EPJ Quantum Technology最新文献

The integration of Quantum Deep Learning (QDL) techniques into the landscape of financial risk analysis presents a promising avenue for innovation. This study introduces a framework for credit risk assessment in the banking sector, combining quantum deep learning techniques with adaptive modeling for Row-Type Dependent Predictive Analysis (RTDPA). By leveraging RTDPA, the proposed approach tailors predictive models to different loan categories, aiming to enhance the accuracy and efficiency of credit risk evaluation. While this work explores the potential of integrating quantum methods with classical deep learning for risk assessment, it focuses on the feasibility and performance of this hybrid framework rather than claiming transformative industry-wide impacts. The findings offer insights into how quantum techniques can complement traditional financial analysis, paving the way for further advancements in predictive modeling for credit risk.

There is a lack of adequate studies on dynamic environments control for Quantum Reinforcement Learning (QRL) algorithms, representing a significant gap in this field. This study contributes to bridging this gap by demonstrating the potential of quantum RL algorithms to effectively handle dynamic environments. In this research, the performance and robustness of Quantum Deep Q-learning Networks (DQN) were examined in two dynamic environments, Cart Pole and Lunar Lander, by using three distinct quantum Ansatz layers: RealAmplitudes, EfficientSU2, and TwoLocal. The quantum DQNs were compared with classical DQN algorithms in terms of convergence speed, loss minimization, and Q-value behavior. It was observed that the RealAmplitudes Ansatz outperformed the other quantum circuits, demonstrating faster convergence and superior performance in minimizing the loss function. To assess robustness, the pole length was increased in the Cart Pole environment, and a wind function was added to the Lunar Lander environment after the 50th episode. All three quantum Ansatz layers were found to maintain robust performance under disturbed conditions, with consistent reward values, loss minimization, and stable Q-value distributions. Although the proposed QRL demonstrates competitive results overall, classical RL can surpass them in convergence speed under specific conditions.

Temporal resolution is a critical figure of merit in quantum sensing. This study combines the distinguishable condition of quantum states with quantum speed limits to establish a lower bound on interrogation time. When the interrogation time falls below this bound, the output state becomes statistically indistinguishable from the input state, and the information will inevitably be lost in noise. Without loss of generality, we extend these conclusions to time-dependent signal Hamiltonian. In theory, leveraging certain quantum control techniques allows us to calculate the minimum interrogation time for arbitrary signal Hamiltonian. Finally, we illustrate the impact of quantum speed limits on magnetic field measurements and temporal resolution.

This study investigates the use of spiral geometry in superconducting resonators to achieve high intrinsic quality factors, crucial for applications in quantum computation and quantum sensing. We fabricated Archimedean Spiral Resonators (ASRs) using domain-matched epitaxially grown titanium nitride (TiN) on silicon wafers, achieving intrinsic quality factors of (Q_{mathrm{i}} = (9.6 pm 1.5) times 10^{6}) at the single-photon level and (Q_{mathrm{i}} = (9.91 pm 0.39) times 10^{7}) at high power, which is more than twice as high as those for coplanar waveguide (CPW) resonators under identical conditions on the same chip. We conducted a comprehensive numerical analysis using COMSOL to calculate surface participation ratios (PRs) at critical interfaces: metal-air, metal-substrate, and substrate-air. Our findings reveal that ASRs have lower PRs than CPWs, explaining their superior quality factors and reduced coupling to two-level systems (TLSs).

Continuous variable quantum key distribution bears the promise of simple quantum key distribution directly compatible with commercial off the shelf equipment. However, for a long time its performance was hindered by the absence of good classical postprocessing capable of distilling secret-keys in the noisy regime. Advanced coding solutions in the past years have partially addressed this problem enabling record transmission distances of up to 165 km, and 206 km over ultra-low loss fiber. In this paper, we show that a very simple coding solution with a single code is sufficient to extract keys at all noise levels. This solution has performance competitive with prior results for all levels of noise, and we show that non-zero keys can be distilled up to a record distance of 192 km assuming the standard loss of a single-mode optical fiber, and 240 km over ultra-low loss fibers. Low-rate codes are constructed using multiplicatively repeated non-binary low-density parity-check codes over a finite field of characteristic two. This construction only makes use of a ((2, k))-regular non-binary low-density parity-check code as mother code, such that code design is in fact not required, thus trivializing the code construction procedure. The construction is also inherently rate-adaptive thereby allowing to easily create codes of any rate. Rate-adaptive codes are of special interest for the efficient reconciliation of errors over time or arbitrary varying channels, as is the case with quantum key distribution. In short, these codes are highly efficient when reconciling errors over a very noisy communication channel, and perform well even for short block-length codes. Finally, the proposed solution is known to be easily amenable to hardware implementations, thus addressing also the requirements for practical reconciliation in continuous variable quantum key distribution.

