Towards Efficient Quantum Computing for Quantum Chemistry: Reducing Circuit Complexity with Transcorrelated and Adaptive Ansatz Techniques
- URL: http://arxiv.org/abs/2402.16659v2
- Date: Wed, 17 Apr 2024 12:23:47 GMT
- Title: Towards Efficient Quantum Computing for Quantum Chemistry: Reducing Circuit Complexity with Transcorrelated and Adaptive Ansatz Techniques
- Authors: Erika Magnusson, Aaron Fitzpatrick, Stefan Knecht, Martin Rahm, Werner Dobrautz,
- Abstract summary: This work demonstrates how to reduce circuit depth by combining the transcorrelated (TC) approach with adaptive quantum ans"atze.
Our study demonstrates that combining the TC method with adaptive ans"atze yields compact, noise-resilient, and easy-to-optimize quantum circuits.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The near-term utility of quantum computers is hindered by hardware constraints in the form of noise. One path to achieving noise resilience in hybrid quantum algorithms is to decrease the required circuit depth -- the number of applied gates -- to solve a given problem. This work demonstrates how to reduce circuit depth by combining the transcorrelated (TC) approach with adaptive quantum ans\"atze and their implementations in the context of variational quantum imaginary time evolution (AVQITE). The combined TC-AVQITE method is used to calculate ground state energies across the potential energy surfaces of H$_4$, LiH, and H$_2$O. In particular, H$_4$ is a notoriously difficult case where unitary coupled cluster theory, including singles and doubles excitations, fails to provide accurate results. Adding TC yields energies close to the complete basis set (CBS) limit while reducing the number of necessary operators -- and thus circuit depth -- in the adaptive ans\"atze. The reduced circuit depth furthermore makes our algorithm more noise-resilient and accelerates convergence. Our study demonstrates that combining the TC method with adaptive ans\"atze yields compact, noise-resilient, and easy-to-optimize quantum circuits that yield accurate quantum chemistry results close to the CBS limit.
Related papers
- Efficient Quantum Circuit Compilation for Near-Term Quantum Advantage [17.38734393793605]
We propose an approximate method for compiling target quantum circuits into brick-wall layouts.
This new circuit design consists of two-qubit CNOT gates that can be directly implemented on real quantum computers.
arXiv Detail & Related papers (2025-01-13T15:04:39Z) - Symmetry-preserved cost functions for variational quantum eigensolver [0.0]
Hybrid quantum-classical variational algorithms are considered ideal for noisy quantum computers.
We propose encoding symmetry preservation directly into the cost function, enabling more efficient use of Hardware-Efficient Ans"atze.
arXiv Detail & Related papers (2024-11-25T20:33:47Z) - A Fast and Adaptable Algorithm for Optimal Multi-Qubit Pathfinding in Quantum Circuit Compilation [0.0]
This work focuses on multi-qubit pathfinding as a critical subroutine within the quantum circuit compilation mapping problem.
We introduce an algorithm, modelled using binary integer linear programming, that navigates qubits on quantum hardware optimally with respect to circuit SWAP-gate depth.
We have benchmarked the algorithm across a variety of quantum hardware layouts, assessing properties such as computational runtimes, solution SWAP depths, and accumulated SWAP-gate error rates.
arXiv Detail & Related papers (2024-05-29T05:59:15Z) - A multiple-circuit approach to quantum resource reduction with application to the quantum lattice Boltzmann method [39.671915199737846]
We introduce a multiple-circuit algorithm for a quantum lattice Boltzmann method (QLBM) solve of the incompressible Navier--Stokes equations.
The presented method is validated and demonstrated for 2D lid-driven cavity flow.
arXiv Detail & Related papers (2024-01-20T15:32:01Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Near-Term Distributed Quantum Computation using Mean-Field Corrections
and Auxiliary Qubits [77.04894470683776]
We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production.
We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
arXiv Detail & Related papers (2023-09-11T18:00:00Z) - Ab Initio Transcorrelated Method enabling accurate Quantum Chemistry on near-term Quantum Hardware [0.0]
Current hardware limitations hamper the straightforward implementation of most quantum algorithms.
In quantum chemistry, the limited number of available qubits and gate operations is particularly restrictive.
We show that the exact transcorrelated approach not only allows for more shallow circuits but also improves the convergence towards the so-called basis set limit.
arXiv Detail & Related papers (2023-03-03T15:24:22Z) - Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem.
The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment.
We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
arXiv Detail & Related papers (2022-04-18T21:45:13Z) - Simulating the Mott transition on a noisy digital quantum computer via
Cartan-based fast-forwarding circuits [62.73367618671969]
Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model.
Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models.
This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
arXiv Detail & Related papers (2021-12-10T17:32:15Z) - Space-efficient binary optimization for variational computing [68.8204255655161]
We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem.
We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
arXiv Detail & Related papers (2020-09-15T18:17:27Z) - Improving the Performance of Deep Quantum Optimization Algorithms with
Continuous Gate Sets [47.00474212574662]
Variational quantum algorithms are believed to be promising for solving computationally hard problems.
In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances.
Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
arXiv Detail & Related papers (2020-05-11T17:20:51Z)
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.