Quantum Physics Experiments Research Articles

Graph-theoretic insights on the constructability of complex entangled states

The most efficient automated way to construct a large class of quantum photonic experiments is via abstract representation of graphs with certain properties. While new directions were explored using Artificial intelligence and SAT solvers to find such graphs, it becomes computationally infeasible to do so as the size of the graph increases. So, we take an analytical approach and introduce the technique of local sparsification on experiment graphs, using which we answer a crucial open question in experimental quantum optics, namely whether certain complex entangled quantum states can be constructed. This provides us with more insights into quantum resource theory, the limitation of specific quantum photonic systems and initiates the use of graph-theoretic techniques for designing quantum physics experiments.

CIRCUS: an autonomous control system for antimatter, atomic and quantum physics experiments

A powerful and robust control system is a crucial, often neglected, pillar of any modern, complex physics experiment that requires the management of a multitude of different devices and their precise time synchronisation. The AEḡIS collaboration presents CIRCUS, a novel, autonomous control system optimised for time-critical experiments such as those at CERN’s Antiproton Decelerator and, more broadly, in atomic and quantum physics research. Its setup is based on Sinara/ARTIQ and TALOS, integrating the ALPACA analysis pipeline, the last two developed entirely in AEḡIS. It is suitable for strict synchronicity requirements and repeatable, automated operation of experiments, culminating in autonomous parameter optimisation via feedback from real-time data analysis. CIRCUS has been successfully deployed and tested in AEḡIS; being experiment-agnostic and released open-source, other experiments can leverage its capabilities.

Laser linewidth measurement using beat-note technique

Lasers play a crucial role in quantum and atomic physics experiments. To prepare atoms in a desired quantum state, these lasers must possess specific properties, including tunability and a narrow spectral linewidth. In this study, we construct and employ an external cavity diode laser to prepare cold rubidium atoms. We control the laser’s stability using an electronic system and evaluate its performance by measuring the spectral linewidth through the beat signal technique. The measurement indicates a linewidth narrower than 1 MHz, which makes the laser suitable for subsequent experiments. Finally, we adjust the laser frequency to match the rubidium transition frequency between states 5S 1/2, F = 2 ↔ 5P 3/2, F′ = 2, and successfully utilizing the laser to prepare cold rubidium atoms in a magneto-optical trap.

Kinetic Modeling of a Poly(N-vinylcaprolactam-co-glycidyl methacrylate) Microgel Synthesis: A Hybrid In Silico and Experimental Approach

Synthesizing microgels with new functionalities is vital for applications in various disciplines; yet microgel synthesis kinetics are mostly unknown. We present a bottom-up approach to model the synthesis of N-vinylcaprolactam-based microgels functionalized with glycidyl methacrylate. The existing synthesis model requires parameter values for unknown reaction rates, which we estimate by a hybrid approach based on quantum chemical calculations and experimental data. Using quantum mechanics, we compute propagation rate constants and enthalpies of the underlying polymerization reactions. We estimate 7 out of 21 reaction parameter values using the reaction calorimetry measurements and a mechanistic process model. Our hybrid approach averages a coefficient of determination of 0.97 for the enthalpy transfer rate over time during microgel synthesis. Our approach illustrates that quantum chemistry methods and physical experiments can be integrated into models toward better understanding and designing of pVCL/GMA microgel synthesis processes.

Design of quantum optical experiments with logic artificial intelligence

Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and solved by checking their satisfiability (SAT). In contrast to machine learning approaches where the results can be approximations or local minima, Logic AI delivers formal and mathematically exact solutions to those problems. In this work, we propose the use of logic AI for the design of optical quantum experiments. We show how to map into a SAT problem the experimental preparation of an arbitrary quantum state and propose a logic-based algorithm, called Klaus, to find an interpretable representation of the photonic setup that generates it. We compare the performance of Klaus with the state-of-the-art algorithm for this purpose based on continuous optimization. We also combine both logic and numeric strategies to find that the use of logic AI significantly improves the resolution of this problem, paving the path to developing more formal-based approaches in the context of quantum physics experiments.

