Reducing the molecular electronic Hamiltonian encoding costs on quantum computers by symmetry shifts

Computational cost of energy estimation for molecular electronic Hamiltonians via quantum phase estimation (QPE) grows with the spectral norm of the Hamiltonian. In this work we propose a preprocessing procedure that reduces the norm of the Hamiltonian without changing its eigen-spectrum for the target states of a particular symmetry. The new procedure, block-invariant symmetry shift (BLISS), builds an operator T such that the cost of implementing H-T is reduced compared to that of H, yet H-T acts on the symmetric subspaces of interest the same way as H does. BLISS performance is demonstrated for linear combination of unitaries (LCU)-based QPE approaches on a set of small molecules. Using the number of electrons as the symmetry specifying the target set of states, BLISS provided a factor of 2-3 reduction of 1-norm compared to that in a single Pauli product LCU decomposition.

[1]  N. Wiebe,et al.  Reducing molecular electronic Hamiltonian simulation cost for linear combination of unitaries approaches , 2022, Quantum Science and Technology.

[2]  C. Gogolin,et al.  Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization , 2022, 2212.07957.

[3]  Daniel Stilck Francca,et al.  Quantum algorithm for ground state energy estimation using circuit depth with exponentially improved dependence on precision , 2022, 2209.06811.

[4]  Pablo Antonio Moreno Casares,et al.  Simulating key properties of lithium-ion batteries with a fault-tolerant quantum computer , 2022, Physical Review A.

[5]  Lin Lin,et al.  Heisenberg-Limited Ground-State Energy Estimation for Early Fault-Tolerant Quantum Computers , 2021, PRX Quantum.

[6]  S. Kais,et al.  Statistical approach to quantum phase estimation , 2021, New Journal of Physics.

[7]  M. Motta,et al.  Quantum Filter Diagonalization with Compressed Double-Factorized Hamiltonians , 2021, PRX Quantum.

[8]  F. Buda,et al.  Orbital transformations to reduce the 1-norm of the electronic structure Hamiltonian for quantum computing applications , 2021, Physical Review Research.

[9]  Earl T Campbell,et al.  Early fault-tolerant simulations of the Hubbard model , 2020, Quantum Science and Technology.

[10]  Joonho Lee,et al.  Even More Efficient Quantum Computations of Chemistry Through Tensor Hypercontraction , 2020, PRX Quantum.

[11]  Damian S. Steiger,et al.  Quantum computing enhanced computational catalysis , 2020, Physical Review Research.

[12]  Tzu-Ching Yen,et al.  Cartan Subalgebra Approach to Efficient Measurements of Quantum Observables , 2020, PRX Quantum.

[13]  S. Brierley,et al.  Efficient quantum measurement of Pauli operators in the presence of finite sampling error , 2020, Quantum.

[14]  Ryan Babbush,et al.  Low rank representations for quantum simulation of electronic structure , 2018, npj Quantum Information.

[15]  Timothy C. Berkelbach,et al.  Recent developments in the PySCF program package. , 2020, The Journal of chemical physics.

[16]  J. Whitfield,et al.  Reducing qubit requirements for quantum simulation using molecular point group symmetries. , 2019, Journal of chemical theory and computation.

[17]  Yuki Kurashige,et al.  A Jastrow-type decomposition in quantum chemistry for low-depth quantum circuits , 2019, 1909.12410.

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

[19]  Nathan Wiebe,et al.  Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers , 2019, 1907.13117.

[20]  Rolando D. Somma,et al.  Quantum eigenvalue estimation via time series analysis , 2019, New Journal of Physics.

[21]  Tzu-Ching Yen,et al.  Unitary partitioning approach to the measurement problem in the Variational Quantum Eigensolver method. , 2019, Journal of chemical theory and computation.

[22]  Ryan Babbush,et al.  Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization , 2019, Quantum.

[23]  Alán Aspuru-Guzik,et al.  Quantum Chemistry in the Age of Quantum Computing. , 2018, Chemical reviews.

[24]  G. Chan,et al.  Efficient Ab Initio Auxiliary-Field Quantum Monte Carlo Calculations in Gaussian Bases via Low-Rank Tensor Decomposition. , 2018, Journal of chemical theory and computation.

[25]  J. Ignacio Cirac,et al.  Faster ground state preparation and high-precision ground energy estimation with fewer qubits , 2017, Journal of Mathematical Physics.

[26]  I. Chuang,et al.  Hamiltonian Simulation by Qubitization , 2016, Quantum.

[27]  Alexandru Paler,et al.  Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity , 2018, Physical Review X.

[28]  Nathan Wiebe,et al.  Hamiltonian Simulation in the Interaction Picture , 2018, 1805.00675.

[29]  Patrick Kofod Mogensen,et al.  Optim: A mathematical optimization package for Julia , 2018, J. Open Source Softw..

[30]  Qi Huangfu,et al.  Parallelizing the dual revised simplex method , 2015, Mathematical Programming Computation.

[31]  Sandeep Sharma,et al.  PySCF: the Python‐based simulations of chemistry framework , 2018 .

[32]  B. Peng,et al.  Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations. , 2017, Journal of chemical theory and computation.

[33]  Iain Dunning,et al.  JuMP: A Modeling Language for Mathematical Optimization , 2015, SIAM Rev..

[34]  Sarah E. Sofia,et al.  The Bravyi-Kitaev transformation: Properties and applications , 2015 .

[35]  Qiming Sun,et al.  Libcint: An efficient general integral library for Gaussian basis functions , 2014, J. Comput. Chem..

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

[37]  Nathan Wiebe,et al.  Hamiltonian simulation using linear combinations of unitary operations , 2012, Quantum Inf. Comput..

[38]  I. Kassal,et al.  Polynomial-time quantum algorithm for the simulation of chemical dynamics , 2008, Proceedings of the National Academy of Sciences.

[39]  M. Head‐Gordon,et al.  Simulated Quantum Computation of Molecular Energies , 2005, Science.

[40]  L. Landau,et al.  Fermionic quantum computation , 2000 .

[41]  S. Lloyd,et al.  Quantum Algorithm Providing Exponential Speed Increase for Finding Eigenvalues and Eigenvectors , 1998, quant-ph/9807070.

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

[43]  M. Suzuki,et al.  General theory of fractal path integrals with applications to many‐body theories and statistical physics , 1991 .

[44]  J. Pople,et al.  Self‐Consistent Molecular‐Orbital Methods. I. Use of Gaussian Expansions of Slater‐Type Atomic Orbitals , 1969 .

[45]  E. Wigner,et al.  Über das Paulische Äquivalenzverbot , 1928 .