Papers

Molecular quantum-dot Cellular Automata (QCA) may provide low-power, high-speed computational hardware for processing classical information. Simulation and modeling play an important role in the design of QCA circuits because fully-coherent models of QCA scale exponentially with the number of devices, and such models are severely limited in size. For larger circuits, approximations become necessary. In the era of fault-tolerant quantum computation, however, it may become possible to model large QCA circuits without such limitations. Presently, this work explores the use of the noisy-intermediate scale quantum (NISQ) variational quantum eigensolver (VQE) method for estimating the ground state of QCA circuits. This is relevant because the computational result of a QCA calculation is encoded in the circuit's ground state. In this study, VQE is used to model logic circuits, including binary wires, inverters, and majority gates. VQE models are performed ideal simulators, noisy simulators, and actual quantum hardware. This study demonstrates that VQE may indeed be used to model molecular QCA circuits. It is observed that using modern NISQ hardware, results are still quite sensitive to noise, so measures should be taken to minimize noise. These include simplifying the ansatz circuit whenever possible, and using low-noise hardware.

Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein interaction (PPI) networks offers a promising avenue for their detection [ 1, 2], but scaling to large network datasets is computationally demanding. Quantum computing offers compact representations and potential complexity reductions. Motivated by the classical method of Gumpinger et al. [3], in this work we introduce a supervised quantum framework that combines mutation scores with network topology via a novel state preparation scheme, Quantum Multi-order Moment Embedding (QMME). QMME encodes low-order statistical moments over the mutation scores of a node's immediate and second-order neighbors, and encodes this information into quantum states. These are used as inputs to a kernel-based quantum binary classifier that discriminates known driver genes from others. Simulations on an empirical PPI network demonstrate competitive performance, with a 12.6% recall gain over a classical baseline. The pipeline performs explicit quantum state preparation and requires no classical training, enabling an efficient, nearly end-to-end quantum workflow. A brief complexity analysis suggests the approach could achieve a quantum speedup in network-based cancer gene prediction. This work underscores the potential of supervised quantum graph learning frameworks to advance biological discovery.

Unclonable cryptography uses the quantum no-cloning principle to achieve security guarantees that are impossible in a classical world. Most existing works, however, consider only single-key security. The few works that achieve collusion-resistance rely on techniques highly tailored to their applications. Moreover, multi-copy security, where an adversary receives identical copies of a pure state, remains largely open. In this work, we develop a toolset of generic compilers and technical lemmata for collusion-resistance and multi-copy security. We first show how classical functional encryption can upgrade single-key quantum protection of decryption keys to collusion-resistant security, covering copy-protection, secure leasing, and leakage resilience. We then give a purification compiler that upgrades collusion-resistant state-query games with classically determined outputs to multi-copy security, assuming only one-way functions. Along the way, we develop a collusion-resistant one-way-to-hiding lemma, a quantum-state analogue of the small-range-distributions lemma, and a quantum pigeonhole lemma for entangled adversaries. We also construct the first deterministic signatures with quantum-query security and show that strong search security implies identical-challenge security for collusion-resistant single-decryptor encryption. Using these tools, we obtain the first public-key quantum coins, multi-copy secure single-decryptor encryption and unclonable encryption, and collusion-resistant secure key leasing with a fully classical vendor. Our quantum coins and plain-model search-secure UE use indistinguishability obfuscation and one-way functions, while our QROM UE achieves indistinguishability security unconditionally. Finally, we obtain multi-copy secure LOCC leakage-resilient public-key encryption, making progress towards quantum key-fire in the plain model.

What are the resources that can be leveraged for a thermodynamic device to exhibit genuine quantum advantage? Typically, the answer to this question is sought in quantum correlations. In the present work, we show that quantum Otto engines that operate with nonlinear qubits significantly outperform linear engines. To this end, we develop a comprehensive thermodynamic description of nonlinear qubits starting with identifying the proper thermodynamic equilibrium state. We then show that for ideal cycles as well as at maximum power the efficiency of the nonlinear engine is significantly higher. Interestingly, nonlinear dynamics can be thought of as an effective description of a correlated, complex quantum many body system. Hence, our findings corroborate common wisdom, while at the same time propose a new design of more efficient quantum engines.

