{"id":200075,"date":"2015-08-31T12:02:09","date_gmt":"2015-08-31T12:02:09","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/events\/quantum-machine-learning\/"},"modified":"2022-08-31T13:29:37","modified_gmt":"2022-08-31T20:29:37","slug":"quantum-machine-learning","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/quantum-machine-learning\/","title":{"rendered":"Quantum Machine Learning"},"content":{"rendered":"\n\n\n\n\n

Recent strides in quantum computing have raised the prospects that near term quantum devices can expediently solve computationally intractable problems in simulation, optimization and machine learning. The opportunities that quantum computing raises for machine learning is hard to understate. The goal of this workshop is, through a series of invited and contributed talks, survey the major results in this new area and facilitate increased dialog between researchers within this field.<\/p>\n

We will be accepting contributed talks as well. The deadline for submission is Oct 24. An abstract and link to the paper should be provided. We will provide feedback on submissions before the deadline for early registration for NIPS. All talks given will not be published in the proceedings and will be reviewed by the conference organizers. Please sent all contributions to nawiebe@microsoft.com<\/a>.<\/p>\n

The workshop is organized by Nathan Wiebe (Microsoft Research) and Seth Lloyd (MIT).<\/p>\n

It will take place at Palais des Congr\u00e8s de Montr\u00e9al, Montr\u00e9al, Canada as part of NIPS (opens in new tab)<\/span><\/a>(Neural Information Processing Systems).<\/p>\n

The workshop will consist of 4 invited talks as well as 4 contributed talks and a primer on quantum mechanics and quantum computing. Ample time will also be allocated to allow discussion between the attendees.<\/p>\n\n\n\n\n\n

Session 1<\/strong><\/p>\n\n\n\n
9:00 \u2013 10:00<\/td>\nSeth Lloyd (Intro to quantum computing and quantum machine learning) Slides <\/a>| Slides<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Session 2<\/strong><\/p>\n\n\n\n\n\n\n\n
10:30 \u2013 11:10<\/td>\nAshish Kapoor (Quantum Deep Learning)<\/td>\n<\/tr>\n
11:10 \u2013 11:50<\/td>\nCyril Stark (Quantum models for non-physical data at the example of item recommendation) | Slides<\/a><\/td>\n<\/tr>\n
11:50 \u2013 12:30<\/td>\nPatrick Rebentrost (TBA)<\/td>\n<\/tr>\n
12:30 \u2013 12:45<\/td>\nVasil Denchev (Totally Corrective Boosting with Cardinality Penalization)<\/td>\n<\/tr>\n
12:45 \u2013 1:00<\/td>\nLuca Rossi (Quantum-Inspired Graph Matching)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Session 3<\/strong><\/p>\n\n\n\n\n\n
2:30 \u2013 3:10<\/td>\nNathan Wiebe (Can small quantum systems learn?) | Slides<\/a><\/td>\n<\/tr>\n
3:10 \u2013 3:35<\/td>\nSteven Adachi (Application of quantum annealing to Training of Deep Neural Networks) | Slides<\/a><\/td>\n<\/tr>\n
3:35 \u2013 4:00<\/td>\nAlejandro Perdomo (Estimation of effective temperatures in a quantum annealer and its impact in sampling applications: A case study towards deep learning).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Session 4<\/strong><\/p>\n\n\n\n\n\n
4:30 \u2013 5:10<\/td>\nMohammad Amin (Quantum Boltzmann Machine) | Slides<\/a><\/td>\n<\/tr>\n
5:10 \u2013 5:50<\/td>\nItay Hen (Fidelity-optimized quantum state estimation) |  Slides<\/a><\/td>\n<\/tr>\n
5:50 \u2013 6:30<\/td>\nHarmut Neven (Emerging Quantum Processors and why the Machine Learning Community should care)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n","protected":false},"excerpt":{"rendered":"

