Quantum annealer accelerates the variational quantum eigensolver in a triple-hybrid algorithm

Abstract

Hybrid algorithms that combine quantum and classical resources have become commonplace in quantum computing. The variational quantum eigensolver (VQE) is routinely used to solve prototype problems. Currently, hybrid algorithms use no more than one kind of quantum computer connected to a classical computer. In this work, a novel triple-hybrid algorithm combines the effective use of a classical computer, a gate-based quantum computer, and a quantum annealer. The solution of a graph coloring problem found using a quantum annealer reduces the resources needed from a gate-based quantum computer to accelerate VQE by allowing simultaneous measurements within commuting groups of Pauli operators. We experimentally validate our algorithm by evaluating the ground state energy of H${}_2$ using different IBM Q devices and the DWave Advantage system requiring only half the resources of standard VQE. Other larger problems we consider exhibit even more significant VQE acceleration. Several examples of algorithms are provided to further motivate a new field of multi-hybrid algorithms that leverage different kinds of quantum computers to gain performance improvements.

References in corpus (35)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• A variational eigenvalue solver on a quantum processor
• Variational Quantum Algorithms
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Ising formulations of many NP problems
• The theory of variational hybrid quantum-classical algorithms
• A Quantum Approximate Optimization Algorithm
• Trapped-Ion Quantum Computing: Progress and Challenges
• Towards Practical Quantum Variational Algorithms
• Quantum Annealing: A New Method for Minimizing Multidimensional Functions
• Self-Verifying Variational Quantum Simulation of the Lattice Schwinger Model
• The Bravyi-Kitaev transformation for quantum computation of electronic structure
• Gate count estimates for performing quantum chemistry on small quantum computers
• Experimental Investigation of an Eight Qubit Unit Cell in a Superconducting Optimization Processor
• Measurement Optimization in the Variational Quantum Eigensolver Using a Minimum Clique Cover
• Accelerated Variational Quantum Eigensolver
• Reverse Quantum Annealing Approach to Portfolio Optimization Problems
• Quantum Annealing Implementation of Job-Shop Scheduling
• Benchmarking the Quantum Approximate Optimization Algorithm
• Reverse annealing for the fully connected $p$-spin model
• Domain wall encoding of discrete variables for quantum annealing and QAOA
• Minimizing State Preparations in Variational Quantum Eigensolver by Partitioning into Commuting Families
• Dynamics of reverse annealing for the fully-connected $p$-spin model
• Quantum-circuit design for efficient simulations of many-body quantum dynamics
• Qubit spin ice
• Simultaneous execution of quantum circuits on current and near-future NISQ systems
• Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era
• Solving Larger Maximum Clique Problems Using Parallel Quantum Annealing
• Accelerating the variational quantum eigensolver using parallelism
• Accelerated variational quantum eigensolver with joint Bell measurement
• Mitigating algorithmic errors in quantum optimization through energy extrapolation
• Improved variational quantum eigensolver via quasi-dynamical evolution
• General error mitigation for quantum circuits
• Initial State Encoding via Reverse Quantum Annealing and h-gain Features
• Graph Coloring with Quantum Annealing

Cited by in corpus (2)

• Reverse quantum annealing assisted by forward annealing
• Slice-Wise Initial State Optimization to Improve Cost and Accuracy of the VQE on Lattice Models