We present ongoing work on Guppy, a domain-specific language embedded in Python that allows users to write high-level hybrid quantum programs with complex control flow in Pythonic syntax, aiming to run them on actual quantum hardware.

In the field of quantum technology, single photons have emerged as a pivotal resource, prompting the development of heralded single photon sources (HSPS) with enhanced generation probability. The majority of such sources are based on spontaneous parametric down-conversion (SPDC), but they exhibit a low single photon generation probability. The multiplexing principle (arXiv:quant-ph/0205103) has been proposed as a solution to this problem. This paper presents a demonstration of a time-multiplexed HSPS based on the SPDC process, including accurate calculations and modeling of key source characteristics, specifically purity and heralding efficiency. Furthermore, the paper provides an analysis and approximation of the probability of a single photon post-application of time multiplexing.

Light-matter interactions within the strong-field regime, where intense laser fields can ionise a target via tunnelling, give rise to fascinating phenomena such as the generation of high-order harmonic radiation (HHG). On the atomic scale, these strong-field processes are described in terms of highly-oscillatory time integrals which are often approximated using saddle-point methods. These methods simultaneously simplify the calculations and let us understand the physical processes in terms of semi-classical electron trajectories, or quantum orbits. However, applying saddle-point methods for HHG driven by polychromatic laser fields without clear dynamical symmetries has remained challenging. Here we introduce Picard-Lefschetz theory as a universal and robust link between the time integrals and the semi-classical trajectories. The continuous deformation of the integration contour towards so-called Lefschetz thimbles allows an exact evaluation of the integral, as well as the identification of relevant quantum orbits, for arbitrary driving fields. The latter is realised via the “necklace algorithm”, a novel solution to the open problem of determining the relevance of saddle points for a two-dimensional integral, which we introduce here. We demonstrate the versatility and rigour of Picard-Lefschetz methods by studying Stokes transitions and spectral caustics arising in HHG driven by two-colour laser fields. For example, we showcase a quantum-orbit analysis of the colour switchover, which links the regime of perturbative two-colour fields with that of fully bichromatic driving fields. With this work, we set the foundation for a rigorous application of quantum-orbit based approaches in attosecond science that enables the interpretation of state-of-the-art experimental setups, and guides the design of future ones.

We derive semiclassical analytical solutions for both the diagonal and off-diagonal functions in the eigenstate thermalization hypothesis (ETH) in a quarter-stadium quantum billiard. For a representative observable, we obtain an explicit expression and an asymptotic closed-form solution that naturally separate into a local contribution and a phase-space correlation term. These analytical results predict the band structure of the observable matrix, including its bandwidth and scaling behavior. We further demonstrate that our analytical formula is equivalent to the prediction of Berry's conjecture. Supported by numerical evidence, we show that Berry's conjecture captures the energetic long-wavelength behavior in the space of eigenstates, while our analytical solution describes the asymptotic behavior of the f function in the semiclassical limit. Finally, by revealing the connection between the bandwidth scaling and the underlying classical dynamics, our results suggest that the ETH carries important physical implications in single-particle and few-body systems, where "thermalization" manifests as the loss of information about initial conditions.

The electromagnetic Green's function is a crucial ingredient for the theoretical study of modern photonic quantum devices, but is often difficult or even impossible to calculate directly. We present a numerically efficient framework for calculating the scattered electromagnetic Green's function of a multi-cavity system with spatially separated open cavities (with arbitrary shape, dispersion and loss) and finite retardation times. The framework is based on a Dyson scattering equation that enables the construction of the Green's function from the quasinormal modes of the individual resonators within a few-mode approximation and a finite number of iteration steps without requiring nested integrals. The approach shows excellent agreement with the full numerical Green's function for the example of two coupled dipoles located in the gaps of two metal dimers serving as quasinormal mode cavities, and is easily extended to arbitrarily large separations and multiple cavities.

Recently, machine learning has been widely applied in the field of quantum information, notably in tasks such as entanglement detection, steering characterization, and nonlocality verification. However, few studies have focused on utilizing machine learning to detect quantum information masking. In this work, we investigate supervised machine learning for detecting quantum information masking in both pure and mixed qubit states. For pure qubit states, we randomly generate the corresponding density matrices and train an XGBoost model to detect quantum information masking. For mixed qubit states, we improve the XGBoost method by optimizing the selection of training samples. The experimental results demonstrate that our approach achieves higher classification accuracy. Furthermore, we analyze the area under the curve (AUC) of the receiver operating characteristic curve for this method, which further confirms its classification performance.

