A pragma based C++ framework for hybrid quantum/classical computation
- URL: http://arxiv.org/abs/2309.02605v3
- Date: Wed, 27 Mar 2024 19:11:21 GMT
- Title: A pragma based C++ framework for hybrid quantum/classical computation
- Authors: Arnaud Gazda, Oceane Koska,
- Abstract summary: This paper specifies the requirements of a hybrid quantum-classical framework compatible with HPC environments.
It introduces a novel hardware-agnostic framework called Q-Pragma.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum computers promise exponential speed ups over classical computers for various tasks. This emerging technology is expected to have its first huge impact in High Performance Computing (HPC), as it can solve problems beyond the reach of HPC. To that end, HPC will require quantum accelerators, which will enable applications to run on both classical and quantum devices, via hybrid quantum-classical nodes. Hybrid quantum-HPC applications should be scalable, executable on Quantum Error Corrected (QEC) devices, and could use quantum-classical primitives. However, the lack of scalability, poor performances, and inability to insert classical schemes within quantum applications has prevented current quantum frameworks from being adopted by the HPC community. This paper specifies the requirements of a hybrid quantum-classical framework compatible with HPC environments, and introduces a novel hardware-agnostic framework called Q-Pragma. This framework extends the classical programming language C++ heavily used in HPC via the addition of pragma directives to manage quantum computations.
Related papers
- Multi-GPU-Enabled Hybrid Quantum-Classical Workflow in Quantum-HPC Middleware: Applications in Quantum Simulations [1.9922905420195367]
This study introduces an innovative distribution-aware Quantum-Classical-Quantum architecture.
It integrates cutting-edge quantum software framework works with high-performance classical computing resources.
It addresses challenges in quantum simulation for materials and condensed matter physics.
arXiv Detail & Related papers (2024-03-09T07:38:45Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - A Hybrid Classical-Quantum HPC Workload [0.0]
A strategy for the orchestration of hybrid classical-quantum workloads on supercomputers featuring quantum devices is proposed.
An example application is investigated that offloads parts of computation to a quantum device.
The present test bed serves as a basis for more advanced hybrid workloads eventually involving a real quantum device.
arXiv Detail & Related papers (2023-12-08T09:54:51Z) - Integration of Quantum Accelerators with High Performance Computing -- A
Review of Quantum Programming Tools [0.8477185635891722]
This study aims to characterize existing quantum programming tools (QPTs) from an HPC perspective.
It investigates if existing QPTs have the potential to be efficiently integrated with classical computing models.
This work structures a set of criteria into an analysis blueprint that enables HPC scientists to assess whether a QPT is suitable for the quantum-accelerated classical application.
arXiv Detail & Related papers (2023-09-12T12:24:12Z) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
Quantum generative learning models (QGLMs) may surpass their classical counterparts.
We review the current progress of QGLMs from the perspective of machine learning.
We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
arXiv Detail & Related papers (2022-06-07T07:32:57Z) - Cutting Quantum Circuits to Run on Quantum and Classical Platforms [25.18520278107402]
CutQC is a scalable hybrid computing approach that distributes a large quantum circuit onto quantum (QPU) and classical platforms ( CPU or GPU) for co-processing.
It achieves much higher quantum circuit evaluation fidelity than the large NISQ devices achieve in real-system runs.
arXiv Detail & Related papers (2022-05-12T02:09:38Z) - 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) - CutQC: Using Small Quantum Computers for Large Quantum Circuit
Evaluations [18.78105450344374]
This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers.
CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices.
In real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers.
arXiv Detail & Related papers (2020-12-03T23:52:04Z) - Quantum Deformed Neural Networks [83.71196337378022]
We develop a new quantum neural network layer designed to run efficiently on a quantum computer.
It can be simulated on a classical computer when restricted in the way it entangles input states.
arXiv Detail & Related papers (2020-10-21T09:46:12Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z)
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.