Systematic Experiment Tracking in Quantum Software: A Case Study of Reservoir Computing with Error Mitigation
- URL: http://arxiv.org/abs/2607.24264v1
- Date: Mon, 27 Jul 2026 10:57:35 GMT
- Title: Systematic Experiment Tracking in Quantum Software: A Case Study of Reservoir Computing with Error Mitigation
- Abstract summary: We go into the details of experiment tracking in quantum software development.<n>We explain the basic concept of experiment tracking and detail how, in essence, quantum computing sets demands on tracking practices.<n>We present a detailed quantum software development process and describe how experiments can be tracked throughout development.
- Score: 0.8739101659113154
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum computers are more widely available than ever, making the field more accessible and widespread. Practitioners are coming from a wide range of domains, conducting experiments and research using quantum computing approaches across a variety of problems. The current literature suggests that developers follow certain methodologies in quantum software development, often with a matching set of tools provided. Yet with the novel paradigm, there are areas that remain unaddressed in practices and tools. In this article, we go into the details of experiment tracking in quantum software development. We explain the basic concept of experiment tracking and detail how, in essence, quantum computing sets demands on tracking practices. Given the experimental state of hardware and the constantly evolving software, quantum execution must be monitored, marginal gains aggregated for the best outcome, and error sources detected. In our case study, quantum reservoir computing for chaotic time series data prediction with error mitigation, we present a detailed quantum software development process and describe how experiments can be tracked throughout development. We then generalize this knowledge into the broader quantum development process.
Related papers
- CircInspect: Integrating Visual Circuit Analysis, Abstraction, and Real-Time Development in Quantum Debugging [3.147897243459717]
Quantum software development introduces complexities due to the probabilistic nature of quantum computing.<n>By leveraging breakpoints and real-time software development features, toolnameempowers users to analyze isolated quantum circuit components, monitor program output, visualize structural changes, and abstract information to enhance comprehension.
arXiv Detail & Related papers (2025-09-04T00:07:34Z) - Digital quantum simulation of many-body systems: Making the most of intermediate-scale, noisy quantum computers [51.56484100374058]
This thesis is centered around simulating quantum dynamics on quantum devices.<n>We present an overview of the most relevant quantum algorithms for quantum dynamics.<n>We identify relevant problems within quantum dynamics that could benefit from quantum simulation in the near future.
arXiv Detail & Related papers (2025-08-29T10:37:19Z) - 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) - Quantum Information Processing with Molecular Nanomagnets: an introduction [49.89725935672549]
We provide an introduction to Quantum Information Processing, focusing on a promising setup for its implementation.
We introduce the basic tools to understand and design quantum algorithms, always referring to their actual realization on a molecular spin architecture.
We present some examples of quantum algorithms proposed and implemented on a molecular spin qudit hardware.
arXiv Detail & Related papers (2024-05-31T16:43:20Z) - Quantum computing: principles and applications [3.717431207294639]
We introduce the basic principles of quantum computing and the multilayer architecture for a quantum computer.
Based on a mature experimental platform, the Nuclear Magnetic Resonance (NMR) platform, we introduce the basic steps to experimentally implement quantum computing.
arXiv Detail & Related papers (2023-10-13T20:12:28Z) - Quantum algorithms: A survey of applications and end-to-end complexities [88.57261102552016]
The anticipated applications of quantum computers span across science and industry.<n>We present a survey of several potential application areas of quantum algorithms.<n>We outline the challenges and opportunities in each area in an "end-to-end" fashion.
arXiv Detail & Related papers (2023-10-04T17:53:55Z) - The QUATRO Application Suite: Quantum Computing for Models of Human
Cognition [49.038807589598285]
We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling.
We release QUATRO, a collection of quantum computing applications from cognitive models.
arXiv Detail & Related papers (2023-09-01T17:34:53Z) - Quantum Computing Toolkit From Nuts and Bolts to Sack of Tools [0.0]
Quantum computing has the potential to provide exponential performance benefits in processing over classical computing.
It utilizes quantum mechanics phenomena (such as superposition, entanglement, and interference) to solve a computational problem.
Quantum computers are in the nascent stage of development and are noisy due to decoherence, i.e., quantum bits deteriorate with environmental interactions.
arXiv Detail & Related papers (2023-02-17T14:08:44Z) - Quantum Advantage Seeker with Kernels (QuASK): a software framework to
speed up the research in quantum machine learning [0.9217021281095907]
QuASK is an open-source quantum machine learning framework written in Python.
It implements most state-of-the-art algorithms to analyze the data through quantum kernels.
It can be used as a command-line tool to download datasets, pre-process them, quantum machine learning routines, analyze and visualize the results.
arXiv Detail & Related papers (2022-06-30T13:43:16Z) - Formal Verification of Quantum Programs: Theory, Tools and Challenges [0.0]
Survey aims to be a short introduction into the area of formal verification of quantum programs.
This survey examines some of the challenges that the field may face in the future, namely the development of complex quantum algorithms.
arXiv Detail & Related papers (2021-10-04T11:00:48Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z)
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.