Synergy between quantum computing and high-performance computing

August 22, 2017 to August 24, 2017
Location : CECAM-ETHZ, Zurich, Switzerland


  • Rosa Di Felice (CNR Institute of Nanoscience, Modena, Italy)
  • Elisa Molinari (University of Modena and Reggio Emilia, Modena, Italy)
  • Sara Bonella (CECAM@EPFL, Switzerland)
  • Ivano Tavernelli (IBM-Zurich Research, Switzerland)
  • Daniel Lidar (USC, USA)
  • Seth Lloyd (Massachusetts Institute of Technology, USA)
  • Matthias Troyer (Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland)



   EU MaX CoE


The European Commission recently proposed to make €1 billion available for a Quantum Flagship, a large-scale European research program for quantum technology [1]. At the “Quantum Europe Conference” held in Amsterdam on May 17-18, 2016, academies and industries convened to define a roadmap for quantum technology; the industrial investment in quantum technologies was massively embodied by the giants Intel, Microsoft, Google and IBM, among others.

Some of the proposed activities call for a dialogue between the quantum computing (QC) community and the high-performance computing (HPC) community. In fact, with humongous hardware and software progress in the past 20 years, HPC has increasingly contributed to diverse scientific fields, ranging from materials science to biology and biomedicine, including genomics and neuroscience. Moreover, the European Commission has recently funded eight new centers of excellence for computing applications, including biomolecular systems, materials design and energy problems [2].

Quantum technology objectives that resonate with the HPC community are:
• Simulator of motion of electrons in materials;
• Development and design of new complex materials;
• Versatile simulator of quantum magnetism and electricity;
• Simulators of quantum dynamics and chemical reaction mechanisms to support drug design;
• Solving chemistry and materials science problems with special purpose quantum computer > 100 physical qubits;
• General purpose quantum computers exceed computational power of classical computers.

The quantum information community and the HPC community have proceeded so far mostly on independent tracks, but recently bridging works are appearing. Such works address the development of quantum machine learning algorithms [3-6] that may revolutionize materials design and genomics, the application of a quantum annealer for a proof-of-concept molecular dynamics simulation [7] and for transcription factor-DNA binding specificity [8], the integration of quantum processing units in current and future HPC systems [9] and the solution of quantum algorithms on classical HPC platforms [10], the implementation of quantum chemistry on quantum computers [11-17].

We propose to bring together the two communities to discuss their specific expertise and to outline the bridges that will eventually identify: (1) the future role of quantum technologies in scientific fields where HPC is currently dominant; (2) the use of existing HPC platforms to demonstrate the potentialities of future quantum technologies to simulate materials and biological systems. The ideas discussed in the workshop will be the basis of a wider brain storming to identify the synergy between HPC and quantum technologies at the European level and worldwide, possibly identifying HPC tasks in the Quantum Flagship.

Quantum simulation is the emulation by a controlled quantum system of another quantum system of interest in the physical sciences. It can be pursued according to digital or analogue routes. The former requires the availability of a universal digital quantum computer that can evolve a certain Hamiltonian by applying sequences of quantum gates. The latter, instead, proceeds, e.g., by mapping a lattice system of interest in condensed matter physics to the Hamiltonian of a lattice of trapped ions or atoms: measurement of this ionic or atomic system is equivalent to determining the properties of the target condensed matter system.

Simulation of quantum systems is nowadays successfully performed on high performance computing platforms, with efficiently parallelized codes that exploit the fastest networks. The scale of manageable systems has grown from few atoms to few thousands of atoms in the past century. Yet, this scale is still far from the natural scale of problems in biomedicine, drug design and materials science/engineering, which may be affected by the quantum behavior of the components. Quantum simulation will eventually bypass such size limitations, opening the exploration of quantum effects in life sciences and in the electrical/mechanical industry.

Thus, we propose to bring together quantum scientists, life scientists and materials scientists, to address three main questions:

1. How and to which extent will quantum computers impact HPC fields of research? Which kind of problems will we be able to address in life sciences and materials sciences with the fabrication of quantum computers?
2. What do we need to do to port HPC target quantum systems on quantum computers? Are there specific problems more suitable for gate-model quantum computers or adiabatic quantum optimizers?
3. How can we exploit HPC supercomputers and computing centers to simulate quantum chemistry with quantum algorithms? What can we do with quantum annealers in biology and materials science?

