Extending Quantum Computing through Subspace, Embedding and Classical Molecular Dynamics Techniques
- URL: http://arxiv.org/abs/2505.16796v1
- Date: Thu, 22 May 2025 15:37:00 GMT
- Title: Extending Quantum Computing through Subspace, Embedding and Classical Molecular Dynamics Techniques
- Authors: Thomas M. Bickley, Angus Mingare, Tim Weaving, Michael Williams de la Bastida, Shunzhou Wan, Martina Nibbi, Philipp Seitz, Alexis Ralli, Peter J. Love, Minh Chung, Mario Hernández Vera, Laura Schulz, Peter V. Coveney,
- Abstract summary: We review techniques that facilitate the study of chemical systems with quantum computers.<n>We present a proof-of-concept demonstration of quantum-selected configuration interaction deployed within a multiscale/multiphysics simulation workflow.
- Score: 0.05185707610786576
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The advent of hybrid computing platforms consisting of quantum processing units integrated with conventional high-performance computing brings new opportunities for algorithms design. By strategically offloading select portions of the workload to classical hardware where tractable, we may broaden the applicability of quantum computation in the near term. In this perspective, we review techniques that facilitate the study of subdomains of chemical systems with quantum computers and present a proof-of-concept demonstration of quantum-selected configuration interaction deployed within a multiscale/multiphysics simulation workflow leveraging classical molecular dynamics, projection-based embedding and qubit subspace tools. This allows the technology to be utilised for simulating systems of real scientific and industrial interest, which not only brings true quantum utility closer to realisation but is also relevant as we look forward to the fault-tolerant regime.
Related papers
- VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - Simulating quantum circuits with restricted quantum computers [0.0]
This thesis is dedicated to the simulation of nonlocal quantum computation using local quantum operations.<n>We characterize the optimal simulation overhead for a broad range of practically relevant nonlocal states and channels.<n>We also investigate the utility of classical communication between the local parties.
arXiv Detail & Related papers (2025-03-27T17:59:45Z) - Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC)
This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions.
Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
arXiv Detail & Related papers (2024-11-13T12:03:39Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Calculating the energy profile of an enzymatic reaction on a quantum computer [0.0]
Quantum computing provides a promising avenue toward enabling quantum chemistry calculations.<n>Recent research efforts are dedicated to developing and scaling algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices.
arXiv Detail & Related papers (2024-08-20T18:00:01Z) - Symmetry-invariant quantum machine learning force fields [0.0]
We design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries.
Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning.
arXiv Detail & Related papers (2023-11-19T16:15:53Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - A Herculean task: Classical simulation of quantum computers [4.12322586444862]
This work reviews the state-of-the-art numerical simulation methods that emulate quantum computer evolution under specific operations.
We focus on the mainstream state-vector and tensor-network paradigms while briefly mentioning alternative methods.
arXiv Detail & Related papers (2023-02-17T13:59:53Z) - 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) - QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum
Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-05-17T16:17:57Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z) - Composable Programming of Hybrid Workflows for Quantum Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-01-20T14:20:14Z) - Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations.
We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
arXiv Detail & Related papers (2020-12-08T01:10:30Z) - Digital quantum simulation of molecular dynamics and control [0.0]
We study how quantum computers could be employed to design optimally-shaped fields to control molecular systems.
We introduce a hybrid algorithm that utilizes a quantum computer for simulating the field-induced quantum dynamics of a molecular system in time.
Numerical illustrations are then presented that explicitly treat paradigmatic vibrational and rotational control problems.
arXiv Detail & Related papers (2020-02-28T00:45:56Z)
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.