Optimal Hamiltonian recognition of unknown quantum dynamics
- URL: http://arxiv.org/abs/2412.13067v1
- Date: Tue, 17 Dec 2024 16:31:35 GMT
- Title: Optimal Hamiltonian recognition of unknown quantum dynamics
- Authors: Chengkai Zhu, Shuyu He, Yu-Ao Chen, Lei Zhang, Xin Wang,
- Abstract summary: We introduce Hamiltonian recognition, a framework to identify the Hamiltonian governing quantum dynamics from a known set of Hamiltonians.
We develop a quantum algorithm for coherent function simulation on two quantum signal processing structures.
We demonstrate the validity of our protocol on a superconducting quantum processor.
- Score: 9.075075598775758
- License:
- Abstract: Identifying unknown Hamiltonians from their quantum dynamics is a pivotal challenge in quantum technologies and fundamental physics. In this paper, we introduce Hamiltonian recognition, a framework that bridges quantum hypothesis testing and quantum metrology, aiming to identify the Hamiltonian governing quantum dynamics from a known set of Hamiltonians. To identify $H$ for an unknown qubit quantum evolution $\exp(-iH\theta)$ with unknown $\theta$, from two or three orthogonal Hamiltonians, we develop a quantum algorithm for coherent function simulation, built on two quantum signal processing (QSP) structures. It can simultaneously realize a target polynomial based on measurement results regardless of the chosen signal unitary for the QSP. Utilizing semidefinite optimization and group representation theory, we prove that our methods achieve the optimal average success probability, taken over possible Hamiltonians $H$ and parameters $\theta$, decays as $O(1/k)$ with $k$ queries of the unknown unitary transformation. Furthermore, we demonstrate the validity of our protocol on a superconducting quantum processor. This work presents an efficient method to recognize Hamiltonians from limited queries of the dynamics, opening new avenues in composite channel discrimination and quantum metrology.
Related papers
- Efficient Quantum Pseudorandomness from Hamiltonian Phase States [41.94295877935867]
We introduce a quantum hardness assumption called the Hamiltonian Phase State (HPS) problem.
We show that our assumption is plausibly fully quantum; meaning, it cannot be used to construct one-way functions.
We show that our assumption and its variants allow us to efficiently construct many pseudorandom quantum primitives.
arXiv Detail & Related papers (2024-10-10T16:10:10Z) - Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - Coherence generation with Hamiltonians [44.99833362998488]
We explore methods to generate quantum coherence through unitary evolutions.
This quantity is defined as the maximum derivative of coherence that can be achieved by a Hamiltonian.
We identify the quantum states that lead to the largest coherence derivative induced by the Hamiltonian.
arXiv Detail & Related papers (2024-02-27T15:06:40Z) - Truncation technique for variational quantum eigensolver for Molecular
Hamiltonians [0.0]
variational quantum eigensolver (VQE) is one of the most promising quantum algorithms for noisy quantum devices.
We propose a physically intuitive truncation technique that starts the optimization procedure with a truncated Hamiltonian.
This strategy allows us to reduce the required number of evaluations for the expectation value of Hamiltonian on a quantum computer.
arXiv Detail & Related papers (2024-02-02T18:45:12Z) - Expanding Hardware-Efficiently Manipulable Hilbert Space via Hamiltonian
Embedding [9.219297088819634]
Many promising quantum applications depend on the efficient quantum simulation of an exponentially large sparse Hamiltonian.
In this paper, we propose a technique named Hamiltonian embedding.
This technique simulates a desired sparse Hamiltonian by embedding it into the evolution of a larger and more structured quantum system.
arXiv Detail & Related papers (2024-01-16T18:19:29Z) - 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) - 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) - Algebraic Compression of Quantum Circuits for Hamiltonian Evolution [52.77024349608834]
Unitary evolution under a time dependent Hamiltonian is a key component of simulation on quantum hardware.
We present an algorithm that compresses the Trotter steps into a single block of quantum gates.
This results in a fixed depth time evolution for certain classes of Hamiltonians.
arXiv Detail & Related papers (2021-08-06T19:38:01Z) - 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) - A Hybrid Quantum-Classical Hamiltonian Learning Algorithm [6.90132007891849]
Hamiltonian learning is crucial to the certification of quantum devices and quantum simulators.
We propose a hybrid quantum-classical Hamiltonian learning algorithm to find the coefficients of the Pauli operator of the Hamiltonian.
arXiv Detail & Related papers (2021-03-01T15:15:58Z) - Hybrid Quantum-Classical Eigensolver Without Variation or Parametric
Gates [0.0]
We present a process for obtaining the eigenenergy spectrum of electronic quantum systems.
This is achieved by projecting the Hamiltonian of a quantum system onto a limited effective Hilbert space.
A process for preparing short depth quantum circuits to measure the corresponding diagonal and off-diagonal terms of the effective Hamiltonian is given.
arXiv Detail & Related papers (2020-08-26T02:31:24Z)
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.