QUROPE Quantum Information Processing and Communication in Europe

Exceptional-point (EP) sensors are characterized by a square-root resonant frequency bifurcation in response to an external perturbation. This has lead numerous suggestions for using these systems for sensing applications. However, there is an open debate as to whether or not this sensitivity advantage is negated by additional noise in the system. We show that an EP sensor's imprecision in measuring a generalized force is independent of its operating point's proximity to the EP. That is because frequency noises of fundamental origin in the sensor -- due to quantum and thermal fluctuations -- increase in a manner that exactly cancels the benefit of increased resonant frequency sensitivity near the EP. So the benefit of EP sensors is limited to the regime where sensing is limited by technical noises. Finally, we outline an EP sensor with phase-sensitive gain that does have an advantage even if limited by fundamental noises.

An emergent and promising tensor-network-based impurity solver is to represent the path integral as a matrix product state, where the bath is analytically integrated out using Feynman-Vernon influence functional. Here we present an approach to calculate the equilibrium impurity spectral function based on the recently proposed Grassmann time-evolving matrix product operators method. The central idea is to perform a quench from a separable impurity-bath initial state as in the non-equilibrium scenario. The retarded Green's function $G(t+t_0, t'+t_0)$ is then calculated after an equilibration time $t_0$ such that the impurity and bath are approximately in thermal equilibrium. There are two major advantages of this method. First, since we focus on real-time dynamics, we do not need to perform the numerically ill-posed analytic continuation in the continuous-time quantum Monte Carlo case that relies on imaginary-time evolution. Second, the entanglement growth of the matrix product states in real-time calculations is observed to be much slower than that in imaginary-time calculations, leading to a significant improvement in numerical efficiency. The accuracy of this method is demonstrated in the single-orbital Anderson impurity model and benchmarked against the continuous-time quantum Monte Carlo method.

The dynamics of the interaction between an atom of three levels interacting with a quantized field of two modes in a cavity is studied within the rotating wave approximation by taking into account experimental values of the accessible hyperfine levels of alkaline atoms. An equal detuning is considered to determine the matter-field entanglement, the statistical properties of the photons, and the occupation probabilities of the atom. For a large detuning or weak dipolar strength appears, the Raman condition, that is, the suppression of one of his atomic transitions. Analytical expression for the time evolution operator allows to have also explicit closed expressions for the field and matter observables.

Optical tweezers constitute pivotal tools in Atomic, Molecular, and Optical(AMO) physics, facilitating precise trapping and manipulation of individual atoms and molecules. This process affords the capability to generate desired geometries in both one-dimensional and two-dimensional spaces, while also enabling real-time reconfiguration of atoms. Due to stochastic defects in these tweezers, which cause catastrophic performance degradation especially in quantum computations, it is essential to rearrange the tweezers quickly and accurately. Our study introduces a machine learning approach that uses the Proximal Policy Optimization model to optimize this rearrangement process. This method focuses on efficiently solving the shortest path problem, ensuring the formation of defect-free tweezer arrays. By implementing machine learning, we can calculate optimal motion paths under various conditions, resulting in promising results in model learning. This advancement presents new opportunities in tweezer array rearrangement, potentially boosting the efficiency and precision of quantum computing research.

The variational quantum eigensolver (VQE) framework has been instrumental in advancing near-term quantum algorithms. However, parameter optimization remains a significant bottleneck for VQE, requiring a large number of measurements for successful algorithm execution. In this paper, we propose sequential optimization with approximate parabola (SOAP) as an efficient and robust optimizer specifically designed for parameter optimization of the unitary coupled-cluster ansatz on quantum computers. SOAP leverages sequential optimization and approximates the energy landscape as quadratic functions, minimizing the number of energy evaluations required to optimize each parameter. To capture parameter correlations, SOAP incorporates the average direction from the previous iteration into the optimization direction set. Numerical benchmark studies on molecular systems demonstrate that SOAP achieves significantly faster convergence and greater robustness to noise compared to traditional optimization methods. Furthermore, numerical simulations up to 20 qubits reveal that SOAP scales well with the number of parameters in the ansatz. The exceptional performance of SOAP is further validated through experiments on a superconducting quantum computer using a 2-qubit model system.

Phase-sensitive amplification of squeezed states is a technique to mitigate high detection loss, e.g. at 2-micrometre wavelengths. Our analytical model of amplified squeezed states expands on the effect of phase noise and derives two practical parameters: the effective measurable squeezing and the effective detection efficiency. A case study including realistic parameters demonstrates the benefit of phase-sensitive amplification. We identified the phase noise in the optical parametric amplifier (OPA) minimally affects the squeezing level, enabling increased gain of the OPA. This scheme is compatible with proposed gravitational-wave detectors and consistent with applications in quantum systems that are degraded by output coupling loss in optical waveguides.

