Keith A. Britt
Travis S. Humble
Skip Abstract Section
Abstract
The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for functional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well as a loose integration that assumes they cannot. We find that the performance of both approaches is likely to depend on the quantum interconnect that serves to entangle multiple QPUs. We also identify several challenges in assessing QPU performance for HPC, and we consider new metrics that capture the interplay between system architecture and the quantum parallelism underlying computational performance.
- Ali Javadi Abhari, Arvin Faruque, Mohammad Javad Dousti, Lukas Svec, Oana Catu, Amlan Chakrabati, Chen-Fu Chiang, Seth Vanderwilt, John Black, Fred Chong, Margaret Martonosi, Martin Suchara andKen Brown, Massoud Pedram, and Todd Brun. 2012. Scaffold: Quantum Programming Language. Technical Report. Retrieved from ftp://ftp.cs.princeton.edu/techreports/2012/934.pdfGoogle Scholar
- Daniel S. Abrams and Seth Lloyd. 1997. Simulation of many-body fermi systems on a universal quantum computer. Phys. Rev. Lett. 79, 13 (Sep. 1997), 2586--2589.Google ScholarCross Ref
- James Ang, Keren Bergman, Shekhar Borkar, William Carlson, Laura Carrington, George Chiu, Robert Colwell, William Dally, Jack Dongarra, Al Geist, Gary Grider, Rud Haring, Jeffrey Hittinger, Adolfy Hoisie, Dean Klein, Peter Kogge, Richard Lethin, Vivek Sarkar, Robert Schreiber, John Shalf, Thomas Sterling, and Rick Stevens. 2010. Top Ten Exascale Research Challenges. Technical Report. DOE ASCAC Subcommittee Report.Google Scholar
- Steve Ashby, Pete Beckman, Jackie Chen, Phil Colella, Bill Collins, Dona Crawford, Jack Dongarra, Doug Kothe, Rusty Lusk, Paul Messina, Tony Mezzacappa, Parviz Moin, Mike Norman, Robert Rosner, Vivek Sarkar, Andrew Siegel, Fred Streitz, Andy White, and Margaret Wright. 2010. The Opportunities and Challenges of Exascale Computing. Technical Report. Summary Report of the Advanced Scientific Computing Advisory Committee Subcommittee.Google Scholar
- Bela Bauer, Dave Wecker, Andrew J. Millis, Matthew B. Hastings, and Matthias Troyer. 2016. Hybrid quantum-classical approach to correlated materials. Phys. Rev. X 6, 3 (Sep. 2016), 39.Google Scholar
- A. Broadbent, J. Fitzsimons, and E. Kashefi. 2009. Universal blind quantum computation. In 2009 50th Annual IEEE Symposium on Foundations of Computer Science. 517--526. Google ScholarDigital Library
- Jacques Carolan, Christopher Harrold, Chris Sparrow, Enrique Martn-Lpez, Nicholas J. Russell, Joshua W. Silverstone, Peter J. Shadbolt, Nobuyuki Matsuda, Manabu Oguma, Mikitaka Itoh, Graham D. Marshall, Mark G. Thompson, Jonathan C. F. Matthews, Toshikazu Hashimoto, Jeremy L. OBrien, and Anthony Laing. 2015. Universal linear optics. Science 349, 6249 (2015), 711--716.Google Scholar
- Andrew M. Childs and Wim van Dam. 2010. Quantum algorithms for algebraic problems. Rev. Mod. Phys. 82, 1 (Jan 2010), 1--52.Google ScholarCross Ref
- Venkat R. Dasari, Ronald J. Sadlier, Ryan Prout, Brian P. Williams, and Travis S. Humble. 2016. Programmable Multi-Node Quantum Network Design and Simulation. Proc. SPIE 9873, Quantum Information and Computation IX, 98730B (May 19, 2016).Google Scholar
- D. Deutsch. 1985. Quantum theory, the Church-Turing principle and the universal quantum computer. Proc. Roy. Soc. Lond. Ser. A 400 (July 1985), 97--117.Google ScholarCross Ref