Learning interpretable representations of entanglement in quantum optics experiments using deep generative models

Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are the core properties of numerous future quantum technologies. The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present a deep generative model of quantum optics experiments where a variational autoencoder is trained on a dataset of quantum optics experiment setups. In a series of computational experiments, we investigate the learned representation of our quantum optics variational autoencoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that QOVAE learns an interpretable representation of quantum optics experiments and the relationship between the experiment structure and entanglement. We show QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. QOVAE can learn to generate specific entangled states and efficiently search the space of experiments that produce highly entangled quantum states. Importantly, we are able to interpret how QOVAE structures its latent space, finding curious patterns that we can explain in terms of quantum physics. The results demonstrate how we can use and understand the internal representations of deep generative models in a complex scientific domain. QOVAE and the insights from our investigations can be immediately applied to other physical systems. A variational autoencoder is trained on a dataset of quantum optics experiment configurations and learns an interpretable representation of the relationship between experiment setup and quantum entanglement. The approach can be used to explore new experiment designs with specific, highly entangled states.

Quantum algorithmic measurement

There has been recent promising experimental and theoretical evidence that quantum computational tools might enhance the precision and efficiency of physical experiments. However, a systematic treatment and comprehensive framework are missing. Here we initiate the systematic study of experimental quantum physics from the perspective of computational complexity. To this end, we define the framework of quantum algorithmic measurements (QUALMs), a hybrid of black box quantum algorithms and interactive protocols. We use the QUALM framework to study two important experimental problems in quantum many-body physics: determining whether a system’s Hamiltonian is time-independent or time-dependent, and determining the symmetry class of the dynamics of the system. We study abstractions of these problems and show for both cases that if the experimentalist can use her experimental samples coherently (in both space and time), a provable exponential speedup is achieved compared to the standard situation in which each experimental sample is accessed separately. Our work suggests that quantum computers can provide a new type of exponential advantage: exponential savings in resources in quantum experiments.

FULLY OPTICAL NETWORKS FOR QUANTUM ENCRYPTION KEY TRANSMISSION SYSTEMS

The development of experimental quantum physics in recent years has led to the fact that the abstract ideas of quantum mechanics began to find practical application for the protection of information in a rapidly developing field such as fiber-optic communication lines (FOCL). On the basis of (smart) translucent SMART mirrors, optical splitters, switches, schemes of unidirectional, counter, bidirectional and universal multiplexers and demultiplexers, and the principle of distributing quantum encryption keys between authorized users have been developed. It is shown that it is possible to improve the speed of the process of switching and multiplexing of information flows in comparison with traditional mechanical switches. The paper proposes a circuit solution for an all-optical network (All-Optical Networks – AON) with the possibility of generating and distributing quantum encryption keys between authorized users. The aim of this work is to develop a principle for constructing quantum systems for the secure distribution of encryption keys on all-optical networks between selected (4 or more) users, as well as methods for generating, transmitting and receiving these keys in real time. In this regard, there is a need to develop a switch and a multiplexer of information flows.

On-Demand, Direct Printing of Nanodiamonds at the Quantum Level.

The quantum defects in nanodiamonds, such as nitrogen‐vacancy (NV) centers, are emerging as a promising candidate for nanoscale sensing and imaging, and the controlled placement with respect to target locations is vital to their practical applications. Unfortunately, this prerequisite continues to suffer from coarse positioning accuracy, low throughput, and process complexity. Here, it is reported on direct, on‐demand electrohydrodynamic printing of nanodiamonds containing NV centers with high precision control over quantity and position. After thorough characterizations of the printing conditions, it is shown that the number of printed nanodiamonds can be controlled at will, attaining the single‐particle level precision. This printing approach, therefore, enables positioning NV center arrays with a controlled number directly on the universal substrate without any lithographic process. The approach is expected to pave the way toward new horizons not only for experimental quantum physics but also for the practical implementation of such quantum systems.

Quantum Optical Experiments Modeled by Long Short-Term Memory

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search, but is also an essential step towards the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

Through the Synergetic Union of Linguistics and Other Sciences - to New Challenges and Directions

In recent decades linguistics is less interested in the structure of language and the systemic relationships between its units, which are generally well described. At the turn of the century, we observe sharply increased interest in the problems of the relationship between language, consciousness, culture, mentality, national character and behavior. The language itself is understood as a mirror (though a crooked one) of reality, of a person, of his external and internal world. Such a broad approach supposes the impossibility of solving emerging problems as part of linguistics only; it needs the help of a whole range of related sciences about man - from psychology, anthropology, sociology, cultural studies to biology, neurosciences and quantum mechanics. Thus, we can see the interest in the integration of sciences. The purpose of this article is to conceptualize theoretically this new phenomenon and to show that modern linguistics must meet the requirements of the time and therefore it should be interdisciplinary and integrative; we tried to establish the relationship between tradition and new ideas in linguistics of the third millennium and showed that the problems arising in the language can be solved by the joint efforts of representatives of a number of sciences. The results obtained in this article allow us to put forward the issues that demand, in our view, immediate attention and show the importance of combining an objective linguistic analysis and the results of their research in semiotics, theology, psychology. These are such phenomena as 1) deep semantic processes; 2) language is a means of expressing Divine Truth; 3) language and national character. The obtained integrative knowledge can reveal non-causal relationships in the world, discover deeper laws of being, for example, the fact that consciousness is not rigidly connected with language, therefore it can hardly be researched by an associative experiment, although psycholinguists have long hoped for this. Physicist John Wheeler, taking into account experiments in quantum physics, proved that even if we just look at an object, it is changing under the influence of the gaze. How much stronger is the influence of a word? This is to be inquired in the near future as the part of an integrative approach.