Recent strides in quantum computing have raised the prospects that near term quantum devices can expediently solve computationally intractable problems in simulation, optimization and machine learning. The opportunities that quantum computing raises for machine learning is hard to understate. The goal of this workshop is, through a series of invited and contributed talks, survey the […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2015-12-08","msr_enddate":"2015-12-08","msr_location":"NIPS 2015, Montreal","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":true,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556,243138],"msr-region":[197900],"msr-event-type":[],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-200075","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-quantum","msr-region-north-america","msr-locale-en_us"],"msr_about":"\n\n\n\n\n

Recent strides in quantum computing have raised the prospects that near term quantum devices can expediently solve computationally intractable problems in simulation, optimization and machine learning. The opportunities that quantum computing raises for machine learning is hard to understate. The goal of this workshop is, through a series of invited and contributed talks, survey the major results in this new area and facilitate increased dialog between researchers within this field.<\/p>\n

We will be accepting contributed talks as well. The deadline for submission is Oct 24. An abstract and link to the paper should be provided. We will provide feedback on submissions before the deadline for early registration for NIPS. All talks given will not be published in the proceedings and will be reviewed by the conference organizers. Please sent all contributions to nawiebe@microsoft.com<\/a>.<\/p>\n

The workshop is organized by Nathan Wiebe (Microsoft Research) and Seth Lloyd (MIT).<\/p>\n

It will take place at Palais des Congr\u00e8s de Montr\u00e9al, Montr\u00e9al, Canada as part of NIPS <\/a>(Neural Information Processing Systems).<\/p>\n

The workshop will consist of 4 invited talks as well as 4 contributed talks and a primer on quantum mechanics and quantum computing. Ample time will also be allocated to allow discussion between the attendees.<\/p>\n\n\n\n\n\n

