SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture

• URL: http://arxiv.org/abs/2211.12711v2
• Date: Fri, 22 Sep 2023 23:14:17 GMT
• Title: SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture
• Authors: Han Zheng, Christopher Kang, Gokul Subramanian Ravi, Hanrui Wang, Kanav Setia, Frederic T. Chong, Junyu Liu
• Abstract summary: SnCQA is a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits. Our quantum neural networks are suitable for solving machine learning problems where permutation symmetries are present.
• Score: 11.404166974371197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries with the number of qubits $n$. By exploiting permutation symmetries of the system, such as lattice Hamiltonians common to many quantum many-body and quantum chemistry problems, Our quantum neural networks are suitable for solving machine learning problems where permutation symmetries are present, which could lead to significant savings of computational costs. Aside from its theoretical novelty, we find our simulations perform well in practical instances of learning ground states in quantum computational chemistry, where we could achieve comparable performances to traditional methods with few tens of parameters. Compared to other traditional variational quantum circuits, such as the pure hardware-efficient ansatz (pHEA), we show that SnCQA is more scalable, accurate, and noise resilient (with $20\times$ better performance on $3 \times 4$ square lattice and $200\% - 1000\%$ resource savings in various lattice sizes and key criterions such as the number of layers, parameters, and times to converge in our cases), suggesting a potentially favorable experiment on near-time quantum devices.

Related papers

• Sequential Quantum Computing [41.94295877935867]
  We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple or heterogeneous quantum processors.<n>SQC overcomes the limitations of each type of quantum computer by combining their complementary strengths.<n>These results highlight SQC as a powerful and versatile approach for addressing complex quantum optimization problems.
  arXiv  Detail & Related papers  (2025-06-25T17:51:29Z)
• Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search [0.7639610349097473]
  This article proposes a gradient-free Monte Carlo Tree Search (MCTS) technique to automate the process of quantum circuit design. Compared to previous MCTS research, our technique reduces the number of quantum circuit evaluations by a factor of 10 up to 100. The resulting quantum circuits exhibit up to three times fewer CNOT gates, which is important for implementation on noisy quantum hardware.
  arXiv  Detail & Related papers  (2025-02-06T10:52:11Z)
• Entropy-driven entanglement forging [0.0]
  We show how entropy-driven entanglement forging can be used to adjust quantum simulations to the limitations of noisy intermediate-scale quantum devices. Our findings indicate that our method, entropy-driven entanglement forging, can be used to adjust quantum simulations to the limitations of noisy intermediate-scale quantum devices.
  arXiv  Detail & Related papers  (2024-09-06T16:54:41Z)
• 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)
• Benchmarking Variational Quantum Eigensolvers for Entanglement Detection in Many-Body Hamiltonian Ground States [37.69303106863453]
  Variational quantum algorithms (VQAs) have emerged in recent years as a promise to obtain quantum advantage. We use a specific class of VQA named variational quantum eigensolvers (VQEs) to benchmark them at entanglement witnessing and entangled ground state detection. Quantum circuits whose structure is inspired by the Hamiltonian interactions presented better results on cost function estimation than problem-agnostic circuits.
  arXiv  Detail & Related papers  (2024-07-05T12:06:40Z)
• 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)
• A multiple-circuit approach to quantum resource reduction with application to the quantum lattice Boltzmann method [39.671915199737846]
  We introduce a multiple-circuit algorithm for a quantum lattice Boltzmann method (QLBM) solve of the incompressible Navier--Stokes equations. The presented method is validated and demonstrated for 2D lid-driven cavity flow.
  arXiv  Detail & Related papers  (2024-01-20T15:32:01Z)
• 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)
• Classical variational optimization of PREPARE circuit for quantum phase estimation of quantum chemistry Hamiltonians [0.8009842832476994]
  We propose a method for constructing $textttPREPARE$ circuits for quantum phase estimation of a molecular Hamiltonian in quantum chemistry. The $textttPREPARE$ circuit generates a quantum state which encodes the coefficients of the terms in the Hamiltonian as probability amplitudes.
  arXiv  Detail & Related papers  (2023-08-26T05:32:38Z)
• Efficient estimation of trainability for variational quantum circuits [43.028111013960206]
  We find an efficient method to compute the cost function and its variance for a wide class of variational quantum circuits. This method can be used to certify trainability for variational quantum circuits and explore design strategies that can overcome the barren plateau problem.
  arXiv  Detail & Related papers  (2023-02-09T14:05:18Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Differentiable matrix product states for simulating variational quantum computational chemistry [6.954927515599816]
  We propose a parallelizable classical simulator for variational quantum eigensolver(VQE) Our simulator seamlessly integrates the quantum circuit evolution into the classical auto-differentiation framework. As applications, we use our simulator to study commonly used small molecules such as HF, LiH and H$$O, as well as larger molecules CO$$, BeH$ and H$_4$ with up to $40$ qubits.
  arXiv  Detail & Related papers  (2022-11-15T08:36:26Z)
• Qubit-efficient encoding scheme for quantum simulations of electronic structure [5.16230883032882]
  Simulating electronic structure on a quantum computer requires encoding of fermionic systems onto qubits. We propose a qubit-efficient encoding scheme that requires the qubit number to be only logarithmic in the number of configurations that satisfy the required conditions and symmetries. Our proposed scheme and results show the feasibility of quantum simulations for larger molecular systems in the noisy intermediate-scale quantum (NISQ) era.
  arXiv  Detail & Related papers  (2021-10-08T13:20:18Z)
• Automatically Differentiable Quantum Circuit for Many-qubit State Preparation [1.5662820454886202]
  We propose the automatically differentiable quantum circuit (ADQC) approach to efficiently prepare arbitrary quantum many-qubit states. The circuit is optimized by updating the latent gates using back propagation to minimize the distance between the evolved and target states. Our work sheds light on the "intelligent construction" of quantum circuits for many-qubit systems by combining with the machine learning methods.
  arXiv  Detail & Related papers  (2021-04-30T12:22:26Z)

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.