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Zheng Shan

Publications and source records attributed to Zheng Shan.

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Recovering Readout-Limited Fisher Information in Superconducting-Qubit Magnetometry with Squeezed Microwaves

The performance of superconducting-qubit magnetometers depends not only on magnetic-field encoding during Ramsey interrogation, but also on how efficiently the encoded information is recovered during readout. Here we quantify how squeezed-microwave-assisted dispersive readout can recover magnetic-field information lost during qubit-state assignment. We develop an effective detected-mode framework linking projected quadrature noise, state-assignment error, and the classical Fisher information accessible from binary readout outcomes. A finite mismatch between the squeezed quadrature and the discrimination axis produces an optimal squeezing strength through the competition between squeezed and anti-squeezed fluctuations. For representative parameters, squeezed readout reduces the readout-limited magnetic-field sensitivity bound by $27.3\%$. This improvement arises from recovering information lost in the readout stage rather than from increasing the information encoded during Ramsey interrogation. These results may provide a practical route for mitigating measurement-stage information loss in superconducting quantum sensing.

quant-ph

QMClaw: A Scalable General-purpose Framework for Quantum Measurement and Control

As quantum computing continues to scale, quantum measurement and control (QMC) are increasingly constrained by calibration workflow complexity and by requirements for low-latency execution, robust exception handling, and traceable workflow governance. Existing frameworks for QMC are specialized and task-specific, while language-model-based agents for QMC suffer from excessive latency and cannot satisfy the strict timing and control-density demands of large-scale quantum systems. Here we propose QMClaw, a general, workflow-oriented framework for QMC built, featuring a local-first, tool-governed, robust architecture. At its core is a RuleEngine-centered control layer that processes structured context, performs rule-based state transitions, and generates execution plans for typical calibration workflows. Language models are used only for natural-language interaction, high-level task understanding, and exception support, keeping the critical fast path efficient. We implement a single qubit tune-up workflow as a demonstration and validation using real quantum device dataset. We also prove that the framework achieves quantitatively acceptable levels in terms of resource cost, LLM calling times and decision latency, enabling its practical deployment in large-scale quantum qubit measurement and control scenarios. This work presents a general workflow-oriented framework for QMC and provides evidence that rule-centered architectures are a promising design choice for scalable quantum-system calibration.

quant-ph

Transducer leakage error suppression using invariant-based shortcut

We present a method for suppressing transducer leakage errors in spin-superconducting hybrid quantum systems with the theory of optimal invariant-based shortcut. By mediated virtual photons as a transducer to exchange the energy between the spin qubit and a transmon qubit, the fidelity of the population of the final state features a broad range above 99\% under the influence of leakage error. The leakage probability from computational subspace to non-computational subspace can be effectively suppressed at a lowest value with $0.01$. Based on the optimized pulse control designed by the invariant-based inverse engineering, the high-fidelity quantum iSWAP gate operations and entanglement state preparation within the computational subspace are achieved. {Compared to the traditional $\pi$ pulse, derivative removal by adiabatic gate, counter-diabatic shortcut schemes, and limited-memory Broyden-Fletcher-Goldfarb-Shanno gradient ascent pulse engineering, the optimized shortcut scheme can still achieve a high fidelity with 99\% in the presence of decoherence and control error.} When taking into account the possibility of leakage errors in actual situations, our solution can still largely resist the influence of control errors. The results provides a feasible path for precisely manipulating the quantum state of hybrid quantum systems.

quant-ph

Separate Control of Transient Leakage Exposure and Endpoint Leakage in Fast Transmon Gates

