EliskaGreplova from @ETH_physics about Hamiltonian learning with neural networks for QEC #MLQT https://t.co/0M2EakggpT

A talk of @EliskaGreplova from @ETH_physics about Hamiltonian learning with neural networks for QEC #MLQT pic.twitter.com/0M2EakggpT — Alexey Melnikov (@alexeyamelnikov) May 9, 2019 from Twitter https://twitter.com/alexeyamelnikov

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