Holonomic quantum computing offers a promising paradigm for quantum computation due to its error resistance and the ability to perform universal quantum computations. Here, we propose a scheme for the rapid implementation of a holonomic swap gate in neutral atomic systems, based on the selective Rydberg pumping mechanism. By employing time-dependent soft control, we effectively mitigate the impact of off-resonant terms even at higher driving intensities compared to time-independent driving. This approach accelerates the synthesis of logic gates and passively reduces the decoherence effects. Furthermore, by introducing an additional atom and applying the appropriate driving field, our scheme can be directly extended to implement a three-qubit controlled-swap gate. This advancement makes it a valuable tool for quantum state preparation, quantum switches, and a variational quantum algorithm in neutral atom systems.

We study the excitation transfer across a fully connected quantum network whose sites energies can be artificially designed. Starting from a simplified model of a broadly-studied physical system, we systematically optimize its local energies to achieve high excitation transfer for various environmental conditions, using an adaptive Gradient Descent technique and Automatic Differentiation. We show that almost perfect transfer can be achieved with and without local dephasing, provided that the dephasing rates are not too large. We investigate our solutions in terms of resilience against variations in either the network connection strengths, or size, as well as coherence losses. We highlight the different features of a dephasing-free and dephasing-driven transfer. Our work gives further insight into the interplay between coherence and dephasing effects in excitation-transfer phenomena across fully connected quantum networks. In turn, this will help designing optimal transfer in artificial open networks through the simple manipulation of local energies.

The classical moving target segmentation (MTS) algorithm in a video can segment the moving targets out by calculating frame by frame, but the algorithm encounters a real-time problem as the data increases. Recently, the benefits of quantum computing in video processing have been demonstrated, but it is still scarce for MTS. In this paper, a quantum moving target segmentation algorithm for grayscale video based on background difference method is proposed, which can simultaneously model the background of all frames and perform background difference to segment the moving targets. In addition, a feasible quantum subtractor is designed to perform the background difference operation. Then, several quantum units, including quantum cyclic shift transformation, quantum background modeling, quantum background difference, and quantum binarization, are designed in detail to establish the complete quantum circuit. For a video containing 2m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$2^{m}$\\end{document} frames (every frame is a 2n×2n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$2^{n} \ imes 2^{n}$\\end{document} image with q grayscale levels), the complexity of our algorithm is O(n+q)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$(n+q)$\\end{document}. This is an exponential speedup over the classical algorithm and also outperforms the existing quantum algorithms. Finally, the experiment on IBM Q demonstrates the feasibility of our algorithm in this noisy intermediate-scale quantum (NISQ) era.

The properties of strongly-coupled lattice gauge theories at finite density as well as in real time have largely eluded first-principles studies on the lattice. This is due to the failure of importance sampling for systems with a complex action. An alternative to evade the sign problem is quantum simulation. Although still in its infancy, a lot of progress has been made in devising algorithms to address these problems. In particular, recent efforts have addressed the question of how to produce thermal Gibbs states on a quantum computer. In this study, we apply a variational quantum algorithm to a low-dimensional model which has a local abelian gauge symmetry. We demonstrate how this approach can be applied to obtain information regarding the phase diagram as well as unequal-time correlation functions at non-zero temperature.

Quantum algorithms for simulating quantum systems provide a clear and provable advantage over classical algorithms in fault-tolerant settings. There is also interest in quantum algorithms and their implementation in Noisy Intermediate Scale Quantum (NISQ) settings. In these settings, various noise sources and errors must be accounted for when executing any experiments. Recently, NISQ devices have been verified as versatile testbeds for simulating open quantum systems and have been used to simulate simple quantum channels. Our goal is to solve the more complicated problem of simulating convex mixtures of single qubit Pauli channels on NISQ devices. We consider two specific cases: mixtures of Markovian channels that result in a non-Markovian channel (M + M = nM) and mixtures of non-Markovian channels that result in a Markovian channel (nM + nM = M). For the first case, we consider mixtures of Markovian single qubit Pauli channels; for the second case, we consider mixtures of Non-Markovian single qubit depolarising channels, which is a special case of the single qubit Pauli channel. We show that efficient circuits, which account for the topology of currently available devices and current levels of decoherence, can be constructed by heuristic approaches that reduce the number of CNOT gates used in our circuit. We also present a strategy for regularising the process matrix so that the process tomography yields a completely positive and trace-preserving (CPTP) channel.Key pointsThis work simulates the convex mixtures of single qubit Markovian and non-Markovian quantum channels on NISQ devices provided by the IMBQE.The circuits used to implement the channels take into account the topolgy of the quantum device used as well as the number of CNOT gates used.We present a strategy for regularising the process matrix to ensure the quantum process tomography yields a CPTP channel. Something that is not correctly implemented in Qiskit.A method is outlined for finding mixtures of non-Markovian depolarising channels that yield a Markovian depolarising channel. It is also shown that, one cannot convexly mix two Markovian depolarising channels that leads to a non-Markovian depolarising channel.

