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Tuesday 2 December

Quantum Physics

Observation of non-Hermitian many-body phase transition in a Rydberg-atom array

Yao-Wen Zhang, Biao Xu, Yijia Zhou et al. (+8 more)

Non-Hermitian quantum mechanics with parity-time (PT) symmetry offers a powerful framework for exploring the complex interplay of dissipation and coherent interactions in open quantum systems. While PT-symmetry breaking has been studied in various physical systems, its observation on a quantum many-body level remains elusive. Here, we experimentally realize a non-Hermitian XY model in a strongly-interacting Rydberg-atom array. By measuring the Loschmidt Echo of a fully polarized state, we observe distinct dynamical signatures of a PT-symmetry-breaking phase transition. Dipole interactions are found to play a crucial role, not only determining the transition point but also triggering a non-Hermitian many-body blockade effect that protects the Loschmidt Echo from decay with a non-monotonic dependence on the system size. Our results reveal intricate interaction-induced effects on PT-symmetry breaking and open the door for exploring non-Hermitian many-body dynamics beyond single-particle and mean-field paradigms.
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All authors Yao-Wen Zhang, Biao Xu, Yijia Zhou, De-Sheng Xiang, Hao-Xiang Liu, Peng Zhou, Kuan Zhang, Ren Liao, Thomas Pohl, Weibin Li, Lin Li

Quantum Physics

Minimal decomposition entropy and optimal representations of absolutely maximally entangled states

N Ramadas

Understanding and classifying multipartite entanglement is fundamental to quantum information processing. This work focuses on absolutely maximally entangled (AME) states, a class of highly entangled states characterized by their maximal entanglement across any bipartitions. To analyze and classify AME states, we employ the minimal decomposition entropy, defined as the minimum Rényi entropy $S_q$ associated with the state's decomposition over all local product bases. This quantity identifies the product bases in which the state is maximally localized, thereby yielding optimal representations for analyzing properties of AME states. We develop an efficient algorithm for computing the minimal decomposition entropy for finite $q>1$ and compare AME and Haar-random states for \( q = 2 \) and \( q = \infty \) in qubit, qutrit, and ququad systems. For \( q = 2 \), AME states of four qutrits and ququads show lower minimal entropy than generic states, indicating sparser optimal forms. For \( q = \infty \) – related to the geometric measure of entanglement – AME states exhibit higher entanglement. The algorithm also simplifies known AME states into sparser representations, aiding in distinguishing genuinely quantum AME states from those constructible from classical combinatorial designs. Our results advance the classification of AME states and demonstrate the utility of minimal decomposition entropy as both a local unitary invariant and a tool for state simplification.
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Quantum Physics

Dynamic Modulation of Long Range Photon Magnon Coupling

Alban Joseph, Mawgan A. Smith, Martin P. Weides et al. (+1 more)

Evidence of non-hermitian behavior has been recently demonstrated in cavity magnonics, including the emergence of mode level attraction and exceptional points in spectroscopic measurements. This work demonstrates experimental evidence of time-domain dynamics of magnon-photon systems that are coupled through a long-range interaction (i.e. remote coupling) exhibiting level attraction mediated by an auxiliary mode. We directly observe the temporal evolution of dissipatively coupled cavity-magnon modes, where heavily damped transmission line modes mediate the interaction. Our frequency-domain measurements confirm the predicted level attraction, while time-domain ring-down measurements reveal the characteristic signatures of dissipative coupling dynamics. Our approach offers in situ tunability over the dissipative coupling strength, including complete suppression, without requiring physical modifications to the experimental setup, providing a versatile platform for exploring tunable, non-Hermitian physics.
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All authors Alban Joseph, Mawgan A. Smith, Martin P. Weides, Rair Macêdo

Quantum Physics

Generative modeling using evolved quantum Boltzmann machines

Mark M. Wilde

Born-rule generative modeling, a central task in quantum machine learning, seeks to learn probability distributions that can be efficiently sampled by measuring complex quantum states. One hope is for quantum models to efficiently capture probability distributions that are difficult to learn and simulate by classical means alone. Quantum Boltzmann machines were proposed about one decade ago for this purpose, yet efficient training methods have remained elusive. In this paper, I overcome this obstacle by proposing a practical solution that trains quantum Boltzmann machines for Born-rule generative modeling. Two key ingredients in the proposal are the Donsker-Varadhan variational representation of the classical relative entropy and the quantum Boltzmann gradient estimator of [Patel et al., arXiv:2410.12935]. I present the main result for a more general ansatz known as an evolved quantum Boltzmann machine [Minervini et al., arXiv:2501.03367], which combines parameterized real- and imaginary-time evolution. I also show how to extend the findings to other distinguishability measures beyond relative entropy. Finally, I present four different hybrid quantum-classical algorithms for the minimax optimization underlying training, and I discuss their theoretical convergence guarantees.
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Quantum Physics · Cross-listed from cond-mat.stat-mech

