Testing and Debugging Quantum Circuits
- URL: http://arxiv.org/abs/2311.18202v2
- Date: Thu, 7 Mar 2024 04:54:47 GMT
- Title: Testing and Debugging Quantum Circuits
- Authors: Sara Ayman Metwalli and Rodney Van Meter
- Abstract summary: This paper focuses on three types of circuit blocks: Amplitude Permutation, Phase Modulation, and Amplitude Redistribution circuit blocks.
We present a comprehensive unit testing tool (Cirquo) and debug approaches tailored to the unique demands of quantum computing.
- Score: 0.65268245109828
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces a process framework for debugging quantum circuits,
focusing on three distinct types of circuit blocks: Amplitude Permutation,
Phase Modulation, and Amplitude Redistribution circuit blocks. Our research
addresses the critical need for specialized debugging approaches tailored to
the unique properties of each circuit type. For Amplitude Permutation Circuits,
we propose techniques to correct amplitude permutations mimicking classical
operations. In phase modulation circuits, our proposed strategy targets the
precise calibration of phase alterations essential for quantum computations.
The most complex Amplitude Redistribution Circuits demand advanced methods to
adjust probability amplitudes. This research bridges a vital gap in current
methodologies and lays the groundwork for future advancements in quantum
circuit debugging. Our contributions are twofold: We present a comprehensive
unit testing tool (Cirquo) and debugging approaches tailored to the unique
demands of quantum computing, and we provide empirical evidence of its
effectiveness in optimizing quantum circuit performance. This work is a crucial
step toward realizing robust quantum computing systems and their applications
in various domains.
Related papers
- Equivalence Checking of Quantum Circuits via Intermediary Matrix Product Operator [4.306566710489809]
Equivalence checking plays a vital role in identifying errors that may arise during compilation and optimization of quantum circuits.
We introduce a novel method based on Matrix Product Operators (MPOs) for determining the equivalence of quantum circuits.
arXiv Detail & Related papers (2024-10-14T18:00:00Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Learning the expressibility of quantum circuit ansatz using transformer [5.368973814856243]
We propose using a transformer model to predict the expressibility of quantum circuit ansatze.
This research can enhance the understanding of the expressibility of quantum circuit ansatze and advance quantum architecture search algorithms.
arXiv Detail & Related papers (2024-05-29T07:34:07Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Adaptive Circuit Learning of Born Machine: Towards Realization of
Amplitude Embedding and Data Loading [7.88657961743755]
We present a novel algorithm "Adaptive Circuit Learning of Born Machine" (ACLBM)
Our algorithm is tailored to selectively integrate two-qubit entangled gates that best capture the complex entanglement present within the target state.
Empirical results underscore the proficiency of our approach in encoding real-world data through amplitude embedding.
arXiv Detail & Related papers (2023-11-29T16:47:31Z) - 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) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network [64.1951227380212]
We propose that quantum circuits can be modeled as queuing networks.
Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
arXiv Detail & Related papers (2021-06-26T10:55:52Z) - Capacity and quantum geometry of parametrized quantum circuits [0.0]
Parametrized quantum circuits can be effectively implemented on current devices.
We evaluate the capacity and trainability of these circuits using the geometric structure of the parameter space.
Our results enhance the understanding of parametrized quantum circuits for improving variational quantum algorithms.
arXiv Detail & Related papers (2021-02-02T18:16:57Z) - 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.