Quantum Computing

Approximate quantum computing: from advantage to applications

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quantum computing

Think Q 2017 attendees, back of Thomas J. Watson Research Center auditorium

quantum computing

Think Q 2017 attendees, front of Thomas J. Watson Research Center auditorium

Photo by IBM Fellow Charles Bennett

Last week at our third Think Q conference at the Thomas J Watson Research Center, industry and academic leaders in quantum computing met to tackle questions about how to bridge the divide between the theory of quantum algorithms and practical applications that can run on today’s approximate (non-fault tolerant) quantum computers.

The conference talks, panels, tutorial workshops, and poster sessions addressed: How can we harness an approximate quantum computer’s advantage over classical systems? How do we sufficiently protect quantum computers from noise? How do we use them to solve computational problems of interest? And can this all be achieve without incurring large overheads such as those required for fault-tolerant quantum computation?

Over the next few years, we’re not going to have fault tolerance, but we’re going to have something that should be more powerful than classical – and how we understand that is the difficulty. – Jay Gambetta, quantum information and computation manager, IBM Research

Our program was packed with scientific talks on quantum algorithms, quantum error mitigation and error correction, quantum chemistry, classical simulation and verification methods, and more – which you can watch, below. We closed with a fascinating panel discussion, Quantum computing before fault tolerance, with Google’s Dave Bacon, University of Maryland’s Andrew Childs, Cambridge University’s Richard Jozsa, Duke University’s and IonQ’s Jungsang Kim, NASA’s Eleanor Rieffel, IBM Research’s Matthias Steffen, which was moderated by Dario Gil, vice president of AI and IBM Q.

2017 Think Q


QVECTOR: An algorithm for variational quantum error correction – Alán Aspuru Guzik, Harvard University

Beyond classical computing via randomized low‐depth quantum circuits – Michael Bremner, University of Technology Sydney

Small quantum computers and big classical data – Aram Harrow, MIT

What to do with a near-term quantum computer – Edward Farhi, MIT/Google

Characterizing coherent errors efficiently, robustly, and simply – Shelby Kimmel, Middlebury College

Reducing errors in near term quantum computers – Ken Brown, Georgia Tech University

Toward protecting analog simulations from errors – Robin Blume Kohout, Sandia National Laboratories

Error mitigation in quantum simulation – Xiao Yuan, University of Oxford

Day 2

Towards quantum advantages of synthetic quantum systems – Jens Eisert, Freie Universität Berlin

Exploring quantum thermalization with a quantum computer – Bela Bauer, Microsoft

Quantum algorithms for Hamiltonian simulation: Recent results and open problems – Robin Kothari, Microsoft

Toward the first quantum simulation with quantum speedup – Andrew Childs, University of Maryland

Tutorial: QISKIT Quantum Computing Platform – Andrew Cross, IBM Research

Classical limits of simulating quantum systems – Garnet Chan, CalTech

Quantum simulation of electronic structure with low depth circuits – Ryan Babbush, Google

Costing quantum computer simulations of chemistry – Nathan Wiebe, Microsoft

Experimental quantum computing at IBM – Maika Takita, IBM Research

Day 3

Classical simulation of quantum computers with few nonClifford gates – Earl Campbell, University of Sheffield

Quantum advantage with shallow circuits – Sergey Bravyi, IBM Research

Quantum speed-ups for semidefinite programming – Fernando Brandão, CalTech

Classical simulation algorithms for quantum computational supremacy experiments – Ashley Montanaro, University of Bristol

Special thanks to the IBM event staff, and team that coordinated the 2017 Think Q.


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