Teaching Quantum Design Automation with Block-Based Programming

• URL: http://arxiv.org/abs/2608.18206v1
• Date: Tue, 18 Aug 2026 18:00:02 GMT
• Title: Teaching Quantum Design Automation with Block-Based Programming
• Abstract summary: We present a block-based programming framework for quantum design automation, implemented as an extension to the Scratch programming platform.<n>This system allows users to build quantum circuits as a sequence of blocks and embed them in classical control logic.<n>Results demonstrate strong understanding and confidence in quantum design automation concepts.
• Score: 5.232399005185121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As quantum circuits grow beyond small toy examples, preparing them for execution on physical devices becomes increasingly complex. Design automation is therefore essential for scalable quantum computing: Compilation procedures optimize resource requirements and transform circuits to a format compatible with specific hardware; resource estimation evaluates execution cost; verification methods prove circuit correctness. However, these concepts present a steep learning curve for novices, particularly when quantum circuits are introduced through low-level textual representations. To address this, we present a block-based programming framework for quantum design automation, implemented as an extension to the Scratch programming platform. This system allows users to build quantum circuits as a sequence of blocks and embed them in classical control logic to perform evaluations, compare simulation results, and directly apply different design automation techniques. We evaluated the approach in a user study with computer science students, who completed guided exercises using the platform and provided structured feedback in the form of self-reports and short knowledge assessments. Results demonstrate strong understanding and confidence in quantum design automation concepts, suggesting that the block-based approach successfully lowers the entry barrier to quantum design automation. The implemented framework is open-source and available at https://github.com/munich-quantum-toolkit/scratch-quantum.

Related papers

• An LLM System for Autonomous Variational Quantum Circuit Design [0.0]
  We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs.<n>We evaluate the framework on two tasks: quantum feature map construction for quantum machine learning and ansatz generation for variational quantum eigensolver applications in quantum chemistry.
  arXiv  Detail & Related papers  (2026-06-11T14:08:00Z)
• Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors [0.0]
  Embedded quantum machine learning (EQML) seeks to bring quantum machine learning capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers.<n>In 2026, EQML is technically feasible only in limited and highly experimental forms.
  arXiv  Detail & Related papers  (2026-03-13T00:48:35Z)
• Investigating Quantum Circuit Designs Using Neuro-Evolution [2.9631016562930537]
  We propose an evolutionary approach to the automated design and training of quantum circuits.<n>The proposed method searches over gate types, qubit connectivity, parameterization, and circuit depth while respecting hardware and noise constraints.<n>Preliminary results demonstrate that circuits evolved on classification tasks are able to achieve over 90% accuracy.
  arXiv  Detail & Related papers  (2026-02-03T18:57:39Z)
• AQER: a scalable and efficient data loader for digital quantum computers [62.40228216126285]
  We develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states.<n>We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits.
  arXiv  Detail & Related papers  (2026-02-02T14:39:42Z)
• Quantum Design Automation: Foundations, Challenges, and the Road Ahead [39.223805375181776]
  We advocate for a holistic design perspective in quantum computing.<n>We detail how interconnected computational methods and tools collaborate to enable end-to-end quantum computer design.
  arXiv  Detail & Related papers  (2025-11-13T16:44:36Z)
• FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design [8.70817825961863]
  We introduce textscFlowQ-Net (Flow-based Quantum design Network), a generative framework for automated quantum circuit synthesis.<n>This framework learns a policy to construct circuits sequentially, sampling them in to a flexible user-defined reward function.<n>We demonstrate the efficacy of textscFlowQ-Net through an extensive set of simulations.
  arXiv  Detail & Related papers  (2025-10-30T16:57:13Z)
• QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design [63.02824918725805]
  Quantum computing is recognized for the significant speedup it offers over classical computing through quantum algorithms.<n>QCircuitBench is the first benchmark dataset designed to evaluate AI's capability in designing and implementing quantum algorithms.
  arXiv  Detail & Related papers  (2024-10-10T14:24:30Z)
• Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects [59.07692103357675]
  This survey explores the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware.<n>It becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
  arXiv  Detail & Related papers  (2024-06-30T15:50:10Z)
• 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 circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• QUANTIFY: A framework for resource analysis and design verification of quantum circuits [69.43216268165402]
  QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits. It is based on Google Cirq and is developed with Clifford+T circuits in mind. For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
  arXiv  Detail & Related papers  (2020-07-21T15:36:25Z)

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.