QUROPE Quantum Information Processing and Communication in Europe

Spin anticoherent states acquired recently a lot of attention as the most "quantum" states. Some coherent and anticoherent spin states are known as optimal quantum rotosensors. In this work we introduce a measure of quantumness for orthonormal bases of spin states, determined by the average anticoherence of individual vectors and the Wehrl entropy. In this way we identify the most coherent and most quantum states, which lead to orthogonal measurements of extreme quantumness. Their symmetries can be revealed using the Majorana stellar representation, which provides an intuitive geometrical representation of a pure state by points on a sphere. Results obtained lead to maximally (minimally) entangled bases in the $2j+1$ dimensional symmetric subspace of the $2^{2j}$ dimensional space of states of multipartite systems composed of $2j$ qubits. Some bases found are iso-coherent as they consist of all states of the same degree of spin-coherence.

We demonstrate the simultaneous estimation of signal frequency and amplitude by a single quantum sensor in a single experimental shot. Sweeping the qubit splitting linearly across a span of frequencies induces a non-adiabatic Landau-Zener transition as the qubit crosses resonance. The signal frequency determines the time of the transition, and the amplitude its extent. Continuous weak measurement of this unitary evolution informs a parameter estimator retrieving precision measurements of frequency and amplitude. Implemented on radiofrequency-dressed ultracold atoms read out by a Faraday spin-light interface, we sense a magnetic signal with $20~\text{pT}$ precision in amplitude, and near-transform-limited precision in frequency, in a single $300~\text{ms}$ sweep from $7$ to $13~\text{kHz}$. The protocol realizes a swept-sine quantum spectrum analyzer, potentially sensing hundreds or thousands of channels with a single quantum sensor.

Quantum data access and quantum processing can make certain classically intractable learning tasks feasible. However, quantum capabilities will only be available to a select few in the near future. Thus, reliable schemes that allow classical clients to delegate learning to untrusted quantum servers are required to facilitate widespread access to quantum learning advantages. Building on a recently introduced framework of interactive proof systems for classical machine learning, we develop a framework for classical verification of quantum learning. We exhibit learning problems that a classical learner cannot efficiently solve on their own, but that they can efficiently and reliably solve when interacting with an untrusted quantum prover. Concretely, we consider the problems of agnostic learning parities and Fourier-sparse functions with respect to distributions with uniform input marginal. We propose a new quantum data access model that we call "mixture-of-superpositions" quantum examples, based on which we give efficient quantum learning algorithms for these tasks. Moreover, we prove that agnostic quantum parity and Fourier-sparse learning can be efficiently verified by a classical verifier with only random example or statistical query access. Finally, we showcase two general scenarios in learning and verification in which quantum mixture-of-superpositions examples do not lead to sample complexity improvements over classical data. Our results demonstrate that the potential power of quantum data for learning tasks, while not unlimited, can be utilized by classical agents through interaction with untrusted quantum entities.

The discovery of the backpropagation algorithm ranks among one of the most important moments in the history of machine learning, and has made possible the training of large-scale neural networks through its ability to compute gradients at roughly the same computational cost as model evaluation. Despite its importance, a similar backpropagation-like scaling for gradient evaluation of parameterised quantum circuits has remained elusive. Currently, the most popular method requires sampling from a number of circuits that scales with the number of circuit parameters, making training of large-scale quantum circuits prohibitively expensive in practice. Here we address this problem by introducing a class of structured circuits that are not known to be classically simulable and admit gradient estimation with significantly fewer circuits. In the simplest case -- for which the parameters feed into commuting quantum gates -- these circuits allow for fast estimation of the gradient, higher order partial derivatives and the Fisher information matrix. Moreover, specific families of parameterised circuits exist for which the scaling of gradient estimation is in line with classical backpropagation, and can thus be trained at scale. In a toy classification problem on 16 qubits, such circuits show competitive performance with other methods, while reducing the training cost by about two orders of magnitude.

The rapid development of quantum computing has led to increasing interest in quantum algorithms for a variety of different applications. Quantum walks have also experienced a surge in interest due to their potential use in quantum algorithms. Using the qiskit software package, we test how accurately the current generation of quantum computers provided by IBM can simulate a cycle discrete-time quantum walk. Implementing an 8-node, 8-step walk and a simpler 4-node, 4-step discrete-time quantum walk on an IBM quantum device known as ibmq_quito, the results for each step of the respective walks are presented. A custom noise model is developed in order to estimate that noise levels in the ibmq_santiago quantum device would need to be reduced by at least 94% in order to execute a 16-node, 16-step cycle discrete-time quantum walk to a reasonable level of fidelity.

A recent theoretical proposal for teleamplification requires preparation of Fock states, programmable interferometers, and photon-number resolving detectors to herald the teleamplification of an input state. These enable teleportation and heralded noiseless linear amplification of a photonic state up to an arbitrarily large energy cutoff. We report on adapting this proposal for Borealis and demonstrating teleamplification of squeezed-vacuum states with variable amplification factors. The results match the theoretical predictions and exhibit features of amplification in the teleported mode, with fidelities from 50 to 93%. This demonstration motivates the continued development of photonic quantum computing hardware for noiseless linear amplification's applications across quantum communication, sensing, and error correction.