Below, in section 2, we list the prospective invited speakers: they have all been contacted; those who have already reacted positively are identified with an asterisk. Due to the timeliness of the subject, we expect to have several applications, among which we will select the contributed talks and posters.
In conjunction with the workshop, we are planning a Research Topic in Physics (



[3] Seth Lloyd, Silvano Garnerone, Paolo Zanardi. Quantum algorithms for topological and geometric analysis of data. Nature Commun. 7, 10138 (2016).
[4] Lloyd, S., Mohseni, M. & Rebentrost, P. Quantum algorithms for supervised and unsupervised machine learning. Preprint at (2013).
[5] Rebentrost, P., Mohseni, M. & Lloyd, S. Quantum support vector machine for big feature and big data classification. Phys. Rev. Lett. 113, 130503 (2014).
[6] Lloyd, S., Mohseni, M. & Rebentrost, P. Quantum principal component analysis. Nat. Phys. 10, 631–633 (2014).
[7] Alejandro Perdomo-Ortiz, Neil Dickson, Marshall Drew-Brook, Geordie Rose, Alán Aspuru-Guzik. Finding low-energy conformations of lattice protein models by quantum annealing. Sci. Rep. 2, 571 (2012).
[8] R. Li, R. Di Felice, R. Rohs, D. Lidar, work in progress.
[9] Keith A. Britt and Travis Humble. High-Performance Computing with Quantum Processing Units. arXiv:1511.04386v1 (2015).
[10] Thomas Häner, Damian S. Steiger, Mikhail Smelyanskiy, and Matthias Troyer. High Performance Emulation of Quantum Circuits. arXiv:1604.06460v1 (2016).
[11] B. P. Lanyon, J. D. Whitfield, G. G. Gillett, M. E. Goggin, M. P. Almeida, I. Kassal, J. D. Biamonte, M. Mohseni, B. J. Powell, M. Barbieri, A. Aspuru-Guzik and A. G. White. Towards quantum chemistry on a quantum computer. Nat. Chem. 2, 106 (2010).
[12] Nicolas P. D. Sawaya, Mikhail Smelyanskiy, Jarrod R. McClean, and Alán Aspuru-Guzik. Error sensitivity to environmental noise in quantum circuits for chemical state preparation. arXiv:1602.01857v2 (2016).
[13] Mikhail Smelyanskiy, Nicolas P. D. Sawaya, and Alán Aspuru-Guzik. qHiPSTER: The Quantum High Performance Software Testing Environment. arXiv:1601.07195v2 (2016).
[14] Ryan Babbush, Peter J. Love and Alán Aspuru-Guzik. Adiabatic Quantum Simulation of
Quantum Chemistry. Sci. Rep. 4, 6603 (2014).
[15] P. J. J. O'Malley, R. Babbush, I. D. Kivlichan, J. Romero, J. R. McClean, R. Barends, J. Kelly, P. Roushan, A. Tranter, N. Ding, B. Campbell, Y. Chen, Z. Chen, B. Chiaro, A. Dunsworth, A. G. Fowler, E. Jeffrey, A. Megrant, J. Y. Mutus, C. Neill, C. Quintana, D. Sank, A. Vainsencher, J. Wenner, T. C. White, P. V. Coveney, P. J. Love, H. Neven, A. Aspuru-Guzik, J. M. Martinis. Scalable Quantum Simulation of Molecular Energies. arXiv:1512.06860v1 (2016).
[16] Nikolaj Moll, Andreas Fuhrer, Peter Staar, Ivano Tavernelli. Optimizing qubit resources for quantum chemistry simulations in second quantization on a quantum computer. arXiv:1510.04048v3 (2016).
[17] Borzu Toloui and Peter Love. Quantum Algorithms for Quantum Chemistry based on the sparsity of the CI-matrix. arXiv:1312.2579v2 (2013).