Reducing the Resources Required by ADAPT-VQE Using Coupled Exchange Operators and Improved Subroutines

Abstract

Adaptive variational quantum algorithms arguably offer the best prospects for quantum advantage in the Noisy Intermediate-Scale Quantum era. Since the inception of the first such algorithm, the Adaptive Derivative-Assembled Problem-Tailored Variational Quantum Eigensolver (ADAPT-VQE), many improvements have appeared in the literature. We combine the key improvements along with a novel operator pool -- which we term Coupled Exchange Operator (CEO) pool -- to assess the cost of running state-of-the-art ADAPT-VQE on hardware in terms of measurement counts and circuit depth. We show a dramatic reduction of these quantum computational resources compared to the early versions of the algorithm: CNOT count, CNOT depth and measurement costs are reduced by up to 88%, 96% and 99.6%, respectively, for molecules represented by 12 to 14 qubits (LiH, H6 and BeH2). We also find that our state-of-the-art CEO-ADAPT-VQE outperforms the Unitary Coupled Cluster Singles and Doubles ansatz, the most widely used static VQE ansatz, in all relevant metrics, and offers a five order of magnitude decrease in measurement costs as compared to other static ansätze with competitive CNOT counts.

References in corpus (41)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• A variational eigenvalue solver on a quantum processor
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• Quantum Circuit Learning
• Improved Simulation of Stabilizer Circuits
• Evaluating analytic gradients on quantum hardware
• An adaptive variational algorithm for exact molecular simulations on a quantum computer
• Variational Quantum Computation of Excited States
• Robust determination of molecular spectra on a quantum processor
• Generalized Unitary Coupled Cluster Wavefunctions for Quantum Computation
• qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor
• An initialization strategy for addressing barren plateaus in parametrized quantum circuits
• Measurement Optimization in the Variational Quantum Eigensolver Using a Minimum Clique Cover
• Exploiting symmetry in variational quantum machine learning
• Efficient quantum measurement of Pauli operators in the presence of finite sampling error
• Is the Trotterized UCCSD Ansatz chemically well-defined?
• Qubit-excitation-based adaptive variational quantum eigensolver
• Adaptive Variational Quantum Dynamics Simulations
• On barren plateaus and cost function locality in variational quantum algorithms
• Efficient quantum circuits for quantum computational chemistry
• ADAPT-VQE is insensitive to rough parameter landscapes and barren plateaus
• Adaptive Variational Quantum Imaginary Time Evolution Approach for Ground State Preparation
• A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers
• Solving Nuclear Structure Problems with the Adaptive Variational Quantum Algorithm
• Overlap-ADAPT-VQE: Practical Quantum Chemistry on Quantum Computers via Overlap-Guided Compact Ansätze
• Adaptive variational quantum eigensolvers for highly excited states
• An efficient adaptive variational quantum solver of the Schrodinger equation based on reduced density matrices
• Quantifying the effect of gate errors on variational quantum eigensolvers for quantum chemistry
• Improving quantum measurements by introducing "ghost" Pauli products
• Resource-Optimized Fermionic Local-Hamiltonian Simulation on Quantum Computer for Quantum Chemistry
• Avoiding symmetry roadblocks and minimizing the measurement overhead of adaptive variational quantum eigensolvers
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive
• Adaptive projected variational quantum dynamics
• Layering and subpool exploration for adaptive Variational Quantum Eigensolvers: Reducing circuit depth, runtime, and susceptibility to noise
• Fermionic Adaptive Sampling Theory for Variational Quantum Eigensolvers
• Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms
• Symmetry breaking slows convergence of the ADAPT Variational Quantum Eigensolver
• Ansätze for Noisy Variational Quantum Eigensolvers
• Toward Accurate Post-Born-Oppenheimer Molecular Simulations on Quantum Computers: An Adaptive Variational Eigensolver with Nuclear-Electronic Frozen Natural Orbitals

Cited by in corpus (4)

• Classical Pre-optimization Approach for ADAPT-VQE: Maximizing the Potential of High-Performance Computing Resources to Improve Quantum Simulation of Chemical Applications
• Counterdiabatic ADAPT-VQE for molecular simulation
• Improved Strategies for Fermionic Quantum Simulation with Global Interactions
• Shot-Efficient ADAPT-VQE via Reused Pauli Measurements and Variance-Based Shot Allocation