The theory of modular flow has proved extremely useful for applying thermodynamic reasoning to out-of-equilibrium states in quantum field theory. However, the standard proofs of the fundamental theorems of modular flow use machinery from Fourier analysis on Banach spaces, and as such are not especially transparent to an audience of physicists. In this article, I present a construction of modular flow that differs from existing treatments. The main pedagogical contribution is that I start with thermal physics via the KMS condition, and derive the modular operator as the only operator that could generate a thermal time-evolution map, rather than starting with the modular operator as the fundamental object of the theory. The main technical contribution is a new proof of the fundamental theorem stating that modular flow is a symmetry. The new proof circumvents the delicate issues of Fourier analysis that appear in previous treatments, but is still mathematically rigorous.

We consider a rational six vertex model on a rectangular lattice with boundary conditions that generalize the usual domain wall type. We find that the partition function of the inhomogeneous version of this model is given by a modified Izergin determinant. The proofs are based on the quantum inverse scattering method and its representation theory together with elementary linear algebra.

Despite significant advancements in the field of quantum sciences over the past two decades, there remains a need for a quantum master equation that precisely and practically depicts quantum dynamics over long-time scales. In this study, we have effectively fulfilled this need by optimizing the computation process of the exact fourth-order time-convolutionless master equation. The earlier versions of this master equation required a three-dimensional integral to be computed, which limited their widespread usability. The master equation takes into account the fact that relaxation and dephasing can happen at the same time. This creates terms that are proportional to the derivative of the system's spectral density with frequency. These relaxation-dephasing hybrids are absent from second-order master equations and can lead to infrared divergent dynamics at zero temperature.

The Jordan-Wigner transformation is frequently utilised to rewrite quantum spin chains in terms of fermionic operators. When the resulting Hamiltonian is bilinear in these fermions, i.e. the fermions are free, the exact spectrum follows from the eigenvalues of a matrix whose size grows only linearly with the volume of the system. However, several Hamiltonians that do not admit a Jordan-Wigner transformation to fermion bilinears still have the same type of free-fermion spectra. The spectra of such ``free fermions in disguise" models can be found exactly by an intricate but explicit construction of the raising and lowering operators. We generalise the methods further to find a family of such spin chains. We compute the exact spectrum, and generalise an elegant graph-theory construction. We also explain how this family admits an N=2 lattice supersymmetry.

Adopting a general framework for quantum-classical dynamics, we analyze the interaction between quantum matter and a classical gravitational field. We point out that, assuming conservation of momentum or energy, and assuming that the dynamics obeys a particular decomposition property set out in the paper, the classical gravitational field cannot change the momentum or energy of the quantum system, whereas the quantum gravitational field can do so. Drawing upon the fundamental relationship between conservation laws and the quantum properties of objects, our analysis offers new perspectives for the study of quantum gravity and provides a novel interpretation of existing experimental observations, such as free fall.

Estimation of quantum relative entropy and its R\'{e}nyi generalizations is a fundamental statistical task in quantum information theory, physics, and beyond. While several estimators of these divergences have been proposed in the literature along with their computational complexities explored, a limit distribution theory which characterizes the asymptotic fluctuations of the estimation error is still premature. As our main contribution, we characterize these asymptotic distributions in terms of Fr\'{e}chet derivatives of elementary operator-valued functions. We achieve this by leveraging an operator version of Taylor's theorem and identifying the regularity conditions needed. As an application of our results, we consider an estimator of quantum relative entropy based on Pauli tomography of quantum states and show that the resulting asymptotic distribution is a centered normal, with its variance characterized in terms of the Pauli operators and states. We utilize the knowledge of the aforementioned limit distribution to obtain asymptotic performance guarantees for a multi-hypothesis testing problem.

Motivated by limitations on the depth of near-term quantum devices, we study the depth-computation trade-off in the query model, where the depth corresponds to the number of adaptive query rounds and the computation per layer corresponds to the number of parallel queries per round. We achieve the strongest known separation between quantum algorithms with $r$ versus $r-1$ rounds of adaptivity. We do so by using the $k$-fold Forrelation problem introduced by Aaronson and Ambainis (SICOMP'18). For $k=2r$, this problem can be solved using an $r$ round quantum algorithm with only one query per round, yet we show that any $r-1$ round quantum algorithm needs an exponential (in the number of qubits) number of parallel queries per round.

Our results are proven following the Fourier analytic machinery developed in recent works on quantum-classical separations. The key new component in our result are bounds on the Fourier weights of quantum query algorithms with bounded number of rounds of adaptivity. These may be of independent interest as they distinguish the polynomials that arise from such algorithms from arbitrary bounded polynomials of the same degree.

