Clifford Accelerated Adaptive QAOA

• URL: http://arxiv.org/abs/2508.16443v1
• Date: Fri, 22 Aug 2025 14:58:33 GMT
• Title: Clifford Accelerated Adaptive QAOA
• Authors: Théo Lisart-Liebermann, Arcesio Castañeda Medina,
• Abstract summary: We show that Clifford Point approximations at multiple levels of ADAPT allow for multiple improvements while increasing quantum-classical integration opportunities.<n>Applying 10 to 30% error approximation on T-gates using low-rank stabilizer decomposition can provide significative improvements in convergence quality for the MaxCut and TFIM problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clifford Circuit Initializaton improves on initial guess of parameters on Parametric Quantum Circuits (PQCs) by leveraging efficient simulation of circuits made out of gates from the Clifford Group. The parameter space is pre-optimized by exploring the Hilbert space in a reduced ensemble of Clifford-expressible points (Clifford Points), providing better initialization. Simultaneously, dynamical circuit reconfiguration algorithms, such as ADAPT-QAOA, improve on QAOA performances by providing a gate re-configuration routine while the optimization is being executed. In this article, we show that Clifford Point approximations at multiple levels of ADAPT allow for multiple improvements while increasing quantum-classical integration opportunities. First we show numerically that Clifford Point preoptimization offers non-trivial gate-selection behavior in ADAPT with some possible convergence improvement. Second, that Clifford Point approximations allows for more suited, fully parallel and fully classical ADAPT operator selection for MaxCut and the TFIM problem. Finally, we show that applying 10 to 30\% error approximation on T-gates using low-rank stabilizer decomposition can provide significative improvements in convergence quality for the MaxCut and TFIM problem. The latter hints at significant T-gate over-representation in antsatz design, opening opportunities for aggressive compilation optimizations.

Related papers

• Extending QAOA-GPT to Higher-Order Quantum Optimization Problems [0.0]
  We extend QAOA-GPT to Higher-Order Unconstrained Binary Optimization problems.<n>We train the model on graph-circuit pairs generated via ADAPT-QAOA and evaluate its performance on 8- and 16-qubit instances embedded on heavy-hex lattices.<n>Results demonstrate that QAOA-GPT generalizes effectively to higher-order cost Hamiltonians and complex energy landscapes.
  arXiv  Detail & Related papers  (2025-11-10T18:46:38Z)
• Symmetry-accelerated classical simulation of Clifford-dominated circuits [0.0]
  We exploit symmetries in the computation of the stabilizer extent, proving that for real, diagonal, and real-diagonal unitaries, the optimization can be restricted to the corresponding subgroups of the Clifford group without loss of optimality.<n>This strong symmetry reduction'' drastically reduces computational cost, enabling optimal decompositions of unitaries on up to seven qubits using a standard laptop.
  arXiv  Detail & Related papers  (2025-10-21T18:01:14Z)
• QuCLEAR: Clifford Extraction and Absorption for Quantum Circuit Optimization [8.043057448895343]
  Currently available quantum devices suffer from noisy quantum gates, which degrade the fidelity of executed quantum circuits.<n>We present QuCLEAR, a compilation framework designed to optimize quantum circuits.
  arXiv  Detail & Related papers  (2024-08-23T18:03:57Z)
• Optimization at the Interface of Unitary and Non-unitary Quantum Operations in PCOAST [0.3496513815948205]
  Pauli-based Circuit Optimization, Analysis and Synthesis Toolchain (PCOAST) introduced as framework for optimizing quantum circuits. In this paper, we focus on the set of subroutines which look to optimize the PCOAST graph in cases involving unitary and non-unitary operations. We evaluate the PCOAST optimization subroutines using the Intel Quantum SDK on examples of the Variational Quantum Eigensolver (VQE) algorithm.
  arXiv  Detail & Related papers  (2023-05-16T22:58:14Z)
• Iterative Qubit Coupled Cluster using only Clifford circuits [36.136619420474766]
  An ideal state preparation protocol can be characterized by being easily generated classically. We propose a method that meets these requirements by introducing a variant of the iterative qubit coupled cluster (iQCC) We demonstrate the algorithm's correctness in ground-state simulations and extend our study to complex systems like the titanium-based compound Ti(C5H5)(CH3)3 with a (20, 20) active space.
  arXiv  Detail & Related papers  (2022-11-18T20:31:10Z)
• Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization [108.35402316802765]
  We propose a new first-order optimization algorithm -- AcceleratedGradient-OptimisticGradient (AG-OG) Ascent. We show that AG-OG achieves the optimal convergence rate (up to a constant) for a variety of settings. We extend our algorithm to extend the setting and achieve the optimal convergence rate in both bi-SC-SC and bi-C-SC settings.
  arXiv  Detail & Related papers  (2022-10-31T17:59:29Z)
• Optimizing Counterdiabaticity by Variational Quantum Circuits [3.4092751295027997]
  We propose a technique of finding optimal coefficients of the CD terms using a variational quantum circuit. By classical optimizations routines, the parameters of this circuit are optimized to provide the coefficients corresponding to the CD terms. Their improved performance is exemplified in Greenberger-Horne-Zeilinger state preparation on nearest-neighbor Ising model.
  arXiv  Detail & Related papers  (2022-08-03T14:12:26Z)
• STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization [74.1615979057429]
  We investigate non-batch optimization problems where the objective is an expectation over smooth loss functions. Our work builds on the STORM algorithm, in conjunction with a novel approach to adaptively set the learning rate and momentum parameters.
  arXiv  Detail & Related papers  (2021-11-01T15:43:36Z)
• Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator [75.05106948314956]
  We show that an increasing large momentum parameter for the first-order moment is sufficient for adaptive scaling.<n>We also give insights for increasing the momentum in a stagewise manner in accordance with stagewise decreasing step size.
  arXiv  Detail & Related papers  (2021-04-30T08:50:24Z)
• FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits [105.54048699217668]
  We propose a FLexible Initializer for arbitrarily-sized Parametrized quantum circuits. FLIP can be applied to any family of PQCs, and instead of relying on a generic set of initial parameters, it is tailored to learn the structure of successful parameters. We illustrate the advantage of using FLIP in three scenarios: a family of problems with proven barren plateaus, PQC training to solve max-cut problem instances, and PQC training for finding the ground state energies of 1D Fermi-Hubbard models.
  arXiv  Detail & Related papers  (2021-03-15T17:38:33Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Convergence of adaptive algorithms for weakly convex constrained optimization [59.36386973876765]
  We prove the $mathcaltilde O(t-1/4)$ rate of convergence for the norm of the gradient of Moreau envelope. Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly smooth optimization domains.
  arXiv  Detail & Related papers  (2020-06-11T17:43:19Z)
• Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA [68.11912614360878]
  Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
  arXiv  Detail & Related papers  (2020-03-11T13:52:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.