The quantum logic program originated in a 1936 article by G. Birkhoff and J. von Neumann. This program is generally disregarded due to no-go theorems restricting the existence of the tensor product of elementary quantum logics and, above all, the impossibility of considering entangled states and Bell non-local states within the framework of these composite quantum logics. We revisit this study from the beginning and reverse the perspective. Here, the existence of a tensor product and a star involution are the only prerequisites for the definition of the state spaces. Surprisingly, the quantum logics constructed in this way turn out to have a close connection with irreducible Hilbert geometries, even though we did not impose this sort of structure ab initio. Endly, the existence of some basic quantum-like properties is explicitly proven in our framework : contextuality, no-broadcasting theorem, and Bell non-locality. These elements demonstrate that our quantum logic program is capable of achieving G. Birkhoff and J. von Neumann's initial ambition of founding quantum theory.

A central concept in quantum information processing is genuine multipartite entanglement (GME), a type of correlation beyond biseparability, that is, correlations that cannot be explained by statistical mixtures of partially separable states. GME is relevant for characterizing and benchmarking complex quantum systems, and it is an important resource for applications such as quantum communication. Remarkably, it has been found that GME can be activated from multiple copies of biseparable quantum states, which do not possess GME individually. Here, we experimentally demonstrate unambiguous evidence of such GME activation from two copies of a biseparable three-qubit state in a trapped-ion quantum processor. These results not only challenge notions of quantum resources but also highlight the potential of using multiple copies of quantum states to achieve tasks beyond the capabilities of the individual copies.

Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit. Due to decoherence, the quality of the computational outcome can degrade seriously with increasing circuit length. Thus, there is major interest to reduce a transpiled quantum circuit to an equivalent circuit which is in its gate count as short as possible. This is, what we call quantum circuit reduction: Finding a quantum circuit with a reduced amount of gates without changing its unitary. One method to address efficient transpilation, e.g. as a post-transpilation process, is based on approaches known from stochastic optimization, e.g. by using random sampling and local resynthesis strategies. Here, a core challenge is that these methods can suffer from sampling efficiency, causing long and energy consuming optimization time. As a remedy, we propose in this work 2D neural guided sampling. Thus, given a 2D representation of a quantum circuit, a neural network predicts groups of gates in the quantum circuit, which are likely reducible. It leads to a sampling prior which can heavily reduce the compute time for quantum circuit reduction. In several experiments, we demonstrate that our method is superior to results obtained from different qiskit or BQSKit optimization levels.

Thermal phonons are a major source of decoherence in quantum mechanical systems. Operating in the quantum ground state is therefore often an experimental prerequisite. Additionally to passive cooling in a cryogenic environment, active laser cooling enables the reduction of phonons at specific acoustic frequencies. Brillouin cooling has been used to show efficient reduction of the thermal phonon population in waveguides at GHz frequencies down to 74 K. In this letter, we demonstrate cooling of a 7.608 GHz acoustic mode by combining Brillouin active cooling with precooling from 77 K using liquid nitrogen. We show a 69 % reduction in the phonon population, resulting in a final temperature of 24.3 K, 50 K lower than previously reported.

Recent advances in quantum simulator experiments enable unprecedented access to quantum many-body states through snapshot measurements of individual many-body configurations. Here, we introduce an exact renormalization group (RG) transformation that can be directly applied to any such snapshot dataset. Our SnapshotRG operates in real space, but can also be directly translated to an RG in the abstract dataspace of measurement configurations, providing a framework for the characterization of quantum many-body systems on a more general level. We demonstrate that snapshot datasets in dataspace exhibit self-similarity at continuous phase transitions, providing an explanation for the recently observed scale-freeness of so-called wavefunction networks. As a consequence, scale invariance extends beyond traditional low-order correlation functions to encompass the full statistical structure of quantum states as contained in their snapshot datasets. Our SnapshotRG can be readily implemented with snapshot data generated by numerical method such as neural quantum states or any quantum simulation platform, offering a versatile tool for characterizing quantum phase transitions and critical phenomena in quantum matter.

