Software tools for quantum control: Improving quantum computer
performance through noise and error suppression
- URL: http://arxiv.org/abs/2001.04060v2
- Date: Thu, 9 Jul 2020 07:15:40 GMT
- Title: Software tools for quantum control: Improving quantum computer
performance through noise and error suppression
- Authors: Harrison Ball, Michael J. Biercuk, Andre Carvalho, Jiayin Chen,
Michael Hush, Leonardo A. De Castro, Li Li, Per J. Liebermann, and Harry J.
Slatyer, Claire Edmunds, Virginia Frey, Cornelius Hempel and Alistair Milne
- Abstract summary: We introduce software tools for the application and integration of quantum control in quantum computing research.
We provide an overview of a set of python-based classical software tools for creating and deploying optimized quantum control solutions.
We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems.
- Score: 3.6508609114589317
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Manipulating quantum computing hardware in the presence of imperfect devices
and control systems is a central challenge in realizing useful quantum
computers. Susceptibility to noise limits the performance and capabilities of
noisy intermediate-scale quantum (NISQ) devices, as well as any future quantum
computing technologies. Fortunately quantum control enables efficient execution
of quantum logic operations and algorithms with built-in robustness to errors,
without the need for complex logical encoding. In this manuscript we introduce
software tools for the application and integration of quantum control in
quantum computing research, serving the needs of hardware R&D teams, algorithm
developers, and end users. We provide an overview of a set of python-based
classical software tools for creating and deploying optimized quantum control
solutions at various layers of the quantum computing software stack. We
describe a software architecture leveraging both high-performance distributed
cloud computation and local custom integration into hardware systems, and
explain how key functionality is integrable with other software packages and
quantum programming languages. Our presentation includes a detailed
mathematical overview of central product features including a flexible
optimization toolkit, filter functions for analyzing noise susceptibility in
high-dimensional Hilbert spaces, and new approaches to noise and hardware
characterization. Pseudocode is presented in order to elucidate common
programming workflows for these tasks, and performance benchmarking is reported
for numerically intensive tasks, highlighting the benefits of the selected
cloud-compute architecture. Finally, we present a series of case studies
demonstrating the application of quantum control solutions using these tools in
real experimental settings for both trapped-ion and superconducting quantum
computer hardware.
Related papers
- The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - An open-source framework for quantum hardware control [31.874825130479174]
The development of quantum computers needs reliable quantum hardware and tailored software for controlling electronics specific to various quantum platforms.
This paper presents updates to Qibolab, a software library that leverages Qibo capabilities to execute quantum algorithms on self hosted quantum hardware platforms.
arXiv Detail & Related papers (2024-07-31T16:44:31Z) - Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
We develop a reinforcement learning-based quantum compiler for a superconducting processor.
We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths.
Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
arXiv Detail & Related papers (2024-06-18T01:49:48Z) - Symbolic quantum programming for supporting applications of quantum
computing technologies [0.0]
The main focus of this paper is on quantum computing technologies, as they can in the most direct way benefit from developing tools.
We deliver a short survey of the most popular approaches in the field of quantum software development and we aim at pointing their strengths and weaknesses.
Next, we describe a software architecture and its preliminary implementation supporting the development of quantum programs using symbolic approach.
arXiv Detail & Related papers (2023-02-18T18:30:00Z) - The Basis of Design Tools for Quantum Computing: Arrays, Decision
Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
Quantum computers promise to efficiently solve important problems classical computers never will.
A fully automated quantum software stack needs to be developed.
This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
arXiv Detail & Related papers (2023-01-10T19:00:00Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - 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) - QFaaS: A Serverless Function-as-a-Service Framework for Quantum
Computing [22.068803245816266]
We propose a Quantum Function-as-a-Service framework to advance quantum computing.
Our framework provides essential components of a quantum serverless platform to simplify the software development and adapt to the quantum cloud computing paradigm.
This paper proposes architectural design, principal components, the life cycle of hybrid quantum-classical function, operation workflow, and implementation of QF.
arXiv Detail & Related papers (2022-05-30T04:18:53Z) - Full-stack quantum computing systems in the NISQ era: algorithm-driven
and hardware-aware compilation techniques [1.3496450124792878]
We will provide an overview on current full-stack quantum computing systems.
We will emphasize the need for tight co-design among adjacent layers as well as vertical cross-layer design.
arXiv Detail & Related papers (2022-04-13T13:26:56Z) - Noisy intermediate-scale quantum (NISQ) algorithms [0.5325753548715747]
A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times.
While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist.
These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time.
arXiv Detail & Related papers (2021-01-21T05:27:34Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z)
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.