International Journal of Quantum Information

The recent advances in Machine Learning hold great promises for the field of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, which is typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system the agent is interacting with, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system and only some parameters of the evolution or of the initial state are unknown. We present here a general Machine Learning technique that can optimize the precision of quantum sensors, and in doing so it exploits the knowledge we have on the system. We have developed a Python package to automate a broad class of optimizations that can be found in the tasks of quantum parameter estimation, quantum metrology and quantum hypothesis testing. What the agent is learning here is an optimal adaptive strategy, that, on the basis of the previous outcomes, decides the next measurements to perform. It works both for Bayesian estimation and for frequentist estimation. The user is required to implement the physics of the system to be studied and state which parameters in the experiment are controllable and which are unknowns. The functions of the library allow then to easily train a neural network agent for optimizing the precision of the sensor, by simulating the experiment. We have explored some applications of this technique to magnetometry on NV centers (both DC and AC), to state discrimination in quantum optics and to phase estimation. So far, we were able to certify better results than the current state-of-the-art controls for many examples. The Machine Learning technique developed here can be applied in all those scenarios where the quantum system is well characterized and relatively simple and small. In these cases, we can squeeze every last bit of information from a quantum sensor by controlling it with a neural network appropriately trained.

We characterize quantum correlations encoded in a three-flavor oscillating neutrino system by taking into account CP violation. By means of the complete complementarity relations (CCR), we study the trade-off of predictability, local coherence and nonlocal correlations by using relevant parameters given by neutrino oscillation experiments. Although the CCR describe very well the contributions associated to bipartite correlations, we explore the possible presence of genuine tripartite correlations, by exploiting polygamy relations. We also provide some observations about the nonlocal advantage of quantum coherence, a mazy quantum correlation characterizing the most valuable, nonlocal, portion of quantum coherence in a multi-partite system.

We consider a recently introduced framework for the description of memory effects based on quantum state distinguishability quantifiers, in which entropic quantifiers can be included. After briefly presenting the approach, we validate it considering the performance of different quantifiers in the characterization of the reduced dynamics of a two-level system undergoing decoherence. We investigate the different behaviors of these quantifiers in the dependence on physical features of the model, such as environmental temperature and coupling strength. It appears that the performance of the different quantifiers conveys the same physical information, though with different sensitivities, thus supporting robustness of the approach.

We address the problem of coherent state discrimination in the presence of phase diffusion. We investigate the role of the HYbrid Near-Optimum Receiver (HYNORE) we proposed in [J. Opt. Soc. Am. B 40 (2023) 705] in the task of mitigating the noise impact. We prove the HYNORE to be a robust receiver, outperforming the Displacement Photon-Number-Resolving (DPNR) receiver and beating the standard quantum limit in particular regimes. We introduce the maximum tolerable phase noise ${\sigma }_{\mathrm{\text{max}}}$ as a figure of merit for the receiver robustness and show that HYNORE increases its value with respect to the DPNR receiver.

Nanostructured Tin oxide powders, prepared by chemical precipitation method, exhibit interesting structural and optical properties at nanoscale dimensions. Structural characterization confirmed the formation of pure nanostructured SnO₂ powders with average crystalline grain size of 54.4nm calculated with relative uncertainty of 1.62%. The optical properties of nanoSnO₂ powder performed by using transmittance and photoluminescence measurements reveal high transparency and multi-photon emissions in visible and ultraviolet regions. The different photon emissions are attributed to radiative transitions via oxygen vacancies, Sn interstitials, Sn substitutional and dangling bonds. The lower wavelength value corresponded to the absorption edge peak indicating the good quantum confinement of nanostructured SnO₂ powder. The larger optical band gap, calculated through transmittance measurements, confirms the quantum confinement effects. The presence of quantum confinement and native point defects in nanostructured SnO₂ powders can contribute to enhancing photon emissions process. This result may offer inherent advantages over conventional materials for photonic quantum information technology. The sustained photon emission at room temperature, indicates that SnO₂ nanoparticles could be useful in room temperature quantum computing and can maintain the delicate quantum states required for computation at higher temperatures.

Cornerstones of the Cellular Automaton Interpretation of Quantum Mechanics are its ontological states that evolve by permutations, in this way never creating would-be quantum mechanical superposition states. We review and illustrate this with a classical Ising spin chain. It is shown that it can be related to the Weyl equation in the continuum limit. Yet, the model of discrete spins or bits unavoidably becomes a model of qubits by generating superpositions, if only slightly deformed. We study modifications of its signal velocity which, however, do not relate to mass terms. To incorporate the latter, we consider the Dirac equation in 1+1 dimensions and sketch an underlying discrete deterministic “necklace of necklaces” automaton that qualifies as ontological.

This paper systematically reviews the historical development trajectory and current situation of quantum financial theory research through an in-depth review of relevant literature. This paper summarizes the history of the concept of quantum finance, elaborates on the development stages of quantum finance, and explains the contributions of representative work in different periods in various aspects, such as theoretical frameworks, mathematical models, and algorithmic tools, which is of great significance to the research path of quantum finance. In addition, the paper introduces in detail the research progress of domestic and foreign body problems, such as quantum algorithm optimization of investment portfolios, quantitative pricing of derivatives, quantum neural networks, etc., laying a good foundation for future in-depth study of this interdisciplinary subject. Overall, this paper systematically reviews the development history of quantum finance research, provides readers with a macro understanding framework by sorting out the progress in different research fields, and has significant reference value for interdisciplinary exchanges.

