Quantum circuit compilation is an indispensable part of executing quantum circuits on quantum devices. It translates logical quantum circuits into physical quantum circuits that meet the device constraints. However, nonspecialist users require extensive expertise to effectively select and evaluate quantum circuit compilation processes. Therefore, this paper proposes a quantum circuit compilation option prediction method based on Deep Residual Attention Neural Network. The method aims to automate the selection of the best compilation scheme and enhance the efficiency and accuracy of quantum circuit compilation. Firstly, a quantum circuit feature extraction algorithm based on Time-Weighted Interaction Graph is proposed. This algorithm can effectively represent quantum circuits using fixed-length vectors, making the model input scalable. Secondly, for existing one-dimensional features, a quantum circuit compilation option prediction model based on Deep Residual Attention Neural Network is designed and trained. This model is capable of quickly and accurately predicting the optimal compilation option combination. Extensive experimental evaluations and comparisons are carried out on the MQT (Munich Quantum Toolkit) quantum circuit compilation dataset. These evaluations verify the effectiveness and advancement of the proposed method. Compared with the current state-of-the-art method (Random Forest), the proposed method improves the accuracy by 5.44%, the Top-3 accuracy by 2.6%, and the F1 by 2.05%.
Associated to every finite abelian group G, there is a generalization of qubit Clifford group associated to G. We demonstrate that every G-Clifford gate can be decomposed into some distinguished elementary types: Fourier transforms, quadratic phase gates, and automorphism gates. Combined with earlier results on classical simulation of normalizer circuits, this implies a generalized Gottesman-Knill-type result. We additionally provide circuits for a universal quantum computing scheme based on local two-qudit G-Clifford gates and magic states.
We contribute further to recent theoretical studies of entanglement dynamics in open quantum systems to include a continuous variable N-harmonic oscillator ring system bathed in Ornstein-Uhlenbeck noise. Despite the fact that harmonic oscillator systems are one of the most studied systems in physics, we believe that our work here is unique and sheds a new perspective in terms of a potential application. We present two sets of differential equations that govern the dynamics of N harmonic oscillators derived from first principles, one for a Markovian environment and the other for non-Markovian one. With these two sets, we are able to compare their entanglement dynamics between environments given an initial state of our oscillators. Across several system and environmental parameters, we show that the entanglement dynamics between oscillator pairs in a non-Markovian environment may be significantly enhanced in both magnitude and duration when compared to its Markovian approximation. We showcase this by numerically simulating a specific solution, i.e., we choose N = 4 oscillators, where two harmonic oscillators are initialized as a two-mode squeezed state while the remaining oscillators are vacuum states. By leveraging our N = 4 results, we consider a slightly larger number of oscillators, i.e., N = 6, and show how we can generate a hidden quantum network, in the absence of direct coupling, that may allow multiple parties to secretly and passively entangle oscillators in their possession. This is accomplished by exploiting the environmental memory effect and maintaining resonance with other oscillators or the central frequency in a shared non-Markovian bath, which may allow for autonomous entanglement distribution over a network of oscillators and aid in future quantum technologies.
In the Internet of Medical Things (IoMT), sensors automatically collect data and transmit it to hospital servers, where doctors provide professional medical advice. The emergence of IoMT has significantly reduced the burden on chronic-disease patients and improved their life security. However, the transmission of patients' medical data still faces serious confidentiality and integrity risks. This paper proposes a novel healthcare-system architecture that integrates a controlled-authentication semi-quantum key-negotiation (SQKN) protocol, leveraging the unconditional security of quantum cryptography to mitigate potential data attacks. The hospital server acts as a trusted controller that supervises every protocol run and authenticates all participants, which effectively thwarts man-in-the-middle and identity-spoofing attacks. The semi-quantum design further minimizes the quantum-device burden on patients' side. Experimental simulation under realistic noise (QBER approximate to 0.03 at 0.02/0.04 noise level, 3 000 shots) shows the protocol keeps the quantum-bit error rate below 3 %, while the overall key-generation efficiency reaches 92 %, demonstrating that the scheme resists known attacks and maintains high quantum efficiency for safeguarding IoMT data security.
Problem Statement Accurate estimation of extreme financial risks, such as Conditional Value-at-Risk (CVaR) at high confidence levels (alpha >= 0.95), poses significant computational challenges for classical Monte Carlo methods, which require O(1/& varepsilon;(2)) samples and struggle to scale under rare-event scenarios. Methodology To address this, we propose a hybrid variational quantum-spintronic framework integrating Variational Quantum State Preparation (VQA), a threshold comparator oracle, and Maximum-Likelihood Amplitude Estimation (MLAE) to enable quantum-accelerated CVaR estimation suitable for NISQ devices. Results Using only 6 qubits and K = 6 amplification levels, our approach achieves CVaR<^>(0.95)=0.281 +/- 0.012 (error < 1.1% compared to classical benchmarks), with tail probability p=0.0478 +/- 0.0009 and state fidelity 0.967 on the ibm_brisbane backend. MLAE reduces circuit depth by 3.3 & times; relative to canonical QAE, and a conceptual spintronic implementation achieves similar to 2.0 fJ/gate, corresponding to an 80% energy reduction versus CMOS. Contributions This work introduces the first NISQ-compatible, energy-efficient quantum-spintronic pipeline for regulatory-compliant financial risk analytics, providing a quadratic sampling speedup, accurate tail-risk estimation, and a pathway toward sustainable quantum finance.
