Preparation of excited states for nuclear dynamics on a quantum computer
- URL: http://arxiv.org/abs/2009.13485v2
- Date: Fri, 8 Jan 2021 21:59:27 GMT
- Title: Preparation of excited states for nuclear dynamics on a quantum computer
- Authors: Alessandro Roggero, Chenyi Gu, Alessandro Baroni and Thomas Papenbrock
- Abstract summary: We study two different methods to prepare excited states on a quantum computer.
We benchmark these techniques on emulated and real quantum devices.
These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
- Score: 117.44028458220427
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study two different methods to prepare excited states on a quantum
computer, a key initial step to study dynamics within linear response theory.
The first method uses unitary evolution for a short time
$T=\mathcal{O}(\sqrt{1-F})$ to approximate the action of an excitation operator
$\hat{O}$ with fidelity $F$ and success probability $P\approx1-F$. The second
method probabilistically applies the excitation operator using the Linear
Combination of Unitaries (LCU) algorithm. We benchmark these techniques on
emulated and real quantum devices, using a toy model for thermal neutron-proton
capture. Despite its larger memory footprint, the LCU-based method is efficient
even on current generation noisy devices and can be implemented at a lower gate
cost than a naive analysis would suggest. These findings show that quantum
techniques designed to achieve good asymptotic scaling on fault tolerant
quantum devices might also provide practical benefits on devices with limited
connectivity and gate fidelity.
Related papers
- Scalable quantum dynamics compilation via quantum machine learning [7.31922231703204]
variational quantum compilation (VQC) methods employ variational optimization to reduce gate costs while maintaining high accuracy.
We show that our approach exceeds state-of-the-art compilation results in both system size and accuracy in one dimension ($1$D)
For the first time, we extend VQC to systems on two-dimensional (2D) strips with a quasi-1D treatment, demonstrating a significant resource advantage over standard Trotterization methods.
arXiv Detail & Related papers (2024-09-24T18:00:00Z) - 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) - Quantum-Trajectory-Inspired Lindbladian Simulation [15.006625290843187]
We propose two quantum algorithms for simulating the dynamics of open quantum systems governed by Lindbladians.
The first algorithm achieves a gate complexity independent of the number of jump operators, $m$, marking a significant improvement in efficiency.
The second algorithm achieves near-optimal dependence on the evolution time $t$ and precision $epsilon$ and introduces only an additional $tildeO(m)$ factor.
arXiv Detail & Related papers (2024-08-20T03:08:27Z) - 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) - A single $T$-gate makes distribution learning hard [56.045224655472865]
This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits.
We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
arXiv Detail & Related papers (2022-07-07T08:04:15Z) - Optimal quantum control via genetic algorithms for quantum state
engineering in driven-resonator mediated networks [68.8204255655161]
We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms.
We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator.
We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
arXiv Detail & Related papers (2022-06-29T14:34:00Z) - Commutation simulator for open quantum dynamics [0.0]
We propose an innovative method to investigate directly the properties of a time-dependent density operator $hatrho(t)$.
We can directly compute the expectation value of the commutation relation and thus of the rate of change of $hatrho(t)$.
A simple but important example is demonstrated in the single-qubit case and we discuss extension of the method for practical quantum simulation with many qubits.
arXiv Detail & Related papers (2022-06-01T16:03:43Z) - F-Divergences and Cost Function Locality in Generative Modelling with
Quantum Circuits [0.0]
We consider training a quantum circuit Born machine using $f$-divergences.
We introduce two algorithms which demonstrably improve the training of the Born machine.
We discuss the long-term implications of quantum devices for computing $f$-divergences.
arXiv Detail & Related papers (2021-10-08T17:04:18Z) - Towards a NISQ Algorithm to Simulate Hermitian Matrix Exponentiation [0.0]
A practical fault-tolerant quantum computer is worth looking forward to as it provides applications that outperform their known classical counterparts.
It would take decades to make it happen, exploiting the power of noisy intermediate-scale quantum(NISQ) devices, which already exist, is becoming one of current goals.
In this article, a method is reported as simulating a hermitian matrix exponentiation using parametrized quantum circuit.
arXiv Detail & Related papers (2021-05-28T06:37:12Z) - Simulation of Thermal Relaxation in Spin Chemistry Systems on a Quantum
Computer Using Inherent Qubit Decoherence [53.20999552522241]
We seek to take advantage of qubit decoherence as a resource in simulating the behavior of real world quantum systems.
We present three methods for implementing the thermal relaxation.
We find excellent agreement between our results, experimental data, and the theoretical prediction.
arXiv Detail & Related papers (2020-01-03T11:48:11Z)
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.