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
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
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.