Universal quantum computation using quantum annealing with the
transverse-field Ising Hamiltonian
- URL: http://arxiv.org/abs/2402.19114v1
- Date: Thu, 29 Feb 2024 12:47:29 GMT
- Title: Universal quantum computation using quantum annealing with the
transverse-field Ising Hamiltonian
- Authors: Takashi Imoto, Yuki Susa, Ryoji Miyazaki, Tadashi Kadowaki, Yuichiro
Matsuzaki
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum computation is a promising emerging technology, and by utilizing the
principles of quantum mechanics, it is expected to achieve faster computations
than classical computers for specific problems. There are two distinct
architectures for quantum computation: gate-based quantum computers and quantum
annealing. In gate-based quantum computation, we implement a sequence of
quantum gates that manipulate qubits. This approach allows us to perform
universal quantum computation, yet they pose significant experimental
challenges for large-scale integration. On the other hand, with quantum
annealing, the solution of the optimization problem can be obtained by
preparing the ground state. Conventional quantum annealing devices with
transverse-field Ising Hamiltonian, such as those manufactured by D-Wave Inc.,
achieving around 5000 qubits, are relatively more amenable to large-scale
integration but are limited to specific computations. In this paper, we present
a practical method for implementing universal quantum computation within the
conventional quantum annealing architecture using the transverse-field Ising
Hamiltonian. Our innovative approach relies on an adiabatic transformation of
the Hamiltonian, changing from transverse fields to a ferromagnetic interaction
regime, where the ground states become degenerate. Notably, our proposal is
compatible with D-Wave devices, opening up possibilities for realizing
large-scale gate-based quantum computers. This research bridges the gap between
conventional quantum annealing and gate-based quantum computation, offering a
promising path toward the development of scalable quantum computing platforms.
Related papers
- Distributed Quantum Computation via Entanglement Forging and Teleportation [13.135604356093193]
Distributed quantum computation is a practical method for large-scale quantum computation on quantum processors with limited size.
In this paper, we demonstrate the methods to implement a nonlocal quantum circuit on two quantum processors without any quantum correlations.
arXiv Detail & Related papers (2024-09-04T08:10:40Z) - 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) - 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) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - SAT-Based Quantum Circuit Adaptation [0.9784637657097822]
Adapting a quantum circuit from a universal quantum gate set to the quantum gate set of a target hardware modality has a crucial impact on the fidelity and duration of the intended quantum computation.
We develop a satisfiability modulo theories model that determines an optimized quantum circuit adaptation given a set of allowed substitutions and decompositions.
arXiv Detail & Related papers (2023-01-27T14:09:29Z) - 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) - Quantum Neural Architecture Search with Quantum Circuits Metric and
Bayesian Optimization [2.20200533591633]
We propose a new quantum gates distance that characterizes the gates' action over every quantum state.
Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems.
arXiv Detail & Related papers (2022-06-28T16:23:24Z) - Numerical hardware-efficient variational quantum simulation of a soliton
solution [0.0]
We discuss the capabilities of quantum algorithms with special attention paid to a hardware-efficient variational eigensolver.
A delicate interplay between magnetic interactions allows one to stabilize a chiral state that destroys the homogeneity of magnetic ordering.
We argue that, while being capable of correctly reproducing a uniform magnetic configuration, the hardware-efficient ansatz meets difficulties in providing a detailed description to a noncollinear magnetic structure.
arXiv Detail & Related papers (2021-05-13T11:58:18Z) - 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) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z) - Simulating quantum chemistry in the seniority-zero space on qubit-based
quantum computers [0.0]
We combine the so-called seniority-zero, or paired-electron, approximation of computational quantum chemistry with techniques for simulating molecular chemistry on gate-based quantum computers.
We show that using the freed-up quantum resources for increasing the basis set can lead to more accurate results and reductions in the necessary number of quantum computing runs.
arXiv Detail & Related papers (2020-01-31T19:44:37Z)
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.