Variationally Scheduled Quantum Simulation

Abstract

Eigenstate preparation is ubiquitous in quantum computing, and a standard approach for generating the lowest-energy states of a given system is by employing adiabatic state preparation (ASP). In the present work, we investigate a variational method for determining the optimal scheduling procedure within the context of ASP. In the absence of quantum error correction, running a quantum device for any meaningful amount of time causes a system to become susceptible to the loss of relevant information. Therefore, if accurate quantum states are to be successfully generated, it is crucial to find techniques that shorten the time of individual runs during iterations of annealing. We demonstrate our variational method toward this end by investigating the hydrogen and P4 molecules, as well as the Ising model problem on a two-dimensional triangular lattice. In both cases, the time required for one iteration to produce accurate results is reduced by several orders of magnitude in comparison to what is achievable via standard ASP. As a result, the required quantum coherence time to perform such a calculation on a quantum device becomes much less stringent with the implementation of this algorithm. In addition, our variational method is found to exhibit resilience against control errors, which are commonly encountered within the realm of quantum computing.

Reference added

References in corpus (23)

• Probing many-body dynamics on a 51-atom quantum simulator
• QuTiP 2: A Python framework for the dynamics of open quantum systems
• A Quantum Approximate Optimization Algorithm
• Predicting Many Properties of a Quantum System from Very Few Measurements
• Exponential algorithmic speedup by quantum walk
• Tapering off qubits to simulate fermionic Hamiltonians
• Decoherence in adiabatic quantum computation
• Prospects for Quantum Enhancement with Diabatic Quantum Annealing
• Quantum annealing with antiferromagnetic fluctuations
• Optimal Protocols in Quantum Annealing and QAOA Problems
• Noise resistance of adiabatic quantum computation using random matrix theory
• Exponential Enhancement of the Efficiency of Quantum Annealing by Non-Stochastic Hamiltonians
• Exponential Speedup of Quantum Annealing by Inhomogeneous Driving of the Transverse Field
• Many-body transverse interactions in the quantum annealing of the p-spin ferromagnet
• Quantum Adiabatic Evolution Algorithms with Different Paths
• Reverse quantum annealing of the $p$-spin model with relaxation
• Improving quantum annealing of the ferromagnetic $p$-spin model through pausing
• The quantum adiabatic search with decoherence in the instantaneous energy eigenbasis
• Testing quantum adiabaticity with quench echo
• Variational optimization of the quantum annealing schedule for the Lechner-Hauke-Zoller scheme
• A double-slit proposal for quantum annealing
• Noise reduction using past causal cones in variational quantum algorithms
• Quantum annealing with a nonvanishing final value of the transverse field

Cited by in corpus (15)

• Genetic optimization of quantum annealing
• Designing Quantum Annealing Schedules using Bayesian Optimization
• Improving the variational quantum eigensolver using variational adiabatic quantum computing
• Diabatic Quantum Annealing for the Frustrated Ring Model
• Deep learning optimal quantum annealing schedules for random Ising models
• Quantum annealing with twisted fields
• Phase transitions in the frustrated Ising ladder with stoquastic and nonstoquastic catalysts
• Beyond Quantum Annealing: Optimal control solutions to MaxCut problems
• Post-processing variationally scheduled quantum algorithm for constrained combinatorial optimization problems
• From quantum-enhanced to quantum-inspired Monte Carlo
• Lagrangian Duality in Quantum Optimization: Overcoming QUBO Limitations for Constrained Problems
• Noise Effects on Diabatic Quantum Annealing Protocols
• Hardware-efficient quantum annealing with error mitigation via classical shadow
• Hidden local adiabatic ramp in the modulated time evolution and the quantum approximate optimization algorithm
• Digital controllability of transverse field Ising chains