We present an experimental demonstration of balanced heterodyne readout that circumvents the 3 dB heterodyne signal-to-noise penalty on a dual Michelson sensor. Our readout obtains both amplitude and phase quadrature information simultaneously. We also employ a high-frequency spectrally entangled, two-mode squeezed state to show further signal-to-noise improvement of an injected audio-band signal. We achieve a quantum enhancement of 3.5 dB, consistent with our experimental efficiencies and dephasing. This technique is applicable for quantum-limited high-precision experiments, with application to searches for quantum gravity, gravitational wave detection and wavelength-multiplexed quantum communication.

The interaction of light and matter at the single-photon level is of central importance in various fields of physics, including, e.g., condensed matter physics, astronomy, quantum optics, and quantum information. Amplification of such quantum light-matter interaction can be highly beneficial to, e.g., improve device performance, explore novel phenomena, and understand fundamental physics, and has therefore been a long-standing goal. Furthermore, simulation of light-matter interaction in the regime of ultrastrong coupling, where the interaction strength is comparable to the bare frequencies of the uncoupled systems, has also become a hot research topic, and considerable progress has been made both theoretically and experimentally in the past decade. In this review, we provide a detailed introduction of recent advances in amplification of quantum light-matter interaction and simulation of ultrastrong light-matter interaction, particularly in cavity and circuit quantum electrodynamics and in cavity optomechanics.

In the standard von Neumann interaction used in Quantum measurements, the chosen observable to which the environment (apparatus) entangles is exactly reproduced in the state of the environment, thereby decohering the quantum system in the eigenbasis of the observable. We relax this by allowing for imperfect measurements whereby the environment evolves to a state that approximately, but not exactly, reflects the state of the system. In this scheme it is possible to attain approximate decoherence of conjugate quantities that resembles classical physics, which we demonstrate using an example.

Quantum metrology has been shown to surpass classical limits of correlation, resolution, and sensitivity. It has been introduced to interferometric Radar schemes, with intriguing preliminary results. Even quantum-inspired detection of classical signals may be advantageous in specific use cases. Following ideas demonstrated so far only in the optical domain, where practically no thermal background photons exist, we realize room-temperature microwave frequency super-resolved phase measurements with trillions of photons, while saturating the Cramer-Rao bound of sensitivity. We experimentally estimate the interferometric phase using the expectation value of the Parity operator by two methods. We achieve super-resolution up to 1200 times better than the wavelength with 25ns integration time and 56dB SNR.

Random matrix theory is a powerful tool for understanding spectral correlations inherent in quantum chaotic systems. Despite diverse applications of non-Hermitian random matrix theory, the role of symmetry remains to be fully established. Here, we comprehensively investigate the impact of symmetry on the level statistics around the spectral origin -- hard-edge statistics -- and complete the classification of spectral statistics in all the 38 symmetry classes of non-Hermitian random matrices. Within this classification, we discern 28 symmetry classes characterized by distinct hard-edge statistics from the level statistics in the bulk of spectra, which are further categorized into two groups, namely the Altland-Zirnbauer$_0$ classification and beyond. We introduce and elucidate quantitative measures capturing the universal hard-edge statistics for all the symmetry classes. Furthermore, through extensive numerical calculations, we study various open quantum systems in different symmetry classes, including quadratic and many-body Lindbladians, as well as non-Hermitian Hamiltonians. We show that these systems manifest the same hard-edge statistics as random matrices and that their ensemble-average spectral distributions around the origin exhibit emergent symmetry conforming to the random-matrix behavior. Our results establish a comprehensive understanding of non-Hermitian random matrix theory and are useful in detecting quantum chaos or its absence in open quantum systems.

Unital qubit Schwarz maps interpolate between positive and completely positive maps. It is shown that relaxation rates of qubit semigroups of unital maps enjoying Schwarz property satisfy the universal constraint which provides a modification of the corresponding constraint known for completely positive semigroups. As an illustration we consider two paradigmatic qubit semigroups: Pauli dynamical maps and phase covariant dynamics. This result has two interesting implications: it provides a universal constraint for the spectra of qubit Schwarz maps and gives rise to a necessary condition for a Schwarz qubit map to be Markovian.

Antimicrobial peptides (AMPs) play important roles in cancer, autoimmune diseases, and aging. A critical aspect of AMP functionality is their targeted interaction with pathogen membranes, which often possess altered lipid compositions. Designing AMPs with enhanced therapeutic properties relies on a nuanced understanding of these interactions, which are believed to trigger a rearrangement of these peptides from random coil to alpha-helical conformations, essential for their lytic action. Traditional supercomputing has consistently encountered difficulties in accurately modeling these structural changes, especially within membrane environments, thereby opening an opportunity for more advanced approaches. This study extends an existing quantum computing algorithm to address the complexities of antimicrobial peptide interactions at interfaces. Our approach enables the prediction of the optimal conformation of peptides located in the transition region between hydrophilic and hydrophobic phases, akin to lipid membranes. The new method has been applied to model the structure of three 10-amino-acid-long peptides, each exhibiting hydrophobic, hydrophilic, or amphipathic properties in different media and at interfaces between solvents of different polarity. Notably, our approach does not demand a higher number of qubits compared to simulations in homogeneous media, making it more feasible with current quantum computing resources. Despite existing limitations in computational power and qubit accessibility, our findings demonstrate the significant potential of quantum computing in accurately characterizing complex biomolecular processes, particularly the folding of AMPs at membrane models. This research paves the way for future advances in quantum computing to enhance the accuracy and applicability of biomolecular simulations.