Quantum computers are expected to offer advantages in solving optimization problems challenging for classical computers. Quadratic Unconstrained Binary Optimization (QUBO) problems represent an important class of problems with relevance in finance and logistics. The Quantum Approximate Optimization Algorithm (QAOA) is a prominent candidate for solving QUBO problems on near-term quantum devices. In this paper, we evaluate the performance of both the standard QAOA and the adaptive derivative assembled problem tailored QAOA (ADAPT-QAOA) to solve QUBO problems of varying sizes and hardnesses for financial feature selection problems. Our main observation is that ADAPT-QAOA achieves substantially higher approximation ratios than standard QAOA for harder feature-selection problems ({\alpha} = 0.6) with statistically significant improvements observed for problem sizes n = 6 and 14. However, the standard QAOA remains competitive for simpler problems. Additionally, we evaluate the practical feasibility and limitations of QAOA through a hardware-aware scaling analysis based on the real-device calibration data for various hardware platforms. We estimate that standard QAOA implementation on superconducting quantum computers provides a shorter time-to-solution compared to trapped-ion devices, while trapped-ion devices yield more favorable error rates. Our findings provide a comprehensive overview of the challenges, trade-offs, and strategies for deploying QAOA-based methods on near-term quantum hardware.

We present a precise characterization of the onset of classicality that combines the formalism of quantum Darwinism with the tools from quantum metrology. We show that the quantum Fisher information provides a useful metric for assessing the rate at which classical objectivity emerges. Furthermore, our formalism allows us to explore how the choice of measurement impacts the precision with which an observer can determine the state of the system. For a paradigmatic example of the spin-star model, we demonstrate that optimal measurements lead to the emergence of classicality at an exponential rate. Although other measurements necessarily lead to slower emergence, we importantly show that suboptimal measurements can still saturate the Cramér-Rao bound. By recasting emergent classicality as an information acquisition protocol, our framework provides a precise operational description of quantum Darwinism.

Quantum machine learning (QML) holds promise for computational advantage, yet progress on real-world tasks is hindered by classical preprocessing and noisy devices. We introduce ViT-QCNN-FT, a hybrid framework that integrates a fine-tuned Vision Transformer with a quantum convolutional neural network (QCNN) to compress high-dimensional images into features suited for noisy intermediate-scale quantum (NISQ) devices. By systematically probing entanglement, we show that ansatzes with uniformly distributed entanglement entropy consistently deliver superior non-local feature fusion and state-of-the-art accuracy (99.77% on CIFAR-10). Surprisingly, quantum noise emerges as a double-edged factor: in some cases, it enhances accuracy (+2.71% under amplitude damping). Strikingly, substituting the QCNN with classical counterparts of equal parameter count leads to a dramatic 29.36% drop, providing unambiguous evidence of quantum advantage. Our study establishes a principled pathway for co-designing classical and quantum architectures, pointing toward practical QML capable of tackling complex, high-dimensional learning tasks.

Quantum secret sharing (QSS) plays a critical role in building the distributed quantum networks. Device-independent (DI) QSS provides the highest security level for QSS. However, the photon transmission loss and extremely low multipartite entanglement generation rate largely limit DI QSS's secure photon transmission distance (less than 1 km) and practical key generation efficiency. To address the above drawbacks, we propose the quantum memory-assisted (QMA) DI QSS protocol based on single photon sources (SPSs). The single photons from the SPSs are used to construct long-distance multipartite entanglement channels with the help of the heralded architecture. The heralded architecture enables our protocol to have an infinite secure photon transmission distance in theory. The QMA technology can not only increase the multi-photon synchronization efficiency, but also optimize the photon transmittance to maximize the construction efficiency of the multipartite entanglement channels. Our protocol achieves the practical key generation efficiency seven orders of magnitude higher than that of the existing DI QSS protocols based on cascaded spontaneous parametric down-conversion sources and six orders of magnitude higher than that of the DI QSS based on SPSs without QMA. Our protocol has modular characteristics and is feasible under the current experimental technical conditions. Combining with the advanced random key generation basis strategy, the requirement on experimental devices can be effectively reduced. Our protocol is expected to promote the development of long-distance and high-efficiency DI quantum network in the future.

