Electronic Structure Theory with Molecular Point Group Symmetries on Quantum Annealers
- URL: http://arxiv.org/abs/2502.00235v1
- Date: Sat, 01 Feb 2025 00:30:57 GMT
- Title: Electronic Structure Theory with Molecular Point Group Symmetries on Quantum Annealers
- Authors: Joseph Desroches, Sijia S. Dong,
- Abstract summary: We implement the Xia-Bian-Kais (XBK) method for improving the efficiency of electronic structure theory calculations on quantum annealers.<n>By providing a more extensive symmetry-adapted encoding (SAE) than previous work, we are able to simulate molecules larger than those previously reported.<n>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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum computation has the potential to revolutionize quantum chemistry through major speedups to computation times and exponential reduction of computational resources. Here, we combine the symmetry-adapted Jordan-Wigner encoding based on the full Boolean symmetry group $\mathbb{Z}_2^k$ with our new implementation of the Xia-Bian-Kais (XBK) method for improving the efficiency of electronic structure theory calculations on quantum annealers, particularly by reducing the number of qubits needed to achieve the same accuracy. By providing a more extensive symmetry-adapted encoding (SAE) than previous work, we are able to simulate molecules larger than those previously reported that have been studied using methods developed for quantum annealers and without using an active space. We calculated the potential energy surfaces of H$_2$, LiH, He$_2$, H$_2$O, O$_2$, N$_2$, Li$_2$, F$_2$, CO, BH$_3$, NH$_3$, and CH$_4$, with the largest molecule in the STO-6G basis set requiring 16 qubits with our SAE, and compared them with full configuration interaction results. The application of SAE to the XBK method provides an exponential reduction of the size of the Hilbert space and scales well with the size of the problem. It does not introduce significant additional errors for even or large values of a key variational parameter that determines the number of ancilla qubits used in the XBK method's Hamiltonian embedding, or for certain molecules such as He$_2$ and H$_2$O. We provide an explanation for this behavior and a recommendation on the usage of our method.
Related papers
- Simulating methylamine using symmetry adapted qubit-excitation-based variational quantum eigensolver [0.39462888523270856]
We analyze the resources that are needed to simulate certain molecules on a medium-scale quantum computer.<n>We propose and analyze optimization techniques based on molecular point group symmetries and compact excitation circuits.
arXiv Detail & Related papers (2025-01-28T15:55:32Z) - Towards determining the (2+1)-dimensional Quantum Electrodynamics running coupling with Monte Carlo and quantum computing methods [0.0]
We present a strategy for studying the running coupling and extracting the non-perturbative $Lambda$- parameter.
We use Monte Carlo simulations and quantum computing to bridge results from small lattice spacings to large-scale lattice calculations.
The procedure outlined in this work can be extended to Abelian and non-Abelian lattice gauge theories with matter fields.
arXiv Detail & Related papers (2024-04-26T17:17:20Z) - Reducing the runtime of fault-tolerant quantum simulations in chemistry
through symmetry-compressed double factorization [0.0]
We introduce the symmetry-compressed double factorization (SCDF) approach, which combines a compressed double factorization of the Hamiltonian with the symmetry shift technique, significantly reducing the 1-norm value.
For the systems considered here, SCDF leads to a sizeable reduction of the Toffoli gate count in comparison to other variants of double factorization or even tensor hypercontraction.
arXiv Detail & Related papers (2024-03-06T07:11:02Z) - Neutron-nucleus dynamics simulations for quantum computers [49.369935809497214]
We develop a novel quantum algorithm for neutron-nucleus simulations with general potentials.
It provides acceptable bound-state energies even in the presence of noise, through the noise-resilient training method.
We introduce a new commutativity scheme called distance-grouped commutativity (DGC) and compare its performance with the well-known qubit-commutativity scheme.
arXiv Detail & Related papers (2024-02-22T16:33:48Z) - Quantum Algorithms for Simulating Nuclear Effective Field Theories [40.83664249192338]
We use state-of-the-art Hamiltonian-simulation methods to estimate the qubit and gate costs to simulate low-energy effective field theories (EFTs) of nuclear physics.
We demonstrate how symmetries of the low-energy nuclear Hamiltonians can be utilized to obtain tighter error bounds on the simulation algorithm.
arXiv Detail & Related papers (2023-12-08T20:09:28Z) - Accelerating Quantum Optimal Control of Multi-Qubit Systems with
Symmetry-Based Hamiltonian Transformations [3.0126004742841253]
We present a novel, computationally efficient approach to accelerate quantum optimal control calculations of large multi-qubit systems.
Our approach reduces the Hamiltonian size of an $n$-qubit system from 2n by 2n to O(n by n) or O((2n / n) by (2n / n) under Sn or Dn symmetry.
arXiv Detail & Related papers (2023-09-12T00:08:17Z) - General quantum algorithms for Hamiltonian simulation with applications
to a non-Abelian lattice gauge theory [44.99833362998488]
We introduce quantum algorithms that can efficiently simulate certain classes of interactions consisting of correlated changes in multiple quantum numbers.
The lattice gauge theory studied is the SU(2) gauge theory in 1+1 dimensions coupled to one flavor of staggered fermions.
The algorithms are shown to be applicable to higher-dimensional theories as well as to other Abelian and non-Abelian gauge theories.
arXiv Detail & Related papers (2022-12-28T18:56:25Z) - 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) - 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) - Adaptive Basis Sets for Practical Quantum Computing [0.0]
We develop small basis sets better suited for quantum computing.
We show that the use of adaptive basis sets, in which exponents and coefficients depend on molecular structure, provide an easy way to dramatically improve the accuracy of quantum chemical calculations.
This approach can be extended to other molecular systems and larger basis sets in a straightforward manner.
arXiv Detail & Related papers (2022-11-11T20:17:05Z) - Even more efficient quantum computations of chemistry through tensor
hypercontraction [0.6234350105794442]
We describe quantum circuits with only $widetildecal O(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary orbitals.
This is the lowest complexity that has been shown for quantum computations of chemistry within an arbitrary basis.
arXiv Detail & Related papers (2020-11-06T18:03:29Z) - 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) - OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted
Atomic-Orbital Features [42.96944345045462]
textscOrbNet is shown to outperform existing methods in terms of learning efficiency and transferability.
For applications to datasets of drug-like molecules, textscOrbNet predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.
arXiv Detail & Related papers (2020-07-15T22:38:41Z) - Quantum Simulation of 2D Quantum Chemistry in Optical Lattices [59.89454513692418]
We propose an analog simulator for discrete 2D quantum chemistry models based on cold atoms in optical lattices.
We first analyze how to simulate simple models, like the discrete versions of H and H$+$, using a single fermionic atom.
We then show that a single bosonic atom can mediate an effective Coulomb repulsion between two fermions, leading to the analog of molecular Hydrogen in two dimensions.
arXiv Detail & Related papers (2020-02-21T16:00:36Z)
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.