Simulating methylamine using symmetry adapted qubit-excitation-based variational quantum eigensolver

• URL: http://arxiv.org/abs/2501.17035v3
• Date: Mon, 17 Mar 2025 13:33:27 GMT
• Title: Simulating methylamine using symmetry adapted qubit-excitation-based variational quantum eigensolver
• Abstract summary: We propose and analyze optimization strategies for the VQE algorithm that combine various methods.<n>Best-performing optimization strategy reduces the number of two-qubit operations for simulating the methylamine molecule from 600,000 to approximately 12,000, using 26 qubits.
• Score: 0.39462888523270856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose and analyze optimization strategies for the VQE algorithm that combine various methods, including molecular point group symmetries (symmetry adaptation), compact excitation circuits (qubit-excitation-based), different types of excitation sets, and qubit tapering. These strategies allow for a significant reduction in computational requirements while ensuring convergence to the correct energies. First, we apply these combinations to small molecules, such as LiH and BeH2, to evaluate their compatibility, accuracy, and potential applicability to larger problems. We then simulate the methylamine molecule within its restricted active space using the best-performing optimization strategies. Finally, we complete our analysis by estimating the resources required for full active-space simulations of the methylamine and formic acid molecules. Our best-performing optimization strategy reduces the number of two-qubit operations for simulating the methylamine molecule from 600,000 (in the STO-3G basis with a naive Unitary Coupled Cluster ansatz) to approximately 12,000, using 26 qubits. Thus, the proposed combination of optimization methods can reduce the number of two-qubit operations by nearly two orders of magnitude. Although, we present alternative approaches that are of interest in the context of the further optimization in the number of two-qubit operation, we note that these approaches do not perform well enough in terms of the convergence to required energies. While these challenges persist, our resource analysis represents a valuable step towards the practical use of quantum computers and the development of better methods for optimizing computing resources.

Related papers

• Tensor network surrogate models for variational quantum computation [0.20391237204597365]
  We adopt a two-dimensional tensor-network (TN) ansatz to simulate variational quantum algorithms on two-dimensional qubit architectures.<n>For heavy-hexagonal problems with up to three-body interactions, parameters trained on small instances and transferred to systems an order of magnitude larger improve the sampled energy distribution.<n>By extending the training itself with TN simulations on larger system sizes, we avoid local minima and obtain lower-energy samples.
  arXiv  Detail & Related papers  (2026-04-22T04:53:39Z)
• Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing [1.1463843149836523]
  Accurately and efficiently predicting the equilibrium geometries of large molecules remains a central challenge in quantum computational chemistry.<n>Two major obstacles hinder progress: the large number of qubits required and the prohibitive cost of conventional nested optimization.<n>We introduce a co-optimization framework that combines Density Matrix Embedding Theory (DMET) with Variational Quantum Eigensolver (VQE) to address these limitations.
  arXiv  Detail & Related papers  (2025-09-09T07:31:29Z)
• Electronic Structure Theory with Molecular Point Group Symmetries on Quantum Annealers [0.0]
  We implement the Xia-Bian-Kais (XBK) method for improving the efficiency of electronic structure theory calculations on quantum annealers. By providing a more extensive symmetry-adapted encoding (SAE) than previous work, we are able to simulate molecules larger than those previously reported. The application of SAE to the XBK method provides an exponential reduction of the size of the space and scales well with the size of the problem.
  arXiv  Detail & Related papers  (2025-02-01T00:30:57Z)
• Optimizing a parameterized controlled gate with Free Quaternion Selection [0.4353365283165517]
  In this study, we propose an algorithm to estimate the optimal parameters for locally minimizing the cost value of a single-qubit gate. To benchmark the performance, we apply the proposed method to various optimization problems, including the Variational Eigensolver (VQE) for Ising and molecular Hamiltonians.
  arXiv  Detail & Related papers  (2024-09-20T14:46:00Z)
• Quantum algorithms for the variational optimization of correlated electronic states with stochastic reconfiguration and the linear method [0.0]
  We present quantum algorithms for the variational optimization of wavefunctions correlated by products of unitary operators. While an implementation on classical computing hardware would require exponentially growing compute cost, the cost (number of circuits and shots) of our quantum algorithms is in system size.
  arXiv  Detail & Related papers  (2024-08-03T17:53:35Z)
• Variational Quantum Imaginary Time Evolution for Matrix Product State Ansatz with Tests on Transcorrelated Hamiltonians [11.985673663540688]
  The matrix product state (MPS) ansatz offers a promising approach for finding the ground state of molecular Hamiltonians. We enhance the optimization performance of the QCMPS ansatz by employing the variational quantum imaginary time evolution (VarQITE) approach.
  arXiv  Detail & Related papers  (2024-07-15T08:28:52Z)
• Surrogate optimization of variational quantum circuits [1.0546736060336612]
  Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE.
  arXiv  Detail & Related papers  (2024-04-03T18:00:00Z)
• Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization [41.94295877935867]
  We propose a faster digital quantum algorithm for portfolio optimization using the digitized-counterdiabatic quantum optimization (DCQO) paradigm. Our approach notably reduces the circuit depth requirement of the algorithm and enhances the solution accuracy, making it suitable for current quantum processors. We experimentally demonstrate the advantages of our protocol using up to 20 qubits on an IonQ trapped-ion quantum computer.
  arXiv  Detail & Related papers  (2023-08-29T17:53:08Z)
• Molecular Symmetry in VQE: A Dual Approach for Trapped-Ion Simulations of Benzene [0.2624902795082451]
  Near-term strategies hinge on the use of variational quantum eigensolver (VQE) algorithms combined with a suitable ansatz. We employ several circuit optimization methods tailored for trapped-ion quantum devices to enhance the feasibility of intricate chemical simulations. These methods, when applied to a benzene molecule simulation, enabled the construction of an 8-qubit circuit with 69 two-qubit entangling operations.
  arXiv  Detail & Related papers  (2023-08-01T17:03:10Z)
• 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)
• Potential energy surfaces inference of both ground and excited state using hybrid quantum-classical neural network [0.0]
  A hybrid quantum-classical neural network has been proposed for surrogate modeling of the variational quantum eigensolver. We extend the model by using the subspace-search variational quantum eigensolver procedure so that the PESs of the both ground and excited state can be inferred with chemical accuracy.
  arXiv  Detail & Related papers  (2022-12-06T14:28:44Z)
• End-to-end resource analysis for quantum interior point methods and portfolio optimization [63.4863637315163]
  We provide a complete quantum circuit-level description of the algorithm from problem input to problem output. We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm.
  arXiv  Detail & Related papers  (2022-11-22T18:54:48Z)
• 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)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• Hybridized Methods for Quantum Simulation in the Interaction Picture [69.02115180674885]
  We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations. Physical applications of these hybridized methods yield a gate complexity scaling as $log2 Lambda$ in the electric cutoff. For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter $lambda$ used to impose an energy cost.
  arXiv  Detail & Related papers  (2021-09-07T20:01:22Z)
• Lyapunov control-inspired strategies for quantum combinatorial optimization [0.0]
  We provide an expanded description of Lyapunov control-inspired strategies for quantum optimization. Instead, these strategies utilize feedback from qubit measurements to assign values to the quantum circuit parameters in a deterministic manner.
  arXiv  Detail & Related papers  (2021-08-12T19:47:59Z)
• 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. 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)
• 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)
• Using models to improve optimizers for variational quantum algorithms [1.7475326826331605]
  Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. We introduce two optimization methods and numerically compare their performance with common methods in use today.
  arXiv  Detail & Related papers  (2020-05-22T05:23:23Z)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)

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.