Prospects and Challenges of Quantum Machine Learning in the NISQ Era
Margaretta Colangelo
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Noisy intermediate-scale quantum (NISQ) computers already exist.?Researchers are finding ways to put NISQ systems to work.
There have been several meaningful breakthroughs in quantum computing in the last year in both academia and in business. Google announced billions of investment into making practical quantum computers by 2029 and Goldman Sachs and JP Morgan stated their plan to implement quantum computing within 5 years. There’s increased interest of adoption of quantum technologies by large enterprise companies and more companies are considering Quantum as a Service. Companies are exploring quantum technologies in the finance, pharma, chemical, logistics, oil & gas and energy sectors.
Quantum.Tech Leaders Summit on September 30th is a day long virtual conference looking at the current state of quantum technologies and covering the entire Quantum ecosystem including hardware & software, cryptography & communications, sensing, metrology and imaging, with a focus on enterprise solution applications and?real world case studies.
Keynote
Gopal Karemore, PhD, principal data scientist in the advanced analytics department of Novo Nordisk will give the keynote entitled Prospects and Challenges of Quantum Machine Learning in the NISQ Era. Dr. Karemore received his Ph.D. degree in Computer Science from the University of Copenhagen, Denmark and has a strong experience with machine learning, computer vision, and quantum computing. Gopal has co-authored over 35+ research articles published in high impact scientific journals with general theme ‘AI and ML applications in life sciences and medicine’. Prior to joining Novo Nordisk Gopal worked with various companies and organizations such as Philips, GE research, Siemens Corporate Research, University of Pennsylvania, Spanish National Center for Cardiovascular Research and the Novo Nordisk Foundation Centers for Protein Research and Stem Cell Biology.
In his keynote, Dr. Karemore will touch upon following topics
1) Present status of AI Techniques?
2) Figure of merits of today's Machine Learning?
3) What is Quantum Machine Learning and how it can provide Quantum Advantage over classical Machine Learning.
5) Types of QML and their application domain in NISQ era.
6) Challenges of adopting Quantum Machine Learning in industry settings.
7) Some state of the art examples of Variational Quantum Circuits, QCNN, QGAN, QRNN, QSVM, Generative models through Quantum Annealing, Quantum Boltzmann Machine, Ising model etc.
Panel Discussion
Quantum Computing Applications in Healthcare and Life Sciences
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Topics Covered
Agenda
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This article was written by?Margaretta Colangelo.?Margaretta is Co-founder and CEO of?Jthereum an enterprise Blockchain technology company. She is associate editor at AI Time Journal and serves on the advisory board of the AI Precision Health Institute at the University of Hawai?i?Cancer Center. She is based in San Francisco.?@realmargaretta