Q-gen: A Parameterized Quantum Circuit Generator

• URL: http://arxiv.org/abs/2407.18697v1
• Date: Fri, 26 Jul 2024 12:22:40 GMT
• Title: Q-gen: A Parameterized Quantum Circuit Generator
• Authors: Yikai Mao, Shaswot Shresthamali, Masaaki Kondo,
• Abstract summary: We introduce Q-gen, a high-level, parameterized quantum circuit generator incorporating 15 realistic quantum algorithms. Q-gen is an open-source project that serves as the entrance for users with a classical computer science background to dive into the world of quantum computing.
• Score: 0.6062751776009752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical domain except for running the quantum circuit in the quantum processor. This leaves massive opportunities for classical automation and optimization toward future utilization of quantum computing. We kickstart the first step in this direction by introducing Q-gen, a high-level, parameterized quantum circuit generator incorporating 15 realistic quantum algorithms. Each customized generation function comes with algorithm-specific parameters beyond the number of qubits, providing a large generation volume with high circuit variability. To demonstrate the functionality of Q-gen, we organize the algorithms into 5 hierarchical systems and generate a quantum circuit dataset accompanied by their measurement histograms and state vectors. This dataset enables researchers to statistically analyze the structure, complexity, and performance of large-scale quantum circuits, or quickly train novel machine learning models without worrying about the exponentially growing simulation time. Q-gen is an open-source and multipurpose project that serves as the entrance for users with a classical computer science background to dive into the world of quantum computing.

Related papers

• QCircuitNet: A Large-Scale Hierarchical Dataset for Quantum Algorithm Design [17.747641494506087]
  We introduce QCircuitNet, the first benchmark and test dataset designed to evaluate AI's capability in designing and implementing quantum algorithms. Unlike using AI for writing traditional codes, this task is fundamentally different and significantly more complicated due to highly flexible design space and intricate manipulation of qubits.
  arXiv  Detail & Related papers  (2024-10-10T14:24:30Z)
• 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)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• 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)
• QArchSearch: A Scalable Quantum Architecture Search Package [1.725192300740999]
  We present textttQArchSearch, an AI based quantum architecture search package with the textttQTensor library as a backend. We show that the search package is able to efficiently scale the search to large quantum circuits and enables the exploration of more complex models for different quantum applications.
  arXiv  Detail & Related papers  (2023-10-11T20:00:33Z)
• 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)
• Parametric Synthesis of Computational Circuits for Complex Quantum Algorithms [0.0]
  The purpose of our quantum synthesizer is enabling users to implement quantum algorithms using higher-level commands. The proposed approach for implementing quantum algorithms has a potential application in the field of machine learning.
  arXiv  Detail & Related papers  (2022-09-20T06:25:47Z)
• Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era [0.0]
  We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis for noise quantum computers with limited number of qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.
  arXiv  Detail & Related papers  (2021-04-27T16:11:27Z)
• Classical variational simulation of the Quantum Approximate Optimization Algorithm [0.0]
  We introduce a method to simulate layered quantum circuits consisting of parametrized gates. A neural-network parametrization of the many-qubit wave function is used. For the largest circuits simulated, we reach 54 qubits at 4 QAOA layers.
  arXiv  Detail & Related papers  (2020-09-03T15:55:27Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)

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.