NIPS 1 Day Workshop

on

Quantum Neural Computing

      2002

Recently there has been a resurgence of interest in quantum computers because of their potential for being very much smaller and faster than classical computers, and because of their ability in principle to do heretofore impossible calculations, such as factorization of large numbers in polynomial time. We will explore ways to implement biologically inspired quantum computing in network topologies, thus exploiting both the intrinsic advantages of quantum computing and the adaptability of neural computing. IN fact NSF now has a special program, QuBIC, in quantum and biologically inspired computing.  This workshop will follow up on our very successful NIPS 2000 workshop and the IJCNN 2001 Speical Session.

Aspects/approaches to be explored will include: quantum hardware, e.g. nmr, SQUIDs, trapped ions,  quantum dots, and molecular computing; theoretical and practical limits to quantum and quantum neural computing, e.g., noise, error correction, and decoherence; and simulations. Targeted groups: computer scientists, physicists and mathematicians interested in quantum computing and next-generation computing hardware.

Paul Werbos, NSF Program Director, Control, Networks & Computational Intelligence Program, Electrical and Communications Systems Division, will keynote the workshop.  Other invited speakers include: Colin Williams, Dan Ventura, Guiseppe Vitiello, Vlatko Vedral, Tom Weinacht, Elizabeth Behrman.

Schedule:

Morning Session: Quantum Neural Networks

Introduction (10 min or less)

Paul Werbos, NSF Program Director, Control, Networks & Computational Intelligence Program, Electrical and Communications Systems Division, "Quantum Information Sciences: Overview and Prognosis." (Keynote Address, 30 min.)

Overview and prognosis discussion and questions (30 min)

3 talks (15 min. each, 5 min questions.)

Discussion (30 min.): Is there an intrinsic advantage to quantum neural networks - is there a quantum advantage? What kinds of practical physical implementations are most feasible for quantum neural networks?

Afternoon Session: Quantum error correction and noise

3 talks (15 min. each, 5 min questions.)

Discussion (30 min.): Does the fault tolerance and parallel nature of neural computing solve some of the problems of quantum computing, like incomplete and/or damaged data, and decoherence?

Send contributed papers to:  Co-chairs: Elizabeth C. Behrman, elizabeth.behrman@wichita.edu and/or James E. Steck steck@bravo.engr.twsu.edu


 

This Workshop is partially supported by the National Science Foundation, Grant #ECS-0201995.