Photothermal alternative to device fabrication using atomic precision advanced manufacturing techniques

The attachment of dopant precursor molecules to depassivated areas of hydrogen-terminated silicon templated with a scanning tunneling microscope (STM) has been used to create electronic devices with subnanometer precision, typically for quantum physics experiments. This process, which we call atomic precision advanced manufacturing (APAM), dopes silicon beyond the solid-solubility limit and produces electrical and optical characteristics that may also be useful for microelectronic and plasmonic applications. However, scanned probe lithography lacks the throughput required to develop more sophisticated applications. Here, we demonstrate and characterize an APAM device workflow where scanned probe lithography of the atomic layer resist has been replaced by photolithography. An ultraviolet laser is shown to locally and controllably heat silicon above the temperature required for hydrogen depassivation on a nanosecond timescale, a process resistant to under- and overexposure. STM images indicate a narrow range of energy density where the surface is both depassivated and undamaged. Modeling that accounts for photothermal heating and the subsequent hydrogen desorption kinetics suggests that the silicon surface temperatures reached in our patterning process exceed those required for hydrogen removal in temperature-programmed desorption experiments. A phosphorus-doped van der Pauw structure made by sequentially photodepassivating a predefined area and then exposing it to phosphine is found to have a similar mobility and higher carrier density compared with devices patterned by STM. Lastly, it is also demonstrated that photodepassivation and precursor exposure steps may be performed concomitantly, a potential route to enabling APAM outside of ultrahigh vacuum.

New trends in quantum machine learning (a) (a)Contribution to the Focus Issue Progress on Statistical Physics and Complexity edited by Roberta Citro, Giorgio Kaniadakis, Claudio Guarcello, Antonio Maria Scarfone and Davide Valenti.

Here we will give a perspective on new possible interplays between machine learning and quantum physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by breakthroughs in physical hardware, as well as to improve existing models or devise new learning schemes in the quantum domain. Moreover, there are lots of experiments in quantum physics that do generate incredible amounts of data and machine learning would be a great tool to analyze those and make predictions, or even control the experiment itself. On top of that, data visualization techniques and other schemes borrowed from machine learning can be of great use to theoreticians to have better intuition on the structure of complex manifolds or to make predictions on theoretical models. This new research field, named as quantum machine learning, is very rapidly growing since it is expected to provide huge advantages over its classical counterpart and deeper investigations are timely needed since they can be already tested on the already commercially available quantum machines.

Reflections on probabilistic compared to quantum computational devices

ABSTRACT In this article, we underline and compare different quantum and deterministic models of computation by means of a probabilistic point of view. The main models used for this are deterministic, probabilistic and quantum Turing machines (DTM, PTM and QTM, respectively). Then we suggest that extending PTM with only part of quantum features is not useful to implement real quantum supremacy if we don’t incorporate them fully. This is done reviewing some notorious quantum physical experiments and quantum algorithms, showing that generally PTM does not fit for them, also if in some particular cases it fits. Here we compare probabilistic and quantum models of computation to underline some common features and differences.

Cost of Quantum Entanglement Simplified.

Quantum entanglement is a key physical resource in quantum information processing that allows for performing basic quantum tasks such as teleportation and quantum key distribution, which are impossible in the classical world. Ever since the rise of quantum information theory, it has been an open problem to quantify entanglement in an information-theoretically meaningful way. In particular, every previously defined entanglement measure bearing a precise information-theoretic meaning is not known to be efficiently computable, or if it is efficiently computable, then it is not known to have a precise information-theoretic meaning. In this Letter, we meet this challenge by introducing an entanglement measure that has a precise information-theoretic meaning as the exact cost required to prepare an entangled state when two distant parties are allowed to perform quantum operations that completely preserve the positivity of the partial transpose. Additionally, this entanglement measure is efficiently computable by means of a semidefinite program, and it bears a number of useful properties such as additivity and faithfulness. Our results bring key insights into the fundamental entanglement structure of arbitrary quantum states, and they can be used directly to assess and quantify the entanglement produced in quantum-physical experiments.