In this article we conceive of the Open Master, a new form of Transnational Education, as a means of enhancing accessibility to specialist expertise in Quantum Technology. Through participatory action research conducted during the setup and operation of a pan-European pilot project, the QTEdu Open Master (QTOM), we examine the viability of this educational model to offer flexible learning opportunities to STEM Master’s students through the setup and year-long operation of an online course exchange platform. A crucial lynchpin in the Open Master model are the mechanisms of local accreditation available for the awarding of credit, which we divide into distinct course types varying in formality and applicability. Furthermore, we have elucidated the strategies taken by staff to successfully implement the Open Master and benefit from its transformative value, building long-lasting communities within and between faculty, and scaling up educational offerings across Europe. With this research, we reflect on a possible future for QT Education.

High-impedance resonators are a promising contender for realizing long-distance entangling gates between spin qubits. Often, the fabrication of spin qubits relies on the use of gate dielectrics which are detrimental to the quality of the resonator. Here, we investigate loss mechanisms of high-impedance NbTiN resonators in the vicinity of thermally grown SiO2 and Al2O3 fabricated by atomic layer deposition. We benchmark the resonator performance in elevated magnetic fields and at elevated temperatures and find that the internal quality factors are limited by the coupling between the resonator and two-level systems of the employed oxides. Nonetheless, the internal quality factors of high-impedance resonators exceed 103 in all investigated oxide configurations which implies that the dielectric configuration would not limit the performance of resonators integrated in a spin-qubit device. Because these oxides are commonly used for spin qubit device fabrication, our results allow for straightforward integration of high-impedance resonators into spin-based quantum processors. Hence, these experiments pave the way for large-scale, spin-based quantum computers.

Quantum authentication is a fundamental first step that ensures secure quantum communication. Although various quantum authentication methods have been proposed recently, their implementation efficiency is limited. This paper proposes a key-controlled maximally mixed quantum state encryption (MMQSE) method using only a single qubit, unitary operation, minimized quantum transmissions, and a single qubit measurement, which improves implementation feasibility and operation efficiency. We applied it to representative quantum authentication applications, namely, quantum identity and message authentication. The security of our authentication schemes was verified by analyzing the relationship between the integral ratio of Uhlmann’s fidelity and probability of successful eavesdropping. Moreover, we demonstrate the higher authentication efficiency of the proposed scheme in a real quantum-channel noise environment. The upper bound of the valid noise rate was quantified using the integral ratio of Uhlmann’s fidelity in a noise environment. Finally, the optimal number of authentication sequences was estimated.

A scheme for measuring microwave (MW) electric (E) fields is proposed based on bichromatic electromagnetically induced transparency (EIT) in Rydberg atoms. A bichromatic control field drives the excited state transition, whose absorption shows three EIT windows. When a MW field drives the Rydberg transition, the EIT windows split and six transmission peaks appear. It is interesting to find that the peak-to-peak distance of transmission spectrum is sensitive to the MW field strength, which can be used to measure MW E-field. Simulation results show that the spectral resolution could be increased by about 4 times, and the minimum detectable strength of the MW E-field may be improved by about 3 times compared with the common EIT scheme. After the Doppler averaging, the minimum detectable MW E-field strength is about 5 times larger than that without Doppler effect. Also, we investigate other effects on the sensitivity of the system.

Variational quantum machine learning algorithms have become the focus of recent research on how to utilize near-term quantum devices for machine learning tasks. They are considered suitable for this as the circuits that are run can be tailored to the device, and a big part of the computation is delegated to the classical optimizer. It has also been hypothesized that they may be more robust to hardware noise than conventional algorithms due to their hybrid nature. However, the effect of training quantum machine learning models under the influence of hardware-induced noise has not yet been extensively studied. In this work, we address this question for a specific type of learning, namely variational reinforcement learning, by studying its performance in the presence of various noise sources: shot noise, coherent and incoherent errors. We analytically and empirically investigate how the presence of noise during training and evaluation of variational quantum reinforcement learning algorithms affect the performance of the agents and robustness of the learned policies. Furthermore, we provide a method to reduce the number of measurements required to train Q-learning agents, using the inherent structure of the algorithm.