Background
Quantum computing is an emerging technology that has significant disruptive power, because it opens up the possibility to tackle certain types of problems that are beyond the reach of classical computers. Hardware development has gained significant momentum in recent years, and ”quantum supremacy” as recently been achieved[1]. It is expected by leading companies that the first noisy intermediate-scale quantum (NISQ) devices will be commercialized in the next 3 to 5 years. However, it is of yet unclear if there exist practically relevant applications (algorithms) where NISQ computers can outperform their classical counterparts.
Problems to be studied
In order to solve practically relevant problems with the help of NISQ devices faster than on classical computers, two main problems have to be overcome: Firstly, due to short decoherence time, only a short set of instructions/gates can be executed (shallow depth circuits). The design of hybrid quantum-classical algorithms fits well with this requirement. Examples are the variational quantum eigensolver (VQE) [6, 5] for quantum chemistry problems and the quantum approximate optimization algorithm (QAOA) [3] for optimization problems. Secondly, the computation on the NISQ computer will be heavily influenced by noise. Noise occurs due to unwanted interactions with the environment or imperfections of circuit elements, i.e., initialization, operations/gates, and measurement. To remedy this and extend the computational reach of NISQ devices, two quantum error mitigation (QEM) techniques have been introduced recently, namely error extrapolation and quasi-probability decomposition, see e.g. [7, 2, 4] and references therein.
Goals of the project
There are three primary goals of the project:
- Developing a sound theoretical understanding of quantum error mitigation, including basic principles and current state of the art.
- Implementing QEM procedures on simulators and actual quantum computers. For execution on real devices, it is suggested to use e.g., IBM’s gate based quantum compute available free online.
- After verification of the implemented QEM procedures on toy examples, they can be tested on problems using VQE and/or QAOA.
Time allowing, one should in addition aspire to find and test improvements on existing approaches. The project will be supervised by Joakim Bergli and researchers from SINTEF
References
[1] Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando G. S. L. Brandao, David A. Buell, Brian Burkett, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Andrew Dunsworth, Edward Farhi, Brooks Foxen, Austin Fowler, Craig Gidney, Marissa Giustina, Rob Graff, Keith Guerin, Steve Habegger, Matthew P. Harrigan, Michael J. Hartmann, Alan Ho, Markus Hoffmann, Trent Huang, Travis S. Humble, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Paul V. Klimov, Sergey Knysh, Alexander Korotkov, Fedor Kostritsa, David Landhuis, Mike Lindmark, Erik Lucero, Dmitry Lyakh, Salvatore Mandr`a, Jarrod R. McClean, Matthew McEwen, Anthony Megrant, Xiao Mi, Kristel Michielsen, Masoud Mohseni, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles Neill, Murphy Yuezhen Niu, Eric Ostby, Andre Petukhov, John C. Platt, Chris Quintana, Eleanor G. Rieffel, Pedram Roushan, Nicholas C. Rubin, Daniel Sank, Kevin J. Satzinger, Vadim Smelyanskiy, Kevin J. Sung, Matthew D. Trevithick, Amit Vainsencher, Benjamin Villalonga, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Hartmut Neven, and John M. Martinis.
Quantum supremacy using a programmable superconducting processor. Nature, 574(7779):505–510, October 2019.
[2] Suguru Endo, Simon C Benjamin, and Ying Li. Practical quantum error mitigation for near-future applications. Physical Review X , 8(3):031027, 2018.
[3] Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028, 2014.
[4] Abhinav Kandala, Kristan Temme, Antonio D C ́orcoles, Antonio Mezzacapo, Jerry M Chow, and Jay M Gambetta. Error mitigation extends the computational reach of a noisy quantum processor. Nature, 567(7749):491, 2019.
[5] Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Al ́an Aspuru-Guzik. The theory of variational hybrid quantum-classical algorithms. New Journal of Physics, 18(2):023023, 2016.
[6] Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alan Aspuru-Guzik, and Jeremy L O’brien. A variational eigenvalue solver on a photonic quantum processor. Nature communications, 5:4213, 2014.
[7] Kristan Temme, Sergey Bravyi, and Jay M Gambetta. Error mitigation for short-depth quantumcircuits . Physical review letters, 119(18):180509, 2017