Enhanced precision bound of low-temperature quantum thermometry via dynamical control

High-precision low-temperature thermometry is a challenge for experimental quantum physics and quantum sensing. Here we consider a thermometer modeled by a dynamically-controlled multilevel quantum probe in contact with a bath. Dynamical control in the form of periodic modulation of the energy-level spacings of the quantum probe can dramatically increase the maximum accuracy bound of low-temperatures estimation, by maximizing the relevant quantum Fisher information. As opposed to the diverging relative error bound at low temperatures in conventional quantum thermometry, periodic modulation of the probe allows for low-temperature thermometry with temperature-independent relative error bound. The proposed approach may find diverse applications related to precise probing of the temperature of many-body quantum systems in condensed matter and ultracold gases, as well as in different branches of quantum metrology beyond thermometry, for example in precise probing of different Hamiltonian parameters in many-body quantum critical systems.

Quantum algorithm for simulating an experiment: Light interference from single ions and their mirror images

We widen the range of applications for quantum computing by introducing digital quantum simulation methods for coherent light-matter interactions: We simulate an experiment where the emitted light from a single ion was interfering with its mirror image [Eschner et. al., Nature 431, 495 (2001)]. Using the quantum simulation software q1tsim we accurately reproduce the interference pattern which had been observed experimentally and also show the effect of the mirror position on the spontaneous emission rate of the ion. In order to minimize the number of required qubits we implement a qubit-reinitialization technique. We show that a digital quantum simulation of complex experiments in atomic and quantum physics is feasible with no more than seven qubits, a setting which is well within reach for advanced quantum computing platforms.

Quantifying quantum reference frames in composed systems: Local, global, and mutual asymmetries

The Page-Wootters mechanism questioned the fundamental nature of time in quantum physics. The mechanism explored the notion that a given physical quantity is always defined and measured relative to a reference frame, in general, not explained in the theoretical description of quantum physical experiments. Recently, the resource theory of asymmetry deals explicitly with what are the physical conditions for a quantum system to serve as a good reference frame. Nonetheless, to quantify a quantum reference frame in relation to another one it is a important task to establish an internal description of quantum theory, i.e., without the need of a classical reference frame. In this work we address this issue by the concept of mutual asymmetry and use this machinery in the Page-Wootters mechanism by identifying the concept of mutual asymmetry as mutual or internal coherence. To do so, the notion of quantum coherence in relation of a quantum reference frame is revisited and a quantifier is proposed in this scenario. Also, this open space to investigate the link of internal coherence and correlations, as proposed by Page and Wootters, under a resource theory approach.

Evaluating probabilistic programming languages for simulating quantum correlations.

This article explores how probabilistic programming can be used to simulate quantum correlations in an EPR experimental setting. Probabilistic programs are based on standard probability which cannot produce quantum correlations. In order to address this limitation, a hypergraph formalism was programmed which both expresses the measurement contexts of the EPR experimental design as well as associated constraints. Four contemporary open source probabilistic programming frameworks were used to simulate an EPR experiment in order to shed light on their relative effectiveness from both qualitative and quantitative dimensions. We found that all four probabilistic languages successfully simulated quantum correlations. Detailed analysis revealed that no language was clearly superior across all dimensions, however, the comparison does highlight aspects that can be considered when using probabilistic programs to simulate experiments in quantum physics.

Nanobob: a CubeSat mission concept for quantum communication experiments in an uplink configuration

We present a ground-to-space quantum key distribution (QKD) mission concept and the accompanying feasibility study for the development of the associated low earth orbit nanosatellite payload. The quantum information is carried by single photons with the binary codes represented by polarization states of the photons. Distribution of entangled photons between the ground and the satellite can be used to certify the quantum nature of the link: a guarantee that no eavesdropping can take place. By placing the entangled photon source on the ground, the space segments contains “only” the less complex detection system, enabling its implementation in a compact enclosure, compatible with the 12U CubeSat standard ({sim}12~mbox{dm}^{3}). This reduces the overall cost of the project, making it an ideal choice as a pathfinder for future European quantum communication satellite missions. The space segment is also more versatile than one that contains the source since it is compatible with a multiple of QKD protocols (not restricted to entangled photon schemes) and can be used in quantum physics experiments, such as the investigation of entanglement decoherence. Other possible experiments include atmospheric transmission/turbulence characterization, dark area mapping, fine pointing and tracking, and accurate clock synchronization; all crucial for future global scale quantum communication efforts.