While the preparation of a general quantum state is challenging, realistic problem instances, such as those encountered in quantum chemistry and quantum machine learning-typically exhibit hierarchical amplitude structures, consisting of a small number of large components alongside a vast number of small but non-negligible ones. Standard approaches deterministically truncate the small amplitude would incur an approximation error that scales linearly with the discarded amplitude mass, enforcing a rigid trade-off between precision and circuit depth. Here, we circumvent the challenge by introducing a randomized state-preparation protocol with probabilistic amplification of small amplitudes using ensembles of low-complexity circuits. Analytically, we prove that this approach significantly reduces the number of encoded amplitudes, halving the requirement for exponentially decaying states and offering asymptotically larger gains for heavy-tailed power-law decays. Numerical simulations on LiH molecular wavefunctions and deep-learning-derived states demonstrate reductions of up to 99 percent in CNOT and T-gate counts compared with deterministic methods. These results establish a resource-efficient paradigm for initializing complex states, relaxing gate-synthesis precision requirements for both near-term and fault-tolerant hardware, and improving the end-to-end feasibility of quantum computing.

Adiabatic evolution is a central paradigm in quantum physics. Digital simulations of adiabatic processes are generally viewed as costly, since algorithmic errors typically accumulate over the long evolution time, requiring exceptionally deep circuits to maintain accuracy. This work demonstrates that digital adiabatic evolution is intrinsically accurate and robust to simulation errors. We analyze two Hamiltonian simulation methods – Trotterization and generalized quantum signal processing – and prove that the simulation error does not increase with time. Numerical simulations of molecular systems and linear equations confirm the theory, revealing that digital adiabatic evolution is substantially more efficient than previously assumed. Remarkably, our estimation for the first-order Trotterization error can be 10^6 times tighter than previous analyses for the transverse field Ising model even with less than 6 qubits. The findings establish fundamental robustness of digital adiabatic evolution and provide a basis for accurate, efficient implementations on fault-tolerant – and potentially near-term – quantum platforms.

The coherent emission of multiple atoms gives rise to superradiance, a cornerstone phenomenon in quantum optics with wide-ranging applications in quantum information processing and precision metrology. Despite its importance, how the superradiant scaling with respect to the number of participating atoms can be effectively controlled remains largely unexplored. In this work, we investigate a cavity-QED system and demonstrate that atom-photon coupling can significantly alter the emission behavior–suppressing the collective superradiant scaling while enhancing the scaling associated with individual atomic emissions. Our study provides a pathway toward controllable collective emission in state-of-the-art experimental platforms.

In this paper, we introduce hierarchical random walks at first. In this model, we use two types of random walkers, {global and local} walkers. The global walker chooses a local walker at every step, then the chosen local walker moves a single step. After that we construct the corresponding continuous-time quantum walks and discuss its spectral structures. Then we define multi-dimensional continuous-time quantum walk by taking a marginal distribution respect to the global walker.

Quantum correlations in Bell and prepare-and-measure experiments are central resources for probing nonclassicality and enabling device-based quantum information protocols. In the absence of shared public randomness (i.e., without run-to-run mixing), even qubit correlation sets are typically non-convex, making standard convex characterizations inadequate. Here we derive qubit-specific constraints from uncertainty relations, yielding a state-independent consistency test for observed statistics in both prepare-and-measure and Bell scenarios. The test captures explicit non-convex boundaries in representative correlation families and enables correlation-based device inference by constraining (and sometimes uniquely determining) unitary-invariant measurement parameters even away from extreme points. Moreover, incorporating the inferred qubit constraints as additional conditions in a moment-matrix relaxation strengthens separability tests and can certify entanglement even for Bell-local correlations within the independent-device model. These tools provide a practical route to characterize and leverage low-dimensional quantum devices, including certification, randomness generation, and entanglement verification.

