Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion
- URL: http://arxiv.org/abs/2602.04253v1
- Date: Wed, 04 Feb 2026 06:26:58 GMT
- Title: Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion
- Authors: Runhong He, Xin Hong, Qiaozhen Chai, Ji Guan, Junyuan Zhou, Arapat Ablimit, Guolong Cui, Shenggang Ying,
- Abstract summary: Param-ADAPT-VQE is a novel improved algorithm that selects excitation operators based on a parameter-based criterion instead of the traditional gradient-based metric.<n>We show that Param-ADAPT-VQE outperforms the original ADAPT-VQE in computational accuracy, ansatz size, and measurement costs.
- Score: 8.808367903406316
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The adaptive derivative-assembled pseudo-trotter variational quantum eigensolver (ADAPT-VQE) is a promising hybrid quantum-classical algorithm for molecular ground state energy calculation, yet its practical scalability is hampered by redundant excitation operators and excessive measurement costs. To address these challenges, we propose Param-ADAPT-VQE, a novel improved algorithm that selects excitation operators based on a parameter-based criterion instead of the traditional gradient-based metric. This strategy effectively eludes redundant operators. We further develop a sub-Hamiltonian technique and integrate a hot-start VQE optimization strategy, achieving a significant reduction in measurement costs. Numerical experiments on typical molecular systems demonstrate that Param-ADAPT-VQE outperforms the original ADAPT-VQE in computational accuracy, ansatz size, and measurement costs. Furthermore, our scheme retains the fundamental framework of ADAPT-VQE and is thus fully compatible with its various modified versions, enabling further performance improvements in specific aspects. This work presents an efficient and scalable enhancement to ADAPT-VQE, mitigating the core obstacles that impede its practical implementation in the field of molecular quantum chemistry.
Related papers
- Benchmarking VQE Configurations: Architectures, Initializations, and Optimizers for Silicon Ground State Energy [0.0]
This work investigates the performance of the Variational Quantumsolver (VQE) in estimating the ground-state energy of the silicon atom.<n>Within a hybrid quantum-classical optimization framework, we implement VQE using a range of ansatz, including Double Excitation Gates, ParticleConservingU2, UCCSD, and k-UpCCGSD.<n>The main contribution of this work lies in a systematic exploration of how these configuration choices interact to influence VQE performance.
arXiv Detail & Related papers (2025-10-27T09:57:26Z) - End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost [53.25965863436039]
Quantization-aware training (QAT) provides a more principled solution, but its reliance on backpropagation incurs prohibitive memory costs.<n>We propose ZeroQAT, a zeroth-order optimization-based QAT framework that supports both weight and activation quantization.<n>Experiments show that ZeroQAT consistently outperforms representative PTQ and QAT baselines while requiring significantly less memory.
arXiv Detail & Related papers (2025-08-21T01:18:27Z) - TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
arXiv Detail & Related papers (2025-08-01T23:37:55Z) - Shot-Efficient ADAPT-VQE via Reused Pauli Measurements and Variance-Based Shot Allocation [0.0]
We propose two integrated strategies to reduce the shot requirements in ADAPT-VQE.<n>First, we reuse Pauli measurement outcomes obtained during VQE parameter optimization in the subsequent operator selection step of the next ADAPT-VQE iteration.<n>Second, we apply variance-based shot allocation to both Hamiltonian and operator gradient measurements.
arXiv Detail & Related papers (2025-07-22T12:34:49Z) - Non-Variational ADAPT algorithm for quantum simulations [0.0]
We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy.
In this algorithm, energy gradient measurements determine both the operators and the gate parameters in the quantum circuit construction.
We compare this non-variational algorithm with ADAPT-VQE and with feedback-based quantum algorithms in terms of the rate of energy reduction, the circuit depth, and the measurement cost in molecular simulation.
arXiv Detail & Related papers (2024-11-14T19:00:01Z) - Classical Pre-optimization Approach for ADAPT-VQE: Maximizing the Potential of High-Performance Computing Resources to Improve Quantum Simulation of Chemical Applications [0.6361348748202732]
We report the implementation and performance of ADAPT-VQE with our sparse wavefunction circuit solver (SWCS)
The SWCS can be tuned to balance computational cost and accuracy, which extends the application of ADAPT-VQE for molecular electronic structure calculations.
By pre-optimizing a quantum simulation with a parameterized ansatz generated with ADAPT-VQE/SWCS, we aim to utilize the power of classical high-performance computing.
arXiv Detail & Related papers (2024-11-12T16:52:31Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Pointer Networks with Q-Learning for Combinatorial Optimization [55.2480439325792]
We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets)
Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
arXiv Detail & Related papers (2023-11-05T12:03:58Z) - A self-consistent field approach for the variational quantum
eigensolver: orbital optimization goes adaptive [52.77024349608834]
We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE)
This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
arXiv Detail & Related papers (2022-12-21T23:15:17Z) - Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the
Race to Practical Quantum Advantage [43.3054117987806]
We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits.
We show this method significantly improves the trainability and performance of PQCs on a variety of problems.
By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
arXiv Detail & Related papers (2022-08-29T15:24:03Z) - Improved accuracy on noisy devices by non-unitary Variational Quantum
Eigensolver for chemistry applications [0.0]
We propose a modification of the Variational Quantum Eigensolver algorithm for electronic structure optimization using quantum computers.
A non-unitary operator is combined with the original system Hamiltonian leading to a new variational problem with a simplified wavefunction Ansatz.
arXiv Detail & Related papers (2021-01-22T20:17:37Z) - Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves.
We find both methods provide good estimates of the energy and ground state.
gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
arXiv Detail & Related papers (2020-11-02T19:52:04Z) - 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)
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.