Session 1<\/strong><\/p>\n\n\n\n
9:00 \u2013 10:00<\/td>\nSeth Lloyd (Intro to quantum computing and quantum machine learning) Slides <\/a>| Slides<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Session 2<\/strong><\/p>\n\n\n\n\n\n\n\n
10:30 \u2013 11:10<\/td>\nAshish Kapoor (Quantum Deep Learning)<\/td>\n<\/tr>\n
11:10 \u2013 11:50<\/td>\nCyril Stark (Quantum models for non-physical data at the example of item recommendation) | Slides<\/a><\/td>\n<\/tr>\n
11:50 \u2013 12:30<\/td>\nPatrick Rebentrost (TBA)<\/td>\n<\/tr>\n
12:30 \u2013 12:45<\/td>\nVasil Denchev (Totally Corrective Boosting with Cardinality Penalization)<\/td>\n<\/tr>\n
12:45 \u2013 1:00<\/td>\nLuca Rossi (Quantum-Inspired Graph Matching)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Session 3<\/strong><\/p>\n\n\n\n\n\n
2:30 \u2013 3:10<\/td>\nNathan Wiebe (Can small quantum systems learn?) | Slides<\/a><\/td>\n<\/tr>\n
3:10 \u2013 3:35<\/td>\nSteven Adachi (Application of quantum annealing to Training of Deep Neural Networks) | Slides<\/a><\/td>\n<\/tr>\n
3:35 \u2013 4:00<\/td>\nAlejandro Perdomo (Estimation of effective temperatures in a quantum annealer and its impact in sampling applications: A case study towards deep learning).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Session 4<\/strong><\/p>\n\n\n\n\n\n
4:30 \u2013 5:10<\/td>\nMohammad Amin (Quantum Boltzmann Machine) | Slides<\/a><\/td>\n<\/tr>\n
5:10 \u2013 5:50<\/td>\nItay Hen (Fidelity-optimized quantum state estimation) |  Slides<\/a><\/td>\n<\/tr>\n
5:50 \u2013 6:30<\/td>\nHarmut Neven (Emerging Quantum Processors and why the Machine Learning Community should care)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n","tab-content":[{"id":0,"name":"Summary","content":"Recent strides in quantum computing have raised the prospects that near term quantum devices can expediently solve computationally intractable problems in simulation, optimization and machine learning. The opportunities that quantum computing raises for machine learning is hard to understate. The goal of this workshop is, through a series of invited and contributed talks, survey the major results in this new area and facilitate increased dialog between researchers within this field.\r\n\r\nWe will be accepting contributed talks as well. The deadline for submission is Oct 24. An abstract and link to the paper should be provided. We will provide feedback on submissions before the deadline for early registration for NIPS. All talks given will not be published in the proceedings and will be reviewed by the conference organizers. Please sent all contributions to nawiebe@microsoft.com<\/a>.\r\n\r\nThe workshop is organized by Nathan Wiebe (Microsoft Research) and Seth Lloyd (MIT).\r\n\r\nIt will take place at Palais des Congr\u00e8s de Montr\u00e9al, Montr\u00e9al, Canada as part of NIPS <\/a>(Neural Information Processing Systems).\r\n\r\nThe workshop will consist of 4 invited talks as well as 4 contributed talks and a primer on quantum mechanics and quantum computing. Ample time will also be allocated to allow discussion between the attendees."},{"id":1,"name":"Agenda","content":"Session 1<\/strong>\r\n\r\n\r\n\r\n
9:00 \u2013 10:00<\/td>\r\nSeth Lloyd (Intro to quantum computing and\u00a0quantum machine learning) Slides <\/a>| Slides<\/a><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nSession 2<\/strong>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
10:30 - 11:10<\/td>\r\nAshish Kapoor (Quantum Deep Learning)<\/td>\r\n<\/tr>\r\n
11:10 - 11:50<\/td>\r\nCyril Stark (Quantum models for non-physical data at the example of item recommendation) | Slides<\/a><\/td>\r\n<\/tr>\r\n
11:50 - 12:30<\/td>\r\nPatrick Rebentrost (TBA)<\/td>\r\n<\/tr>\r\n
12:30 - 12:45<\/td>\r\nVasil Denchev (Totally Corrective Boosting with Cardinality Penalization)<\/td>\r\n<\/tr>\r\n
12:45 \u2013 1:00<\/td>\r\nLuca Rossi (Quantum-Inspired Graph Matching)<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n \r\n\r\nSession 3<\/strong>\r\n\r\n\r\n\r\n\r\n\r\n
2:30 - 3:10<\/td>\r\nNathan Wiebe (Can small quantum systems learn?) | Slides<\/a><\/td>\r\n<\/tr>\r\n
3:10 - 3:35<\/td>\r\nSteven Adachi (Application of quantum annealing to Training of Deep Neural Networks) | Slides<\/a><\/td>\r\n<\/tr>\r\n
3:35 \u2013 4:00<\/td>\r\nAlejandro Perdomo (Estimation of effective temperatures in a quantum annealer and its impact in sampling applications: A case study towards deep learning).<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n \r\n\r\nSession 4<\/strong>\r\n\r\n\r\n\r\n\r\n\r\n
4:30 - 5:10<\/td>\r\nMohammad Amin (Quantum Boltzmann Machine) | Slides<\/a><\/td>\r\n<\/tr>\r\n
5:10 - 5:50<\/td>\r\nItay Hen (Fidelity-optimized quantum state estimation) |\u00a0 Slides<\/a><\/td>\r\n<\/tr>\r\n
5:50 \u2013 6:30<\/td>\r\nHarmut Neven (Emerging Quantum Processors and why the Machine Learning Community should care)<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n "}],"msr_startdate":"2015-12-08","msr_enddate":"2015-12-08","msr_event_time":"","msr_location":"NIPS 2015, Montreal","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"December 8, 2015","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"Recent strides in quantum computing have raised the prospects that near term quantum devices can expediently solve computationally intractable problems in simulation, optimization and machine learning. The opportunities that quantum computing raises for machine learning is hard to understate. The goal of this workshop is, through a series of invited and contributed talks, survey the major results in this new area and facilitate increased dialog between researchers within this field. We will be accepting contributed…","msr_research_lab":[],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-opportunities":[],"related-publications":[],"related-videos":[],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/200075","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/200075\/revisions"}],"predecessor-version":[{"id":874398,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/200075\/revisions\/874398"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=200075"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=200075"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=200075"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=200075"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=200075"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=200075"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=200075"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=200075"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=200075"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}