Leakage to noncomputational states limits the speed of single-qubit gates in weakly anharmonic transmons. Conventional pulse-shaping methods, including derivative removal by adiabatic gate (DRAG), primarily suppress the leakage remaining at the end of a gate. Here we demonstrate that endpoint leakage and the transient leakage population that accumulates during the gate represent distinct control objectives. Endpoint leakage is associated with the drive spectrum at the anharmonicity, whereas transient exposure depends on spectral weight over a finite frequency band and governs the additional leakage induced by dephasing. A spectral null at the leakage transition therefore suppresses the endpoint amplitude without necessarily reducing transient exposure. Based on this distinction, we introduce a path--endpoint separation pulse that combines transient-path shaping with a two-tone endpoint correction. For a $10$ ns $R_X(\pi/2)$ gate with an anharmonicity magnitude of $0.2$ GHz, numerical simulations show a $21\%$ reduction in transient exposure relative to cosine DRAG and a corresponding $20\%$ reduction in dephasing-induced excess leakage. The two correction tones further suppress residual leakage through the $|2\rangle$ and $|3\rangle$ channels, lowering the coherent endpoint leakage from approximately $7\times10^{-7}$ to $3\times10^{-8}$ without increasing transient exposure. These results establish transient exposure and endpoint leakage as complementary targets for the design of fast transmon gates.

quant-ph

HI-HCQC: A Tightly-Coupled Hardware Interface with High-Efficiency Communication for Hybrid Classical-Quantum Computing

Hybrid classical-quantum computing requires frequent data exchange between classical processors and quantum control hardware. However, existing superconducting quantum control systems are commonly connected through loosely coupled interfaces such as Ethernet, resulting in high communication latency and limited task throughput. To address this issue, we present HI-HCQC, an RFSoC-based hardware interface for tightly coupled hybrid classical-quantum computing. HI-HCQC integrates high-speed RF-DACs, RF-ADCs, programmable logic, embedded processors, clock synchronization circuits, and a PCIe Gen3 x8 interface, enabling direct microwave pulse synthesis, qubit readout, and high-throughput data transfer between host servers and quantum measurement-control units. Experimental results show that HI-HCQC supports six control channels and one multiplexed readout channel, achieves stable microwave generation and acquisition, and successfully performs qubit spectroscopy, Rabi oscillation, T1 measurement, single-shot readout, randomized benchmarking, and CZ-gate characterization. Compared with a conventional control system, HI-HCQC reduces end-to-end execution latency for representative quantum gate and circuit tasks and significantly improves task throughput. These results demonstrate that PCIe-coupled RFSoC control hardware provides a practical foundation for scalable and efficient hybrid classical-quantum computing systems.

cs.DC

Raw-Curve Quantum Fingerprints: A Mahalanobis Authentication Framework with Drift Early Warning and Adversarial Detection

Quantum cloud platforms are poised to deliver powerful computing capabilities, but users have no direct means to verify which physical device executes their workload. This lack of transparency enables hardware substitution attacks, where a malicious adversary could redirect a job to a substituted or inferior processor. We present a general authentication framework that addresses this problem by constructing multi-dimensional quantum fingerprints from raw measurement data. Without any curve fitting, we directly concatenate the raw statistics of complementary experiments into a high-dimensional feature vector that preserves subtle device-specific information. A Mahalanobis nearest-neighbor classifier achieves 100\% benign authentication accuracy on three superconducting processors over a three-week chronological split. The classifier naturally yields an authentication confidence $C_{\mathrm{claimed}}$ which reveals device-specific safety margins and motivates per-device alert thresholds. We assess the framework's robustness under two distinct scenarios. Under additive isotropic Gaussian noise, $C_{\mathrm{claimed}}$ decays predictably at a rate explained by inverse covariance traces, enabling an early warning mechanism. Against white-box adversarial perturbations, the same confidence threshold detects $L_2$ targeted attacks with near-perfect success and reveals device-dependent empirical thresholds for $L_\infty$ attacks, while untargeted and sparse attacks are ineffective. The proposed framework thus unifies fingerprint extraction, drift-resilient authentication, proactive health monitoring, and adversarial defense, offering a practical step toward trustworthy quantum cloud computing.

