REVIEW 6 cited by
MoG-VQE: Multiobjective genetic variational quantum eigensolver
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Variational quantum eigensolver (VQE) emerged as a first practical algorithm for near-term quantum computers. Its success largely relies on the chosen variational ansatz, corresponding to a quantum circuit that prepares an approximate ground state of a Hamiltonian. Typically, it either aims to achieve high representation accuracy (at the expense of circuit depth), or uses a shallow circuit sacrificing the convergence to the exact ground state energy. Here, we propose the approach which can combine both low depth and improved precision, capitalizing on a genetically-improved ansatz for hardware-efficient VQE. Our solution, the multiobjective genetic variational quantum eigensolver (MoG-VQE), relies on multiobjective Pareto optimization, where topology of the variational ansatz is optimized using the non-dominated sorting genetic algorithm (NSGA-II). For each circuit topology, we optimize angles of single-qubit rotations using covariance matrix adaptation evolution strategy (CMA-ES) -- a derivative-free approach known to perform well for noisy black-box optimization. Our protocol allows preparing circuits that simultaneously offer high performance in terms of obtained energy precision and the number of two-qubit gates, thus trying to reach Pareto-optimal solutions. Tested for various molecules (H$_2$, H$_4$, H$_6$, BeH$_2$, LiH), we observe nearly ten-fold reduction in the two-qubit gate counts as compared to the standard hardware-efficient ansatz. For 12-qubit LiH Hamiltonian this allows reaching chemical precision already at 12 CNOTs. Consequently, the algorithm shall lead to significant growth of the ground state fidelity for near-term devices.
Forward citations
Cited by 6 Pith papers
-
Quantum Architecture Search for Solving Quantum Machine Learning Tasks
A reinforcement learning framework (RL-QAS) discovers compact variational quantum circuit architectures for Iris and binary MNIST classification, outperforming a simple strongly-entangling-layer baseline.
-
Enhanced image classification via hybridizing quantum dynamics with classical neural networks
A classical encoder trained with a pairwise fidelity loss feeds images into a transverse-field Ising chain, and the resulting quantum states are classified by class-averaged observables, reporting higher accuracy than...
-
Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
A diffusion model is extended to generate both the architecture and the continuous gate parameters of parameterized quantum circuits, conditioned on target performance like fidelity or accuracy.
-
Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms
In small variational quantum eigensolver problems, high Hamiltonian expressibility helps for superposition-state problems while low expressibility helps for basis-state problems.
-
Genetic Transformer-Assisted Quantum Neural Networks for Optimal Circuit Design
A transformer front-end plus NSGA-II circuit search produces compact quantum classifiers that match or beat previous quantum models on Iris, Breast Cancer, MNIST (3 digits), and Heart Disease.
-
Practical Fidelity Limits of Toffoli Gates in Superconducting Quantum Processors
Benchmarking a decomposed Toffoli gate on IBM quantum hardware yields 56-64% state fidelities, but the claimed state-dependent error pattern is confounded by using different devices.
Discussion (0). Sign in to comment.