Qubit-ADAPT-VQE: An Adaptive Algorithm for Constructing Hardware-Efficient Ansätze on a Quantum Processor

Quantum simulation, one of the most promising applications of a quantum computer, is currently being explored intensely using the variational quantum eigensolver. The feasibility and performance of this algorithm depend critically on the form of the wavefunction ansatz. Recently in Nat. Commun. 10, 3007 (2019), an algorithm termed ADAPT-VQE was introduced to build system-adapted ans\"atze with substantially fewer variational parameters compared to other approaches. This algorithm relies heavily on a predefined operator pool with which it builds the ansatz. However, Nat. Commun. 10, 3007 (2019) did not provide a prescription for how to select the pool, how many operators it must contain, or whether the resulting ansatz will succeed in converging to the ground state. In addition, the pool used in that work leads to state preparation circuits that are too deep for a practical application on near-term devices. Here, we address all these key outstanding issues of the algorithm. We present a hardware-efficient variant of ADAPT-VQE that drastically reduces circuit depths using an operator pool that is guaranteed to contain the operators necessary to construct exact ans\"atze. Moreover, we show that the minimal pool size that achieves this scales linearly with the number of qubits. Through numerical simulations on $\text{H}_4$, LiH and $\text{H}_6$, we show that our algorithm ("qubit-ADAPT") reduces the circuit depth by an order of magnitude while maintaining the same accuracy as the original ADAPT-VQE. A central result of our approach is that the additional measurement overhead of qubit-ADAPT compared to fixed-ansatz variational algorithms scales only linearly with the number of qubits. Our work provides a crucial step forward in running algorithms on near-term quantum devices.

[1]  Harper R. Grimsley,et al.  Is the Trotterized UCCSD Ansatz Chemically Well-Defined? , 2019, Journal of chemical theory and computation.

[2]  R. Bartlett,et al.  Coupled-cluster theory in quantum chemistry , 2007 .

[3]  Harper R. Grimsley,et al.  An adaptive variational algorithm for exact molecular simulations on a quantum computer , 2018, Nature Communications.

[4]  K. B. Whaley,et al.  Generalized Unitary Coupled Cluster Wave functions for Quantum Computation. , 2018, Journal of chemical theory and computation.

[5]  Ryan Babbush,et al.  Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.

[6]  Isaac L. Chuang,et al.  Quantum Computation and Quantum Information (10th Anniversary edition) , 2011 .

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

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

[9]  Ivano Tavernelli,et al.  Gate-Efficient Simulation of Molecular Eigenstates on a Quantum Computer , 2018, Physical Review Applied.

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

[11]  W. Kohn,et al.  Self-Consistent Equations Including Exchange and Correlation Effects , 1965 .

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

[13]  Jonathan Romero,et al.  Low-depth circuit ansatz for preparing correlated fermionic states on a quantum computer , 2018, Quantum Science and Technology.

[14]  D. Abrams,et al.  Simulation of Many-Body Fermi Systems on a Universal Quantum Computer , 1997, quant-ph/9703054.

[15]  Bryan O'Gorman,et al.  A non-orthogonal variational quantum eigensolver , 2019 .

[16]  Nicholas J. Mayhall,et al.  Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm , 2019, npj Quantum Information.

[17]  M. Yung,et al.  Quantum implementation of the unitary coupled cluster for simulating molecular electronic structure , 2015, 1506.00443.

[18]  Francesco A. Evangelista,et al.  Exact parameterization of fermionic wave functions via unitary coupled cluster theory. , 2019, The Journal of chemical physics.

[19]  Ivano Tavernelli,et al.  Quantum algorithms for electronic structure calculations: Particle-hole Hamiltonian and optimized wave-function expansions , 2018, Physical Review A.

[20]  Ryan Babbush,et al.  The theory of variational hybrid quantum-classical algorithms , 2015, 1509.04279.

[21]  Scott N. Genin,et al.  Qubit Coupled Cluster Method: A Systematic Approach to Quantum Chemistry on a Quantum Computer. , 2018, Journal of chemical theory and computation.

[22]  Jonathan Carter,et al.  Computation of Molecular Spectra on a Quantum Processor with an Error-Resilient Algorithm , 2018 .

[23]  P. Hohenberg,et al.  Inhomogeneous electron gas , 1964 .

[24]  Nathan Wiebe,et al.  Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers , 2019, npj Quantum Information.

[25]  J. McClean,et al.  Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz , 2017, Quantum Science and Technology.

[26]  Alán Aspuru-Guzik,et al.  Quantum computational chemistry , 2018, Reviews of Modern Physics.

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

[28]  L. Lamata,et al.  From transistor to trapped-ion computers for quantum chemistry , 2013, Scientific Reports.

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

[30]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.