Greedy parameter optimization for diabatic quantum annealing

Abstract

A shorter processing time is desirable for quantum computation to minimize the effects of noise. We propose a simple procedure to variationally determine a set of parameters in the transverse-field Ising model for quantum annealing appended with a field along the $y$ axis. The method consists of greedy optimization of the signs of coefficients of the $y$-field term based on the outputs of short annealing processes. We test the idea in the ferromagnetic system with all-to-all couplings and spin-glass problems, and find that the method outperforms the traditional form of quantum annealing and simulated annealing in terms of the success probability and the time to solution, in particular in the case of shorter annealing times, achieving the goal of improved performance while avoiding noise. The non-stoquastic ${\sigma }^y$ term can be eliminated by a rotation in the spin space, resulting in a non-trivial diabatic control of the coefficients in the stoquastic transverse-field Ising model, which may be feasible for experimental realization.

arXiv comment18 pages, 15 figures

References in corpus (45)

• Quantum Annealing in the Transverse Ising Model
• QuTiP 2: A Python framework for the dynamics of open quantum systems
• Barren plateaus in quantum neural network training landscapes
• A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
• Adiabatic Quantum Computing
• Theory of Quantum Annealing of an Ising Spin Glass
• Quantum Annealing and Analog Quantum Computation
• Fast optimal frictionless atom cooling in harmonic traps
• Shortcuts to adiabaticity
• Shortcuts to adiabaticity by counter-diabatic driving
• Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
• Mathematical Foundation of Quantum Annealing
• Lewis-Riesenfeld invariants and transitionless tracking algorithm
• Minimizing irreversible losses in quantum systems by local counter-diabatic driving
• Reverse Quantum Annealing Approach to Portfolio Optimization Problems
• Floquet-engineering counterdiabatic protocols in quantum many-body systems
• First order phase transition in the Quantum Adiabatic Algorithm
• Generating shortcuts to adiabaticity in quantum and classical dynamics
• Application of Quantum Annealing to Nurse Scheduling Problem
• Quantum annealing with antiferromagnetic fluctuations
• Power of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers
• Transitionless quantum driving for spin systems
• Simple Glass Models and their Quantum Annealing
• Reverse annealing for the fully connected $p$-spin model
• First-order transitions and the performance of quantum algorithms in random optimization problems
• Exponential Enhancement of the Efficiency of Quantum Annealing by Non-Stochastic Hamiltonians
• Exponential Speedup of Quantum Annealing by Inhomogeneous Driving of the Transverse Field
• Dynamics of reverse annealing for the fully-connected $p$-spin model
• Benchmarking Quantum Annealing Controls with Portfolio Optimization
• Many-body transverse interactions in the quantum annealing of the p-spin ferromagnet
• Shortcuts to adiabaticity for quantum annealing
• Reverse quantum annealing of the $p$-spin model with relaxation
• Counterdiabatic driving in the quantum annealing of the $p$-spin model: a variational approach
• Quantum annealing of the $p$-spin model under inhomogeneous transverse field driving
• Controlling and exploring quantum systems by algebraic expression of adiabatic gauge potential
• Rapid counter-diabatic sweeps in lattice gauge adiabatic quantum computing
• Reverse Annealing for Nonnegative/Binary Matrix Factorization
• Universal dynamics of inhomogeneous quantum phase transitions: suppressing defect formation
• Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods
• Two-parameter counter-diabatic driving in quantum annealing
• Mean field analysis of reverse annealing for code-division multiple-access multiuser detection
• Inhomogeneous quasi-adiabatic driving of quantum critical dynamics in weakly disordered spin chains
• Steering random spin systems to speed up the quantum adiabatic algorithm
• Engineering non-equilibrium quantum phase transitions via causally gapped Hamiltonians
• Fluctuation guided search in quantum annealing

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