- M. H. Devoret and R. J. Schoelkopf. 2013. Superconducting circuits for quantum information: An outlook. Science 339, 6124 (2013), 1169--1174.Google Scholar
- D. P. DiVincenzo. 2000. The physical implementation of quantum computation. Fortschr. Phys. 48 (2000), 771783.Google ScholarCross Ref
- A. Geist and R. Lucas. 2009. Major computer science challenges at exascale. Int. J. High. Perform. Comput. Appl. 23 (2009), 427436. Issue 4. Google ScholarDigital Library
- Alexander S. Green, Peter LeFanu Lumsdaine, Neil J. Ross, Peter Selinger, and Benoît Valiron. 2013. Quipper: A scalable quantum programming language. In Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’13). ACM, New York, NY, 333--342. Google ScholarDigital Library
- Charles D. Hill, Eldad Peretz, Samuel J. Hile, Matthew G. House, Martin Fuechsle, Sven Rogge, Michelle Y. Simmons, and Lloyd C. L. Hollenberg. 2015. A surface code quantum computer in silicon. Sci. Adv. 1, 9 (2015), e1500707.Google ScholarCross Ref
- J. M. Hornibrook, J. I. Colless, I. D. Conway Lamb, S. J. Pauka, H. Lu, A. C. Gossard, J. D. Watson, G. C. Gardner, S. Fallahi, M. J. Manfra, and D. J. Reilly. 2015. Cryogenic control architecture for large-scale quantum computing. Phys. Rev. Appl. 3 (Feb. 2015), 024010.Google ScholarCross Ref
- T. S. Humble, A. J. McCaskey, R. S. Bennink, J. J. Billings, E. F. DAzevedo, B. D. Sullivan, C. F. Klymko, and H. Seddiqi. 2014. An integrated programming and development environment for adiabatic quantum optimization. Comput. Sci. Discov. 7, 1 (2014), 015006.Google ScholarCross Ref
- M. W. Johnson, M. H. S. Amin, S. Gildert, T. Lanting, F. Hamze, N. Dickson, R. Harris, A. J. Berkley, J. Johansson, P. Bunyk, and others. 2011. Quantum annealing with manufactured spins. Nature 473, 7346 (2011), 194--198.Google Scholar
- N. Cody Jones, Rodney Van Meter, Austin G. Fowler, Peter L. McMahon, Jungsang Kim, Thaddeus D. Ladd, and Yoshihisa Yamamoto. 2012. Layered architecture for quantum computing. Phys. Rev. X 2, 3 (2012), 031007.Google ScholarCross Ref
- Ivan Kassal, James D. Whitfield, Alejandro Perdomo-Ortiz, Man-Hong Yung, and Alán Aspuru-Guzik. 2011. Simulating chemistry using quantum computers. Annu. Rev. Phys. Chem. 62, 1 (2011), 185--207.Google ScholarCross Ref
- Volodymyr Kindratenko, George K. Thiruvathukal, and Steven Gottlieb. 2008. High-performance computing applications on novel architectures. Comput. Sci. Eng. 10, 6 (2008), 13--15. Google ScholarDigital Library
- Emmanuel Knill. 1996. Conventions for Quantum Pseudocode. Technical Report. Technical Report LAUR-96-2724, Los Alamos National Laboratory.Google Scholar
- J. M. Kreula, S. R. Clark, and D. Jaksch. 2015. A Quantum Coprocessor for Accelerating Simulations of Non-equilibrium many Body Quantum Dynamics. arXiv:1510.05703 {quant-ph}.Google Scholar
- Rodney van Meter and Mark Oskin. 2006. Architectural implications of quantum computing technologies. ACM J. Emerg. Technol. Comput. Syst. 2, 1 (2006), 31--63. Google ScholarDigital Library
- Tzvetan S. Metodi, Arvin I. Faruque, and Frederic T. Chong. 2011. Quantum Computing for Computer Architects, (2nd ed.). Morgan 8 Claypool Publishers. Google ScholarDigital Library
- C. Monroe and J. Kim. 2013. Scaling the ion trap quantum processor. Science 339, 6124 (2013), 1164--1169.Google Scholar
- Michael A. Nielsen and Isaac L. Chuang. 2000. Quantum Computation and Quantum Information. Cambridge University Press. Google ScholarDigital Library
- Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik, and Jeremy L. OBrien. 2014. A variational eigenvalue solver on a photonic quantum processor. Nat. Commun. 5 (Jul. 2014).Google Scholar
- Kamyar Saeedi, Stephanie Simmons, Jeff Z. Salvail, Phillip Dluhy, Helge Riemann, Nikolai V. Abrosimov, Peter Becker, Hans-Joachim Pohl, John J. L. Morton, and Mike L. W. Thewalt. 2013. Room-temperature quantum bit storage exceeding 39 minutes using ionized donors in silicon-28. Science 342, 6160 (2013), 830--833.Google Scholar
- Barry I. Schneider. 2015. The impact of heterogeneous computer architectures on computational physics. Comput. Sci. Eng. 17, 2 (Mar 2015), 9--13.Google ScholarCross Ref
- Peter Selinger. 2004. Towards a quantum programming language. Math. Struct. Comput. Sci. 14 (8 2004), 527--586. Issue 04. Google ScholarDigital Library
- Peter W. Shor. 1997. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26, 5 (1997), 1484--1509. Google ScholarDigital Library
- Daniel R. Simon. 1997. On the power of quantum computation. SIAM J. Comput. 26, 5 (1997), 1474--1483. DOI:http://dx.doi.org/S0097539796298637 Google ScholarDigital Library
- Darshan D. Thaker, Tzvetan S. Metodi, Andrew W. Cross, Isaac L. Chuang, and Frederic T. Chong. 2006. Quantum memory hierarchies: Efficient designs to match available parallelism in quantum computing. SIGARCH Comput. Archit. News 34, 2 (May 2006), 378--390. Google ScholarDigital Library
- Rodney Van Meter, Thaddeus D. Ladd, Austin G. Fowler, and Yoshihisa Yamamoto. 2010. Distributed quantum computation architecture using semiconductor nanophotonics. Int. J. Quant. Inf. 8, 01n02 (2010), 295--323.Google ScholarCross Ref
- Rob V. van Nieuwpoort, Thilo Kielmann, and Henri E. Bal. 2001. Efficient load balancing for wide-area divide-and-conquer applications. SIGPLAN Not. 36, 7 (Jun. 2001), 34--43. Google ScholarDigital Library
- Dave Wecker and Krysta M. Svore. 2014. LIQUID: A Software Design Architecture and Domain-Specific Language for Quantum Computing. Retrieved from http://arxiv.org/pdf/1402.4467v1.pdf.Google Scholar
- M.-H. Yung, J. Casanova, A. Mezzacapo, J. McClean, L. Lamata, A. Aspuru-Guzik, and E. Solano. 2014. From transistors to trapped-ion computers for quantum chemistry. Sci. Rep. 4, 3589 (2014).Google Scholar
Index Terms
- High-Performance Computing with Quantum Processing Units
Recommendations
Algorithmic performance studies on graphics processing units
We report on our experience with integrating and using graphics processing units (GPUs) as fast parallel floating-point co-processors to accelerate two fundamental computational scientific kernels on the GPU: sparse direct factorization and nonlinear ...
Quantum computing simulator on a heterogenous HPC system
CF '19: Proceedings of the 16th ACM International Conference on Computing FrontiersQuantum computing simulation on a classical computer is difficult due to the exponential runtime and memory overhead. Previous work addresses the difficulty by utilizing multiple Graphical Processing Units (GPUs) and multi-node computers. GPUs are ...
Performance of the NVIDIA Jetson TK1 in HPC
CLUSTER '15: Proceedings of the 2015 IEEE International Conference on Cluster ComputingThe NVIDIA Jetson is demonstrated as a competitiveHPC platform. The Jetson has 192 Kepler CUDA cores that are"true" in that they share a processor: in the case of the Jetson, a32-bit ARM Cortex-A15 variant low power architecture. Ourwork explores the ...
Comments