Contemporary perspectivism is viewed as a framework of scientific inquiry concerning the origin, generation and systematization of scientific knowledge of nature by focusing on the conditions under which such knowledge may arise in perspectivist terms and investigating the essential ramifications of these conditions. To this end, we develop the conceptual, methodological and semantic framework of contemporary perspectivism according to the norms of the proposed endo-theoretic approach. Implementation of the preceding three-fold scheme in quantum mechanics implies that the global structure of a quantum algebra of events can be consistently comprehended through a multilevel structure of locally variable Boolean perspectives, interconnected in a category-theoretic environment, yielding jointly all the information encoded in the former. In this respect, the proposed approach validates the perspectivist/contextual nature of quantum mechanics at a fundamental level of discourse. Furthermore, due to its general character, it may acquire the form of a theoretical pattern of scientific inquiry in the natural sciences, especially when dealing with complex trans-perspectival phenomena, the analysis of which requires the use of information resulting from more than one perspective. Finally, in the appendix, we provide a concise comparative assessment between our perspectivist framework of quantum theory and Rovelli's relational interpretation of quantum mechanics.

We present a thermal engine that exploits the cooperative superradiance and superabsorption of a sample of \(N\) two-level atoms. This engine operates using a single cold reservoir via cycles of collective pumping followed by decay. Using an effective mean-field Hamiltonian to describe the many-body dynamics, we design optimized drive pulses that preserve adiabaticity and achieve an average power output scaling quadratically with the system size, \(P \propto N^2\). An experimentally measurable figure of merit demonstrates that the efficiency of this superengine can approach unity. The resulting analytical model, which yields a representative Hamiltonian for the sample within the mean-field formalism, is validated by numerical simulations. Our results pave the way for scalable and highly efficient quantum heat engines based on collective effects.

We investigate the quantum dynamics of Coulomb potential systems in thermal baths. We study these systems within the framework of open quantum dynamics theory, focusing on preserving the rotational symmetry of the entire system, including the baths. Thus, we employ a three-dimensional rotationally invariant system-bath (3D-RISB) model to derive numerically “exact” hierarchical equations of motion for atomic orbitals (AO-HEOM) that enable a non-perturbative and non-Markovian treatment of system-bath interactions at finite temperatures. To assess the formalism, we calculated the linear absorption spectrum of an atomic system under isotropic thermal environment, with systematic variation of system-bath coupling strength and temperature.

We report on the synthesis and spectroscopic characterization of Sm$^{3+}$-doped K$_2$YF$_5$ microparticles. The particles were synthesized via the hydrothermal technique, yielding a particle size of approximately 20 $\mu$m in length. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses confirmed their orthorhombic crystal structure. A combination of absorption and laser excited fluorescence performed on samples cooled to 10~K, allow for the determination of {56} experimental crystal-field levels. A parametrised crystal-field analysis for Sm$^{3+}$ in the C$_{\rm s}$ point group symmetry centres of K$_2$YF$_5$ yields good approximation to the data.

We consider bound and scattering states of the one-dimensional dimer formed by two coupled non-identical atoms when one of them also interacts with the zero-range potential located at the origin. By calculating the dimer localized and scattering wave functions, we identify properties of the system without the two-body bound-state breakdown. In general, we predict an enhancement of the dimer reflection compared to a single atom, except for a narrow region on the attractive side of the external potential.

Precise, real-time monitoring of magnetic field evolution is important in applications including magnetic navigation and searches for physics beyond the standard model. One main field-monitoring technique, the spin-precession magnetometer (SPM), observes electron, nucleus, colour centre, or muon spins as they precess in response to their local magnetic field. Here, we study Bayesian signal-recovery methods for SPMs in the free-induction decay (FID) mode. In particular, we study tracking of field changes well within the coherence time of the spin system, and thus well beyond the response bandwidth, as in [R. Jiménez-Martínez et al., Phys. Rev. Lett. 120, 040503 (2018)]. We derive the Bayesian Cramér-Rao bound that dictates the ultimate precision in estimating the Larmor frequency, which we show to be attained by the computationally expensive prediction error method (PEM). Relative to this benchmark, we show that the extended Kalman filter (EKF) and cubature Kalman filter (CKF) offer near-optimal tracking that is also computationally efficient, with the use of the latter giving better results only for a large spin number. Focusing thus on the EKF, we show that it is sufficient to accurately track fluctuating and unknown transient signals. Our methods can be easily adapted to other types of sensors undergoing nonlinear dissipative dynamics and experiencing intrinsic Gaussian-like stochastic noises.