quant-ph

Modeling and Resource Optimization for Quantum Oracles

Quantum oracles are fundamental building blocks of many quantum algorithms, and their resource consumption directly affects performance, yet structured description and complexity analysis for their composition are still lacking. In this paper, we introduce the Framework for Oracle Recursion Modeling (FORM), a unified formal abstraction of multi-function composition in quantum oracles: it provides a structured description of the composition layer, makes its gate complexity exactly computable, and turns oracle design into an optimizable tree-construction problem. Based on this model, we propose the ShallowGrow algorithm, which constructs an oracle structure under a given ancilla budget and provably minimizes the number of function evaluations. On Boolean quadratic equation systems, ShallowGrow reduces Qiskit-measured circuit depth by 54.1% on average relative to the state-of-the-art W-cycle construction, with consistent reductions on the EPFL and ISCAS85 combinational logic networks under scarce ancilla budgets. Furthermore, pebbling-based syntheses trade space against time within the logic network of a function; ShallowGrow extends this trade-off across functions, and integrated with their published circuits it reduces the ancillary qubits of a complete oracle from one per constraint function to logarithmically many. With half as many ancillas as constraint functions, the T-count falls by a factor of 7.9 to 32 relative to the W-cycle-based construction.

quant-ph

QLLVM: A Scalable Quantum-Classical Co-Compilation Framework based on LLVM

To address the urgent need in the NISQ era for high-performance, scalable quantum compilers and to advance the integration of classical and quantum computing, we present QLLVM, an advanced Quantum-Classical co-compilation framework built on LLVM. To our knowledge, QLLVM delivers an end-to-end, LLVM-based compilation workflow that unifies the build of classical high-performance programs, including CUDA, MPI, and C++, together with quantum programs into a single executable. For quantum program compilation, QLLVM adopts a three-stage design: high-level optimizations are implemented in the MLIR Quantum dialect and then lowered to QIR, an LLVM IR-based representation, for low-level optimization and hardware mapping. Its extensible architecture and seamless interoperability with classical high-performance computing provide an efficient, flexible, industrial-grade compilation infrastructure for future quantum software development. Experimental results show that, on the MQTBench benchmark suite, QLLVM reduces circuit depth and gate counts compared with state-of-the-art compilers and demonstrates clear advantages in compiling hybrid classical-quantum programs.

quant-ph

MPI-Q: A Message Communication Library for Large-Scale Classical-Quantum Heterogeneous Hybrid Distributed Computing

The classical-quantum system heterogeneity (different data characteristics, execution paradigms and synchronization mechanism etc.) renders existing distributed communication mechanisms (e.g. MPI, NCCL etc.) inadequate. This bottleneck severely impairs operational synergy and programming efficiency. Thus, the performance of hybrid applications on classical-quantum heterogeneous infrastructures is directly limited. To address these challenges, this paper proposes a message-passing library tailored for large-scale classical-quantum heterogeneous distributed computing, referred to as MPI-Q. The design centers on three mechanisms. First, it defines a heterogeneous hybrid communication domain that achieves unified management of classical and quantum processes in heterogeneous hybrid systems. Second, it uses a lightweight communication path that allows classical control nodes to send device-ready waveform data directly to quantum MonitorProcesses, avoiding unnecessary relay stages. Third, it establishes a heterogeneous hybrid synchronization mechanism to tackle the problem of timing control for multi-node quantum operations. While retaining the traditional MPI programming model, MPI-Q achieves extension toward quantum subsystems. Experiments on distributed GHZ state preparation demonstrate that this model exhibits near-linear scalability, achieving a maximum speedup of 18.76 times on 24 quantum nodes. This proves that the library can effectively support large-scale heterogeneous hybrid distributed computing applications, filling the technical gap in this field.

cs.DC

Tunable Nonlocal $ZZ$ Interaction for Remote Controlled-Z Gates Between Distributed Fixed-Frequency Qubits

