Solving Quantum Chemistry Problems with a D-Wave Quantum Annealer

Quantum annealing devices have been subject to various analyses in order to classify their usefulness for practical applications. While it has been successfully proven that such systems can in general be used for solving combinatorial optimization problems, they have not been used to solve chemistry applications. In this paper we apply a mapping, put forward by Xia et al. (The Journal of Physical Chemistry B 122.13 (2017): 3384-3395.), from a quantum chemistry Hamiltonian to an Ising spin glass formulation and find the ground state energy with a quantum annealer. Additionally we investigate the scaling in terms of needed physical qubits on a quantum annealer with limited connectivity. To the best of our knowledge, this is the first experimental study of quantum chemistry problems on quantum annealing devices. We find that current quantum annealing technologies result in an exponential scaling for such inherently quantum problems and that new couplers are necessary to make quantum annealers attractive for quantum chemistry.

[1]  Steven H. Adachi,et al.  Application of Quantum Annealing to Training of Deep Neural Networks , 2015, ArXiv.

[2]  Alán Aspuru-Guzik,et al.  A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.

[3]  Hartmut Neven,et al.  Quantum simulation of chemistry with sublinear scaling in basis size , 2018, npj Quantum Information.

[4]  Alexei Y. Kitaev,et al.  Quantum measurements and the Abelian Stabilizer Problem , 1995, Electron. Colloquium Comput. Complex..

[5]  R. Feynman Simulating physics with computers , 1999 .

[6]  David Von Dollen,et al.  Traffic Flow Optimization Using a Quantum Annealer , 2017, Front. ICT.

[7]  Yudong Cao,et al.  OpenFermion: the electronic structure package for quantum computers , 2017, Quantum Science and Technology.

[8]  M. W. Johnson,et al.  Quantum annealing with manufactured spins , 2011, Nature.

[9]  Alán Aspuru-Guzik,et al.  Quantum Simulation of Electronic Structure with Linear Depth and Connectivity. , 2017, Physical review letters.

[10]  H. Neven,et al.  Low-Depth Quantum Simulation of Materials , 2018 .

[11]  G. Billman,et al.  An introduction to heart rate variability: methodological considerations and clinical applications , 2015, Front. Physiol..

[12]  Sabrina Hong,et al.  Demonstration of universal parametric entangling gates on a multi-qubit lattice , 2017, Science Advances.

[13]  S. Debnath,et al.  Demonstration of a small programmable quantum computer with atomic qubits , 2016, Nature.

[14]  P. Coveney,et al.  Scalable Quantum Simulation of Molecular Energies , 2015, 1512.06860.

[15]  Hartmut Neven,et al.  Low-Depth Quantum Simulation of Materials , 2017, 1706.00023.

[16]  Sabre Kais,et al.  Electronic Structure Calculations and the Ising Hamiltonian. , 2017, The journal of physical chemistry. B.

[17]  Alán Aspuru-Guzik,et al.  Adiabatic Quantum Simulation of Quantum Chemistry , 2013, Scientific Reports.

[18]  J. Whitfield,et al.  Simulation of electronic structure Hamiltonians using quantum computers , 2010, 1001.3855.

[19]  T. Monz,et al.  Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator , 2018, Physical Review X.

[20]  L. DiCarlo,et al.  Demonstration of two-qubit algorithms with a superconducting quantum processor , 2009, Nature.

[21]  David Von Dollen,et al.  Quantum-Assisted Cluster Analysis on a Quantum Annealing Device , 2018, Front. Phys..

[22]  J. Gambetta,et al.  Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.

[23]  B. Lanyon,et al.  Towards quantum chemistry on a quantum computer. , 2009, Nature chemistry.

[24]  Anna Levit,et al.  Free energy-based reinforcement learning using a quantum processor , 2017, ArXiv.

[25]  P. Love,et al.  The Bravyi-Kitaev transformation for quantum computation of electronic structure. , 2012, The Journal of chemical physics.

[26]  H. Neven,et al.  Digitized adiabatic quantum computing with a superconducting circuit. , 2015, Nature.