In a conducting medium held at finite temperature, free carriers are performing Brownian motion and generate fluctuating electromagnetic fields. We compute the averaged Lorentz force density that turns out nonzero in a thin sub-surface layer, pointing towards the surface, while vanishing in the bulk. This is an elementary example of rectified fluctuations, similar to the Casimir force or radiative heat transport. Our results also provide an experimental way to distinguish between the Drude and so-called plasma models.

We provide a systemic theory to entangle excitons with microcavity photons. This is realized by adopting an exciton-optomechanics system and introducing a nonlinear dispersive interaction with a mechanical oscillator. We show that when either the exciton and cavity modes in the weak-coupling regime, or the two exciton-polariton modes in the strong-coupling regime, are respectively resonant with the optomechanical Stokes and anti-Stokes sidebands, entanglement between excitons and cavity photons, or between two exciton polaritons, can be established. The entanglement is in the steady state and can potentially be achievable at room temperature. In both cases, genuine tripartite entanglement is shown to be present.

Reentrant localization (RL), a recently prominent phenomenon, traditionally links to the interplay of staggered correlated disorder and hopping dimerization, as indicated by prior research. Contrary to this paradigm, our present study demonstrates that hopping dimerization is not a pivotal factor in realizing RL. Considering a helical magnetic system with antiferromagnetic ordering, we uncover spin-dependent RL at multiple energy regions, in the {\em absence} of hopping dimerization. This phenomenon persists even in the thermodynamic limit. The correlated disorder in the form of Aubry-Andr\'{e}-Harper model is introduced by applying a transverse electric field to the helical system, circumventing the use of traditional substitutional disorder. Described within a tight-binding framework, present work provides a novel outlook on RL, highlighting the crucial role of electric field, antiferromagnetic ordering, and the helicity of the geometry.

Quantum approximate optimization algorithm (QAOA) is one of the popular quantum algorithms that are used to solve combinatorial optimization problems via approximations. QAOA is able to be evaluated on both physical and virtual quantum computers simulated by classical computers, with virtual ones being favored for their noise-free feature and availability. Nevertheless, performing QAOA on virtual quantum computers suffers from a slow simulation speed for solving combinatorial optimization problems which require large-scale quantum circuit simulation (QCS). In this paper, we propose techniques to accelerate QCS for QAOA using mathematical optimizations to compress quantum operations, incorporating efficient bitwise operations to further lower the computational complexity, and leveraging different levels of parallelisms from modern multi-core processors, with a study case to show the effectiveness on solving max-cut problems.

Device-independent quantum secure direct communication (DI-QSDC) is a promising primitive in quantum cryptography aimed towards addressing the problems of device imperfections and key management. However, significant effort is required to tackle practical challenges such as the distance limitation due to decohering effects of quantum channels. Here, we explore the constructive effect of non-Markovian noise to improve the performance of DI-QSDC. Considering two different environmental dynamics modeled by the amplitude damping and the dephasing channels, we show that for both cases non-Markovianty leads to a considerable improvement over Markovian dynamics in terms of three benchmark performance criteria of the DI-QSDC task. Specifically, we find that non-Markovian noise (i) enhances the protocol security measured by Bell-violation, (ii) leads to a lower quantum bit error rate, and (iii) enables larger communication distances by increasing the capacity of secret communication.

We show that the perturbation of the Su-Schrieffer-Heeger chain by a localized lossy defect leads to higher-order exceptional points (HOEPs). Depending on the location of the defect, third- and fourth-order exceptional points (EP3s & EP4s) appear in the space of Hamiltonian parameters. On the one hand, they arise due to the non-Abelian braiding properties of exceptional lines (ELs) in parameter space. Namely, the HOEPs lie at intersections of mutually non-commuting ELs. On the other hand, we show that such special intersections happen due to the fact that the delocalization of edge states, induced by the non-Hermitian defect, hybridizes them with defect states. These can then coalesce together into an EP3. When the defect lies at the midpoint of the chain, a special symmetry of the full spectrum can lead to an EP4. In this way, our model illustrates the emergence of interesting non-Abelian topological properties in the multiband structure of non-Hermitian perturbations of topological phases.

An overarching milestone of quantum machine learning (QML) is to demonstrate the advantage of QML over all possible classical learning methods in accelerating a common type of learning task as represented by supervised learning with classical data. However, the provable advantages of QML in supervised learning have been known so far only for the learning tasks designed for using the advantage of specific quantum algorithms, i.e., Shor's algorithms. Here we explicitly construct an unprecedentedly broader family of supervised learning tasks with classical data to offer the provable advantage of QML based on general quantum computational advantages, progressing beyond Shor's algorithms. Our learning task is feasibly achievable by executing a general class of functions that can be computed efficiently in polynomial time for a large fraction of inputs by arbitrary quantum algorithms but not by any classical algorithm. We prove the hardness of achieving this learning task for any possible polynomial-time classical learning method. We also clarify protocols for preparing the classical data to demonstrate this learning task in experiments. These results open routes to exploit a variety of quantum advantages in computing functions for the experimental demonstration of the advantage of QML.