Quantum Computing with The Machine Learning Approach

Sep 15, 07:00PM PDT(02:00AM GMT).
  • Free 139 Attendees
This seminar is hosted by SF Bay ACM

Quantum computing promises unprecedented possibilities for important computing tasks such as quantum simulations in chemistry and materials science or optimization and machine learning. The development of novel and efficient methods and algorithms that explicitly take advantage of such emerging disruptive computing architectures is a huge challenge and opportunity.

Quantum algorithms for gate-based quantum computers and quantum annealers are different. On a gate-based quantum computer, a quantum algorithm consists of a sequence of quantum circuit operations (gates) that are performed on the qubits. In case of quantum annealing, a quantum algorithm is the continuous time (natural) evolution of a system of qubits to find the lowest-energy state of a system representing an optimization problem.

Quantum computing is increasingly attracting interest from industry and scientific groups that use high-performance computing for their applications. These pilot users of quantum computing are primarily interested in testing whether available quantum computing technologies are suitable today or in the foreseeable future for solving problems relevant to them. An important question to be answered is, which of these problems can be formulated such that a quantum or a hybrid quantum-classical algorithm can be developed to solve them.

Bhairav Mehta (Microsoft)

Principal Data Scientist Manager and Technical Program Manager at Microsoft, with 18 years experience in Analytics, Data Science, AI/ML, Big-Data and Program/Product management at Fortune 10 companies (Microsoft, Apple, Qualcomm, Ford) and startups (MIT Startup) in various industry verticals. Focused, highly dependable and detail-oriented solutions architect offering exceptional problem solving, troubleshooting skills and a talent for developing innovative solutions to unusual and difficult problems.
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