Variational Quantum Circuits for Multi-Qubit Gate Automata

• URL: http://arxiv.org/abs/2209.00139v1
• Date: Wed, 31 Aug 2022 22:05:17 GMT
• Title: Variational Quantum Circuits for Multi-Qubit Gate Automata
• Authors: Arunava Majumder, Dylan Lewis, Sougato Bose
• Abstract summary: Variational quantum algorithms (VQAs) may have the capacity to provide a quantum advantage in the Noisy Intermediate-scale Quantum (NISQ) era. We present a quantum machine learning framework, inspired by VQAs, to tackle the problem of finding time-independent Hamiltonians that generate desired unitary evolutions.
• Score: 0.6445605125467573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational quantum algorithms (VQAs) may have the capacity to provide a quantum advantage in the Noisy Intermediate-scale Quantum (NISQ) era. Here, we present a quantum machine learning (QML) framework, inspired by VQAs, to tackle the problem of finding time-independent Hamiltonians that generate desired unitary evolutions, i.e. multi-qubit quantum gates. The Hamiltonians are designed by tuning local fields and two-body interaction terms only. We find that our approach achieves high fidelity quantum gates, such as the Toffoli gate, with a significantly lower computational complexity than is possible classically. This method can also be extended to realize higher-order multi-controlled quantum gates, which could be directly applied to quantum error correction (QEC) schemes.

Related papers

• Universal quantum computation using quantum annealing with the transverse-field Ising Hamiltonian [0.0]
  We present a practical method for implementing universal quantum computation using the transverse-field Ising Hamiltonian. Our proposal is compatible with D-Wave devices, opening up possibilities for realizing large-scale gate-based quantum computers.
  arXiv  Detail & Related papers  (2024-02-29T12:47:29Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• 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)
• Quantum process tomography of continuous-variable gates using coherent states [49.299443295581064]
  We demonstrate the use of coherent-state quantum process tomography (csQPT) for a bosonic-mode superconducting circuit. We show results for this method by characterizing a logical quantum gate constructed using displacement and SNAP operations on an encoded qubit.
  arXiv  Detail & Related papers  (2023-03-02T18:08:08Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy [67.02951777522547]
  We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs) We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution. Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
  arXiv  Detail & Related papers  (2022-05-10T08:05:59Z)
• Variational Quantum-Neural Hybrid Error Mitigation [6.555128824546528]
  Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers. We show how variational quantum-neural hybrid eigensolver (VQNHE) algorithm is inherently noise resilient with a unique QEM capacity.
  arXiv  Detail & Related papers  (2021-12-20T08:07:58Z)
• Variational Quantum Eigensolver for SU($N$) Fermions [0.0]
  Variational quantum algorithms aim at harnessing the power of noisy intermediate-scale quantum computers. We apply the variational quantum eigensolver to study the ground-state properties of $N$-component fermions. Our approach lays out the basis for a current-based quantum simulator of many-body systems.
  arXiv  Detail & Related papers  (2021-06-29T16:39:30Z)
• Noncyclic nonadiabatic holonomic quantum gates via shortcuts to adiabaticity [5.666193021459319]
  We propose a fast and robust scheme to construct high-fidelity holonomic quantum gates for universal quantum systems via shortcuts to adiabaticity. Our scheme is readily realizable in physical system currently pursued for implementation of quantum computation.
  arXiv  Detail & Related papers  (2021-05-28T15:23:24Z)
• Variational quantum compiling with double Q-learning [0.37798600249187286]
  We propose a variational quantum compiling (VQC) algorithm based on reinforcement learning (RL) An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q-learning. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices.
  arXiv  Detail & Related papers  (2021-03-22T06:46:35Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.