A Filtered Sequential Approximate Optimization Algorithm Based on Dual Subproblems Using an Enhanced Two-point Diagonal Quadratic Approximation for Structural Optimization

2012 ◽  
Author(s):  
Seonho Park ◽  
Seung-Hyun Jeong ◽  
Dong-hoon Choi ◽  
Albert Groenwold ◽  
Gilho Yoon
Keyword(s):  
Structural Optimization ◽  
Optimization Algorithm ◽  
Sequential Approximate Optimization ◽  
Quadratic Approximation ◽  
Approximate Optimization ◽  
Diagonal Quadratic Approximation

scholarly journals Lower bounds on circuit depth of the quantum approximate optimization algorithm

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Rebekah Herrman ◽  
James Ostrowski ◽  
Travis S. Humble ◽  
George Siopsis
Keyword(s):  
Lower Bounds ◽  
Optimization Algorithm ◽  
Approximate Optimization ◽  
Circuit Depth

Peloton Dynamics Optimization: Algorithm for Discrete Structural Optimization

2021 ◽  
Vol 147 (10) ◽  
pp. 04021164
Author(s):  
Gérard Jacques Poitras ◽  
Gabriel Cormier ◽  
Armel Stanislas Nabolle
Keyword(s):  
Structural Optimization ◽  
Optimization Algorithm

scholarly journals Modeling and mitigation of cross-talk effects in readout noise with applications to the Quantum Approximate Optimization Algorithm

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 464
Author(s):  
Filip B. Maciejewski ◽  
Flavio Baccari ◽  
Zoltán Zimborás ◽  
Michał Oszmaniec
Keyword(s):  
Cross Talk ◽  
Optimization Algorithm ◽  
Probability Distributions ◽  
System Size ◽  
Measurement Noise ◽  
Noise Model ◽  
Noise Mitigation ◽  
Energy Estimation ◽  
Approximate Optimization ◽  
Readout Noise

Measurement noise is one of the main sources of errors in currently available quantum devices based on superconducting qubits. At the same time, the complexity of its characterization and mitigation often exhibits exponential scaling with the system size. In this work, we introduce a correlated measurement noise model that can be efficiently described and characterized, and which admits effective noise-mitigation on the level of marginal probability distributions. Noise mitigation can be performed up to some error for which we derive upper bounds. Characterization of the model is done efficiently using Diagonal Detector Overlapping Tomography – a generalization of the recently introduced Quantum Overlapping Tomography to the problem of reconstruction of readout noise with restricted locality. The procedure allows to characterize k-local measurement cross-talk on N-qubit device using O(k2klog(N)) circuits containing random combinations of X and identity gates. We perform experiments on 15 (23) qubits using IBM's (Rigetti's) devices to test both the noise model and the error-mitigation scheme, and obtain an average reduction of errors by a factor >22 (>5.5) compared to no mitigation. Interestingly, we find that correlations in the measurement noise do not correspond to the physical layout of the device. Furthermore, we study numerically the effects of readout noise on the performance of the Quantum Approximate Optimization Algorithm (QAOA). We observe in simulations that for numerous objective Hamiltonians, including random MAX-2-SAT instances and the Sherrington-Kirkpatrick model, the noise-mitigation improves the quality of the optimization. Finally, we provide arguments why in the course of QAOA optimization the estimates of the local energy (or cost) terms often behave like uncorrelated variables, which greatly reduces sampling complexity of the energy estimation compared to the pessimistic error analysis. We also show that similar effects are expected for Haar-random quantum states and states generated by shallow-depth random circuits.

scholarly journals Classical symmetries and the Quantum Approximate Optimization Algorithm

2021 ◽  
Vol 20 (11) ◽  
Author(s):  
Ruslan Shaydulin ◽  
Stuart Hadfield ◽  
Tad Hogg ◽  
Ilya Safro
Keyword(s):  
Optimization Algorithm ◽  
Approximate Optimization ◽  
Classical Symmetries
Sign in / Sign up

Export Citation Format

Share Document