Efficient optical quantum memories are a milestone required for several quantum technologies including repeater-based quantum key distribution and on-demand multi-photon generation. We present an efficiency optimization of an optical electromagnetically induced transparency (EIT) memory experiment in a warm cesium vapor using a genetic algorithm and analyze the resulting waveforms. The control pulse is represented either as a Gaussian or free-form pulse, and the results from the optimization are compared. We see an improvement factor of 3(7)\% when using optimized free-form pulses. By limiting the allowed pulse energy in a solution, we show an energy-based optimization giving a 30% reduction in energy, with minimal efficiency loss.

Ultracold molecules confined in optical lattices or tweezer traps can be used to process quantum information and simulate the behaviour of many-body quantum systems. Molecules offer several advantages for these applications. They have a large set of stable states with strong transitions between them and long coherence times. They can be prepared in a chosen state with high fidelity, and the state populations can be measured efficiently. They have controllable long-range dipole-dipole interactions that can be used to entangle pairs of molecules and generate interesting many-body states. We review the advances that have been made and the challenges still to overcome, and describe the new ideas that will unlock the full potential of the field.

Nonreciprocity, arising from the breaking of time-reversal symmetry, has become a fundamental tool in diverse quantum technology applications. It enables directional flow of signals and efficient noise suppression, constituting a key element in the architecture of current quantum information and computing systems. Here we explore its potential in optimizing the charging dynamics of a quantum battery. By introducing nonreciprocity through reservoir engineering during the charging process, we induce a directed energy flow from the quantum charger to the battery, resulting in a substantial increase in energy accumulation. Despite local dissipation, the nonreciprocal approach demonstrates a fourfold increase in battery energy compared to conventional charger-battery systems. We demonstrate that employing a shared reservoir can establish an optimal condition where nonreciprocity enhances charging efficiency and elevates energy storage in the battery. This effect is observed in the stationary limit and remains applicable even in overdamped coupling regimes, eliminating the need for precise temporal control over evolution parameters. Our result can be extended to a chiral network of quantum nodes, serving as a multi-cell quantum battery system to enhance storage capacity. The proposed approach is straightforward to implement using current state-of-the-art quantum circuits, both in photonics and superconducting quantum systems. In a broader context, the concept of nonreciprocal charging has significant implications for sensing, energy capture, and storage technologies or studying quantum thermodynamics.

We study mixed unitary channels generated by finite subgroups of the group of all unitary operators in a Hilbert space. Based on the majorization theory we introduce techniques allowing to calculate different characteristics of output states of channels. A class of channels has been allocated for which the use of entangled states doesn't give any advantage under taking supremum and infimum for output characteristics of channels. In particular, $l_p$-norms are multiplicative and the minimal entropy is additive with respect to taking tensor products of channels. As an important application of the obtained results the classical capacity of channel is calculated in the evident form. We compare our techniques with the informational characteristics of Boson quantum channels.

The coupling of an isolated quantum state to a continuum is typically associated with decoherence and decreased lifetime. Here, we demonstrate that Rydberg macrodimers, weakly bound pairs of Rydberg atoms, can overcome this dissipative mechanism and instead form bound states with the continuum of free motional states. This is enabled by the unique combination of extraordinarily slow vibrational motion in the molecular state and the optical coupling to a non-interacting continuum. Under conditions of strong coupling, we observe the emergence of distinct resonances and explain them within a Fano model. For atoms arranged on a lattice, we predict the strong continuum coupling to even stabilize molecules consisting of more than two atoms and find first signatures of these by observing atom loss correlations using a quantum gas microscope. Our results present an intriguing mechanism to control decoherence and bind multiatomic molecules using strong light-matter interactions.

Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with ADAPT-VQE, an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the Hessian in the state of the optimizer to better approximate the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Based on earlier work by Carlen-Maas and the second- and third-named author, we introduce the notion of intertwining curvature lower bounds for graphs and quantum Markov semigroups. This curvature notion is stronger than both Bakry-\'Emery and entropic Ricci curvature, while also computationally simpler than the latter. We verify intertwining curvature bounds in a number of examples, including finite weighted graphs and graphs with Laplacians admitting nice mapping representations, as well as generalized dephasing semigroups and quantum Markov semigroups whose generators are formed by commuting jump operators. By improving on the best-known bounds for entropic curvature of depolarizing semigroups, we demonstrate that there can be a gap between the optimal intertwining and entropic curvature bound. In the case of qubits, this improved entropic curvature bound implies the modified logarithmic Sobolev inequality with optimal constant.