We study the long distance $(0,1)$-excitation state restoring in the linear open chain governed by the XX-Hamiltonian. We show that restoring the 1-order coherence matrix results in restoring the 1-excitation block of the 0-order coherence matrix, so that only one 0-excitation element of the density matrix remains unrestored. Such restoring also scales the concurrence between any two qubits of the transferred state, the scaling factor is defined by the Hamiltonian and doesn’t depend on the initial sender’s state. Sender–Receiver entanglement is also studied via the PPT criterion.

In this paper, we present a simple algorithm for the explicit construction of $n$-particle harmonic oscillator states simultaneously belonging to irreducible representations of $Sp(2,ℝ)$ (or $SU(1,1)$) and $O(n)$. For degenerate representations, the construction can be done using generating functions or hyperspherical harmonics. The cases with $n=2$ and $n=3$ are investigated at greater length. The analysis is extended to nondegenerate states, although the states are not so easily obtained as in the degenerate case. Finally, we briefly touch on the $O(2)$ structure of Bell states.

In this paper, we consider a quantum battery, namely a miniaturized device able to store energy and release it on-demand. In the so-called Dicke configuration, it is made of $N$ two-level systems embedded into a cavity which plays the role of charger. In this context, we compare two different situations, namely: the resonant regime, where the energy of the two-level systems is the same as the one of the photons trapped in the cavity, and the off-resonant regime, where the photons are way more energetic. We observe that, while the energy stored per two-level system is comparable in the two cases, the average charging power, despite showing the same asymptotic superextensive scaling, is strongly suppressed in the latter. This analysis will help to orient future experimental implementations of these devices.

The development of quantum technologies based on optical platforms demands an adequate engineering of the employed quantum states of light. In this framework, we show that superthermal states generated by sending a coherent beam through a sequence of two diffusers in cascade represent good candidates for different applications. Here, we summarize some recent results achieved exploiting this kind of light and show that it can be used to realize a differential ghost-imaging scheme and to encode information in an underwater communication protocol based on mesoscopic twin-beam states and photon-number-resolving detectors. The good quality of the experimental results suggests further investigations on the possible application of this kind of light to different contexts.

Quantum counting is a key quantum algorithm that aims to determine the number of marked elements in a database. This algorithm is based on the quantum phase estimation algorithm and uses the evolution operator of Grover’s algorithm because its nontrivial eigenvalues are dependent on the number of marked elements. Since Grover’s algorithm can be viewed as a quantum walk on a complete graph, a natural way to extend quantum counting is to use the evolution operator of quantum-walk-based search on noncomplete graphs instead of Grover’s operator. In this paper, we explore this extension by analyzing the coined quantum walk on the complete bipartite graph with an arbitrary number of marked vertices. We show that some eigenvalues of the evolution operator depend on the number of marked vertices and using this fact we show that the quantum phase estimation can be used to obtain the number of marked vertices. The time complexity for estimating the number of marked vertices in the bipartite graph with our algorithm equates closely with that of the original quantum counting algorithm.

As an important branch of quantum-secure multiparty computation, quantum-secure multiparty summation enables participants to complete the summation of their inputs without revealing their own secrets. In this paper, a new multiparty quantum secure summation protocol based on locally indistinguishable orthogonal product (LIOP) states is proposed. Leveraging the properties of LIOP states, participants can successfully accomplish the summation of their secrets with the assistance of a semi-honest third party (TP). The present protocol is shown to effectively resist certain existing attacks. Furthermore, complex operations and entanglement resources are not required in the proposed protocol. We anticipate that these results will contribute positively to the future development of quantum-secure multiparty computation.

Linear systems of equations lie at the heart of numerous scientific and engineering challenges. In cutting-edge arena like artificial intelligence, machine learning and neuro-computation, these systems serve as a fundamental tool for mathematical modeling. Classical algorithms for solving linear systems have been extensively developed and forms the backbone of diverse applications across various scientific disciplines. While classical algorithms exist for solving linear systems, they often encounter limitations termed “NP-completeness” as data complexity increases. The emerging field of quantum computing offers a revolutionary approach to deal with these kinds of problems. The Harrow–Hassidim–Lloyd (HHL) algorithm tackles these challenges and opens new avenues for research. This study delves into the contemporary effectiveness of the HHL algorithm to address systems of linear equations. By examining recent research in quantum machine learning, we aim to assess the HHL algorithm’s potential to revolutionize the process of optimizing hyperparameters for machine learning models, resulting in increased efficiency and cost savings. This paper meticulously analyzes the HHL algorithm and explores its evolution from conception to the latest advancements. A comprehensive examination of the HHL algorithm, including its evolution over time, is thoroughly explored. The investigation delves into the potential challenges and limitations that might hinder the practical deployment of the HHL algorithm. Identifying these roadblocks will pave the way for future research and development efforts.