Scaling superconducting quantum processors toward fault-tolerant operation will likely require architectures that extend beyond monolithic chips. Modular processors connected by low-loss superconducting links provide a promising route, but implementing entangling gates between remote fixed-frequency qubits remains challenging. Here we propose a distributed architecture in which two synchronously controlled double-transmon couplers mediate the interaction between fixed-frequency transmons in separate packages connected by a 25-cm coaxial cable. The scheme activates a tunable nonlocal $ZZ$ interaction on demand while suppressing residual static coupling, allowing the superconducting link to function as a gate-native interconnect rather than solely as a state-transfer channel. Circuit-level simulations show an on/off ratio exceeding $10^6$ and a remote controlled-Z gate with a projected coherent fidelity of $99.99\%$ under experimentally relevant parameters. Open-system simulations further indicate that, within the representative Markovian noise model considered here, endpoint-qubit decoherence is the largest contribution to gate infidelity, while photon loss in the retained cable modes remains smaller but non-negligible. These results identify DTC-mediated tunable nonlocal coupling as a promising gate primitive for modular superconducting processors based on fixed-frequency qubits.

quant-ph

Fast CZ Gate via Energy-Level Engineering in Superconducting Qubits with a Tunable Coupler

In superconducting quantum circuits, decoherence errors in qubits constitute a critical factor limiting quantum gate performance. To mitigate decoherence-induced gate infidelity, rapid implementation of quantum gates is essential. Here we propose a scheme for rapid controlled-Z (CZ) gate implementation through energy-level engineering, which leverages Rabi oscillations between the $\left|11\right\rangle$ state and the non-computational state in a tunable-coupler architecture. Numerical simulations achieved a $\mathrm{22~ns}$ nonadiabatic CZ gate with fidelity over $99.99\%$. We further investigated the performance of the CZ gate in the presence of anharmonicity offsets. The results demonstrate that a high-fidelity CZ gate with an error rate below $10^{-4}$ remains achievable even with finite anharmonicity variations. Furthermore, the detrimental impact of spectator qubits in different quantum states on the fidelity of CZ gate is effectively suppressed by incorporating a tunable coupler. This scheme exhibits potential for extending the circuit execution depth constrained by coherence time limitations.

quant-ph

Time-frequency-correlated Native CCZ Gate in Superconducting Circuits

Practical quantum advantage hinges on executing deep quantum circuits within the coherence limits of noisy intermediate-scale quantum processors. The absence of native, high-fidelity multi-qubit gates remains a major bottleneck, as their decomposition into single- and two-qubit gates leads to prohibitive depth and error overhead. Here, we propose a hardware-efficient protocol that directly implements a native controlled-controlled-Z (CCZ) gate in a tunable-coupler superconducting circuit. Our theoretical protocol activates a resonant three-qubit interaction via a time-frequency correlated virtual process, explicitly relying on the dynamic resonant exchange within the $|101\rangle \leftrightarrow |020\rangle$ transition manifold. This approach is compatible with standard tunable-coupler architectures without requiring additional control resources. Through a systematic calibration workflow combining pulse shaping and active cancellation of residual phases, we demonstrate a gate fidelity exceeding 99\% within $165\,\mathrm{ns}$ -- significantly outperforming decomposed sequences. Comprehensive error budgeting confirms that the gate performance remains robust against realistic experimental imperfections. Furthermore, we show that this scheme can be naturally extended to a continuous $\mathrm{CCPhase}(\theta)$ gate set. This work provides a direct, high-fidelity route to three-qubit entanglement, offering promising prospects for efficient execution of quantum algorithms on near-term superconducting hardware.

