A Tool For Debugging Quantum Circuits

• URL: http://arxiv.org/abs/2205.01899v1
• Date: Wed, 4 May 2022 05:36:52 GMT
• Title: A Tool For Debugging Quantum Circuits
• Authors: Sara Ayman Metwalli and Rodney Van Meter
• Abstract summary: The tool allows the user to divide the circuit vertically or horizontally into smaller chunks known as slices. The tool also enables developers to track gates within the overall circuit and each chunk to understand their behavior better.
• Score: 1.2183405753834562
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As the scale of quantum programs grows to match that of classical software, the nascent field of quantum software engineering must mature and tools such as debuggers will become increasingly important. However, developing a quantum debugger is challenging due to the nature of a quantum computer; sneaking a peek at the value of a quantum state will cause either partial or complete collapse of the superposition and may destroy the necessary entanglement. As a first step to developing a full quantum circuit debugger, we have designed and implemented a quantum circuit debugging tool. The tool allows the user to divide the circuit vertically or horizontally into smaller chunks known as slices, and manage their simulation or execution for either interactive debugging or automated testing. The tool also enables developers to track gates within the overall circuit and each chunk to understand their behavior better. Feedback on usefulness and usability from early users shows that using the tool to slice and test their circuits has helped make the debugging process more time-efficient for them.

Related papers

• A Framework for Debugging Quantum Programs [4.704614749567071]
  Development of quantum programs can be time-consuming and tedious. New tools that could aid developers in resolving these errors are still barely existent. This work proposes a framework, available as an open-source implementation at https://github.com/cda-tum/mqt-ger.
  arXiv  Detail & Related papers  (2024-12-16T19:00:07Z)
• Demonstrating real-time and low-latency quantum error correction with superconducting qubits [52.08698178354922]
  We demonstrate low-latency feedback with a scalable FPGA decoder integrated into a superconducting quantum processor. We observe logical error suppression as the number of decoding rounds is increased. The decoder throughput and latency developed in this work, combined with continued device improvements, unlock the next generation of experiments.
  arXiv  Detail & Related papers  (2024-10-07T17:07:18Z)
• 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 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)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Design Verification of the Quantum Control Stack [0.5089990359065384]
  The paper serves both as an introduction to quantum computing and to how classical device verification techniques can be employed there. Two main challenges in building a quantum control stack are generating precise deterministic-timing operations at the edge and scaled-out processing in the middle layer.
  arXiv  Detail & Related papers  (2023-10-08T16:51:48Z)
• Demonstration of a Hardware-Independent Toolkit for Automated Quantum Subcircuit Synthesis [2.828466685313335]
  This article describes an automated quantum-software toolkit for synthesis, compilation, and optimization. It transforms classically-specified, irreversible functions into both technology-independent and technology-dependent quantum circuits. We describe and analyze the toolkit's application to three situations -- quantum read-only memories, quantum random number generators, and quantum oracles.
  arXiv  Detail & Related papers  (2023-09-02T21:46:38Z)
• 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)
• 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 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)
• 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)

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.