We demonstrate that atomic spin trajectories on the Bloch sphere can be manipulated through direct feedback, achieving spin amplification. This method is applied to Spin-Exchange Relaxation-Free (SERF) magnetometers where a feedback loop introduces a magnetic field positively proportional to the transverse spin polarization, which significantly amplifies the low-frequency response signal by an order of magnitude. Experimental results show that the feedback mechanism improves the signal-to-noise ratio and effectively strengthens the system’s ability to suppress technical noise. In addition, this feedback-enabled magnetometer exhibits superior sensitivity at lower spin polarization, reducing reliance on optical power and thereby facilitating scalability in multi-channel systems. This approach can be extended to various physical systems utilizing atomic spins, such as quantum memory and quantum metrology.

Quantum noise in real-world devices poses a significant challenge in achieving practical quantum advantage, since accurately compiled and executed circuits are typically deep and highly susceptible to decoherence. To facilitate the implementation of complex quantum algorithms on noisy hardware, we propose an approximate method for compiling target quantum circuits into brick-wall layouts. This new circuit design consists of two-qubit CNOT gates that can be directly implemented on real quantum computers, in conjunction with optimized one-qubit gates, to approximate the essential dynamics of the original circuit while significantly reducing its depth. Our approach is evaluated through numerical simulations of time-evolution circuits for the critical Ising model, quantum Fourier transformation, and Haar-random quantum circuits, as well as experiments on IBM quantum platforms. By accounting for compilation error and circuit noise, we demonstrate that time evolution and quantum Fourier transformation circuits achieve high compression rates, while random quantum circuits are less compressible. The degree of compression is related to the rate of entanglement accumulation in the target circuit. In particular, experiments on IBM platforms achieve a compression rate of 12.5 for (N=12), significantly extending the application of current quantum devices. Furthermore, large-scale numerical simulations for system sizes up to (N=30) reveal that the optimal depth (d_{mathrm{max}}) to achieve maximal overall fidelity is independent of system size N, suggesting the scalability of our method for large quantum devices in terms of quantum resources.

Biomolecular residue pairs have been utilized in constructing quantum logic gates (QLGs), significantly reducing the hardware size to the subnanoscale level. However, the development of molecular fault-tolerant topological quantum computers (TQCs) presents challenges in error reduction and hardware size minimization. This study presents the manipulation of molecular QLGs (MQLGs) by utilizing protein residue pairs as molecular transistors, enabling the construction of molecular topological QLGs. This innovative approach leverages molecular functionality in quantum computer (QC) designs to build sub-nanometer transistors that significantly reduce size, enhance efficiency, and accelerate computing. The transmission spectra of electron transfer in molecular junction systems were analyzed using the non-equilibrium Green’s function method. The molecular field effect led to the observation of four quantum states on a two-dimensional potential energy surface with two degrees of freedom—proton translation and molecular rotation. These states form a quaternary QLG, similar to a 2-qubit controlled-NOT logic gate. By applying the Kitaev honeycomb lattice model, MQLGs were employed to generate nonabelian anyons that adhere to fusion rules, such as Ising and Fibonacci anyons. Furthermore, quantum circuits incorporating nonabelian anyons and their fusion processes were developed for practical applications. These findings underscore the shift away from conventional atom-based silicon technology and highlight the potential for innovative applications of molecular universal QLGs, particularly in the advancement of sub-nanometer molecular fault-tolerance TQCs.

The Sum of Even-Mansour (SoEM) construction, proposed by Chen et al. at Crypto 2019, has become the basis for designing some symmetric schemes, such as the nonce-based MAC scheme (text{nEHtM}_{p}) and the nonce-based encryption scheme CENCPP^∗. In this paper, we make the first attempt to study the quantum security of SoEM under the Q1 model where the targeted encryption oracle can only respond to classical queries rather than quantum ones. Firstly, we propose a quantum key recovery attack on SoEM21 with a time complexity of (tilde{O}(2^{n/3})) along with (O(2^{n/3})) online classical queries. Compared with the current best classical result which requires (O(2^{2n/3})) time, our method offers a quadratic time speedup while maintaining the same number of queries. The time complexity of our attack is less than that observed for quantum exhaustive search by a factor of (2^{n/6}). We further propose classical and quantum key recovery attacks on the generalized SoEMs1 construction (consisting of (sgeq 2) independent public permutations), revealing that the application of quantum algorithms can provide a quadratic acceleration over the pure classical methods. Our results also imply that the quantum security of SoEM21 cannot be strengthened merely by increasing the number of permutations.

Space science and technology are among the most challenging and strategic fields in which quantum computing promises to have a pervasive and long-lasting impact. We provide an overview of selected published works reporting the application of quantum computing to space science and technology. Our systematic analysis identifies three major classes of problems that have been approached with quantum computing. The first category includes optimization tasks, often cast into Quadratic Unconstrained Binary Optimization and solved using quantum annealing, with scheduling problems serving as a notable example. A second class comprises learning tasks, such as image classification in Earth Observation, often tackled with gate-based hybrid quantum-classical computation, namely with Quantum Machine Learning concepts and tools. Finally, integrating quantum computing with other quantum technologies may lead to new disruptive technologies, for instance, the creation of a quantum satellite internet constellation and distributed quantum computing. We organize our exposition by providing a critical analysis of the main challenges and methods at the core of different quantum computing paradigms and algorithms, which are often fundamentally similar across different domains of application in the space sector and beyond.