quant-ph

EDA-Q: Electronic Design Automation for Superconducting Quantum Chip

Electronic Design Automation (EDA) plays a crucial role in classical chip design and significantly influences the development of quantum chip design. However, traditional EDA tools cannot be directly applied to quantum chip design due to vast differences compared to the classical realm. Several EDA products tailored for quantum chip design currently exist, yet they only cover partial stages of the quantum chip design process instead of offering a fully comprehensive solution. Additionally, they often encounter issues such as limited automation, steep learning curves, challenges in integrating with actual fabrication processes, and difficulties in expanding functionality. To address these issues, we developed a full-stack EDA tool specifically for quantum chip design, called EDA-Q. The design workflow incorporates functionalities present in existing quantum EDA tools while supplementing critical design stages such as device mapping and fabrication process mapping, which users expect. EDA-Q utilizes a unique architecture to achieve exceptional scalability and flexibility. The integrated design mode guarantees algorithm compatibility with different chip components, while employing a specialized interactive processing mode to offer users a straightforward and adaptable command interface. Application examples demonstrate that EDA-Q significantly reduces chip design cycles, enhances automation levels, and decreases the time required for manual intervention. Multiple rounds of testing on the designed chip have validated the effectiveness of EDA-Q in practical applications.

cs.ET

One-step implementation of nonadiabatic geometric fSim gate in superconducting circuits

Due to its significant application in reducing algorithm depth, fSim gates have attracted a lot of attention. However, during the implementation of quantum gates, fluctuations in control parameters and decoherence caused by the environment may lead to a decrease in the fidelity of the gate. Implementing the fSim gate that is robust to these factors in one step remains an unresolved issue. In this manuscript, we propose a one-step implementation of the nonadiabatic geometric fSim gate composed of a nonadiabatic holonomic controlled phase (CP) gate and a nonadiabatic noncyclic geometric iSWAP gate with parallel paths in a tunable superconducting circuit. Compared to the composite nonadiabatic geometric fSim gate composed of a nonadiabatic holonomic CP gate and a nonadiabatic geometric iSWAP gate, our scheme only takes half the time and demonstrates robustness to parameter fluctuations, as well as to environmental impacts. Moreover, the scheme does not require complex controls, making it very easy to implement in experiments, and can be achieved in various circuit structures. Our scheme may provide a promising path toward quantum computation and simulation.

quant-ph

Factoring integers with sublinear resources on a superconducting quantum processor

Shor's algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglog N), which is sublinear in the bit length of the integer $N$, making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance.

quant-ph

A Fast Matrix-Completion-Based Approach for Recommendation Systems

Matrix completion is widely used in machine learning, engineering control, image processing, and recommendation systems. Currently, a popular algorithm for matrix completion is Singular Value Threshold (SVT). In this algorithm, the singular value threshold should be set first. However, in a recommendation system, the dimension of the preference matrix keeps changing. Therefore, it is difficult to directly apply SVT. In addition, what the users of a recommendation system need is a sequence of personalized recommended results rather than the estimation of their scores. According to the above ideas, this paper proposes a novel approach named probability completion model~(PCM). By reducing the data dimension, the transitivity of the similar matrix, and singular value decomposition, this approach quickly obtains a completion matrix with the same probability distribution as the original matrix. The approach greatly reduces the computation time based on the accuracy of the sacrifice part, and can quickly obtain a low-rank similarity matrix with data trend approximation properties. The experimental results show that PCM can quickly generate a complementary matrix with similar data trends as the original matrix. The LCS score and efficiency of PCM are both higher than SVT.

cs.IR

Multifractal characteristics and return predictability in the Chinese stock markets

By adopting Multifractal detrended fluctuation (MF-DFA) analysis methods, the multifractal nature is revealed in the high-frequency data of two typical indexes, the Shanghai Stock Exchange Composite 180 Index (SH180) and the Shenzhen Stock Exchange Composite Index (SZCI). The characteristics of the corresponding multifractal spectra are defined as a measurement of market volatility. It is found that there is a statistically significant relationship between the stock index returns and the spectral characteristics, which can be applied to forecast the future market return. The in-sample and out-of-sample tests on the return predictability of multifractal characteristics indicate the spectral width $Δα$ is a significant and positive excess return predictor. Our results shed new lights on the application of multifractal nature in asset pricing.

q-fin.ST