Colloquium: Advances in automation of quantum dot devices control
- URL: http://arxiv.org/abs/2112.09362v3
- Date: Thu, 25 May 2023 15:52:24 GMT
- Title: Colloquium: Advances in automation of quantum dot devices control
- Authors: Justyna P. Zwolak and Jacob M. Taylor
- Abstract summary: Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems.
The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem.
In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Arrays of quantum dots (QDs) are a promising candidate system to realize
scalable, coupled qubit systems and serve as a fundamental building block for
quantum computers. In such semiconductor quantum systems, devices now have tens
of individual electrostatic and dynamical voltages that must be carefully set
to localize the system into the single-electron regime and to realize good
qubit operational performance. The mapping of requisite QD locations and
charges to gate voltages presents a challenging classical control problem. With
an increasing number of QD qubits, the relevant parameter space grows
sufficiently to make heuristic control unfeasible. In recent years, there has
been considerable effort to automate device control that combines script-based
algorithms with machine learning (ML) techniques. In this Colloquium, a
comprehensive overview of the recent progress in the automation of QD device
control is presented, with a particular emphasis on silicon- and GaAs-based QDs
formed in two-dimensional electron gases. Combining physics-based modeling with
modern numerical optimization and ML has proven effective in yielding
efficient, scalable control. Further integration of theoretical, computational,
and experimental efforts with computer science and ML holds vast potential in
advancing semiconductor and other platforms for quantum computing.
Related papers
- Quantum Digital Simulation of Cavity Quantum Electrodynamics: Insights from Superconducting and Trapped Ion Quantum Testbeds [0.016994625126740815]
We present an early exploration of the potential for quantum computers to efficiently investigate open CQED physics.
Our simulations make use of a recent quantum algorithm that maps the dynamics of a singly excited open Tavis-Cummings model containing $N$ atoms.
By applying technology-specific transpilation and error mitigation techniques, we minimize the impact of gate errors, noise, and decoherence in each hardware platform.
arXiv Detail & Related papers (2024-04-05T02:25:49Z) - Quantum control by the environment: Turing uncomputability, Optimization over Stiefel manifolds, Reachable sets, and Incoherent GRAPE [56.47577824219207]
In many practical situations, the controlled quantum systems are open, interacting with the environment.
In this note, we briefly review some results on control of open quantum systems using environment as a resource.
arXiv Detail & Related papers (2024-03-20T10:09:13Z) - TeD-Q: a tensor network enhanced distributed hybrid quantum machine
learning framework [59.07246314484875]
TeD-Q is an open-source software framework for quantum machine learning.
It seamlessly integrates classical machine learning libraries with quantum simulators.
It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
arXiv Detail & Related papers (2023-01-13T09:35:05Z) - Potential and limitations of quantum extreme learning machines [55.41644538483948]
We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements.
Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
arXiv Detail & Related papers (2022-10-03T09:32:28Z) - Estimating Phosphorescent Emission Energies in Ir(III) Complexes using
Large-Scale Quantum Computing Simulations [0.0]
We apply the iterative qubit coupled cluster (iQCC) method on classical hardware to the calculation of the transition energies in nine phosphorescent iridium complexes.
Our simulations would require a gate-based quantum computer with a minimum of 72 fully-connected and error-corrected logical qubits.
The iQCC quantum method is found to match the accuracy of the fine-tuned DFT functionals, has a better Pearson correlation coefficient, and still has considerable potential for systematic improvement.
arXiv Detail & Related papers (2021-11-07T20:02:10Z) - On exploring practical potentials of quantum auto-encoder with
advantages [92.19792304214303]
Quantum auto-encoder (QAE) is a powerful tool to relieve the curse of dimensionality encountered in quantum physics.
We prove that QAE can be used to efficiently calculate the eigenvalues and prepare the corresponding eigenvectors of a high-dimensional quantum state.
We devise three effective QAE-based learning protocols to solve the low-rank state fidelity estimation, the quantum Gibbs state preparation, and the quantum metrology tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - 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) - Entanglement transfer, accumulation and retrieval via quantum-walk-based
qubit-qudit dynamics [50.591267188664666]
Generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies.
We propose a protocol that is able to attain entangled states of $d$-dimensional systems through a quantum-walk-based it transfer & accumulate mechanism.
In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons.
arXiv Detail & Related papers (2020-10-14T14:33:34Z) - 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) - End-to-End Quantum Machine Learning Implemented with Controlled Quantum
Dynamics [0.9599644507730106]
This work presents a hardware-friendly end-to-end quantum machine learning scheme that can be implemented with imperfect near-term intermediate-scale quantum (NISQ) processors.
The proposal transforms the machine learning task to the optimization of controlled quantum dynamics, in which the learning model is parameterized by experimentally tunable control variables.
Our design also enables automated feature selection by encoding the raw input to quantum states through agent control variables.
arXiv Detail & Related papers (2020-03-30T17:44:51Z) - 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.