Detection of Mpemba effect through good observables in open quantum systems

Pitambar Bagui, Arijit Chatterjee and Bijay Kumar Agarwalla

The Mpemba effect refers to the anomalous relaxation of a quantum state that, despite being initially farther from equilibrium, relaxes faster than a closer counterpart. Detecting such a quantum Mpemba effect typically requires full knowledge of the quantum state during its time evolution, which is an experimentally challenging task since state tomography becomes exponentially difficult as system size increases. This poses a significant obstacle in studying Mpemba effect in complex many-body systems. In this work, we demonstrate that this limitation can be overcome by identifying suitable observables that signal rapid relaxation. Moreover, as long as the system equilibrates to a known unique steady-state, it is possible to fully detect the occurrence of quantum Mpemba effect just by measuring the observable for known state preparations. Our approach thus significantly reduces experimental complexity and offers a practical route for observing the quantum Mpemba effect in complex and extended multi-qubit setups.
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Quantum Physics

Lectures on Quantum Field Theory on a Quantum Computer

Aninda Sinha and Ujjwal Basumatary

The lecture notes cover the basics of quantum computing methods for quantum field theory applications. No detailed knowledge of either quantum computing or quantum field theory is assumed and we have attempted to keep the material at a pedagogical level. We review the anharmonic oscillator, using which we develop a hands-on treatment of certain interesting QFTs in $1+1D$: $\phi^4$ theory, Ising field theory, and the Schwinger model. We review quantum computing essentials as well as tensor network techniques. The latter form an essential part for quantum computing benchmarking. Some error modelling on QISKIT is also done in the hope of anticipating runs on NISQ devices. These lecture notes are the expanded version of a one semester course taught by AS during August-November 2025 at the Indian Institute of Science and TA-ed by UB. The programs written for this course are available in a GitHub repository.
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Quantum Physics

Multi-node quantum key distribution network using existing underground optical fibre infrastructure

Mariella Minder, Andreas Siakolas, Stephanos Yerolatsitis et al. (+2 more)

Quantum key distribution (QKD) offers unconditional information security by allowing two distant users to establish a common encryption key resilient to hacking. Resultingly, QKD networks interconnecting critical infrastructure and enabling the secure exchange of classified information, can provide a solution to the increasing number of successful cyberattacks. To efficiently deploy quantum networks, the technology must be integrated over existing communication infrastructure, such as optical fibre links. Yet, QKD poses stringent requirements on the conditions of the network over which it is deployed. This work demonstrates the first quantum communication network in Cyprus via the deployment of a multi-node quantum network, exploiting existing commercial underground optical fibre. The network employs bidirectional occupation of fibres and wavelength multiplexing in a ring architecture to achieve, with minimal use of dark fibres, high-rate QKD. Results obtained reveal consistent key generation rates across all nodes, confirming reliable operation in a real-world environment. This deployment highlights the feasibility of leveraging existing telecom infrastructure for quantum-secured communication, marking a significant step toward scalable and cost-effective quantum networks suited for critical applications.
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All authors Mariella Minder, Andreas Siakolas, Stephanos Yerolatsitis, Konstantinos Katzis, Kyriacos Kalli

Quantum Physics · Cross-listed from cond-mat.mtrl-sci

Rational regulation strategies of interstitial localized electrons in electride: A density functional theory study

L. Zhang, D. Wang, H. Wang et al. (+4 more)

As a class of electron-rich materials, electrides demonstrate promising applications in many fields. However, the required high pressure restricts the practical applications to some extent. This study reveals that the unique feature of electride, i.e., the localization of interstitial electrons, can be greatly enhanced and tuned by self-defective doping, applying tensile/compressive stress, or shear stress. Moreover, the requirement of orbital orthogonality between the valence and core electron wave functions, as well as the Pauli exclusion principle, should be the driven force for the electron interstitial localization; and the exertion of external pressure modifies the available space to accommodate the electronic wave functions, thus enhances the interstitial localization. These discoveries lay down the ground for searching for promising electrides that are practicable at ambient conditions.
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All authors L. Zhang, D. Wang, H. Wang, J. Li, Y. F. Wang, Q. Wu, Hua Y. Geng

Quantum Physics

Efficient Simulation of the 2D Hubbard Model via Hilbert Space-Filling Curve Mapping

Ashkan Abedi, Vittorio Giovannetti and Dario De Santis

We investigate tensor network simulations of the two-dimensional Hubbard model by mapping the lattice onto a one-dimensional chain using space-filling curves. In particular, we focus on the Hilbert curve, whose locality-preserving structure minimizes the range of effective interactions in the mapped model. This enables a more compact matrix product state (MPS) representation compared to conventional snake mapping. Through systematic benchmarks, we show that the Hilbert curve consistently yields lower ground-state energies at fixed bond dimension, with the advantage increasing for larger system sizes and in physically relevant interaction regimes. Our implementation reaches clusters up to $32\times32$ sites with open and periodic boundary conditions, delivering reliable ground-state energies and correlation functions in agreement with established results, but at significantly reduced computational cost. These findings establish space-filling curve mappings, particularly the Hilbert curve, as a powerful tool for extending tensor-network studies of strongly correlated two-dimensional quantum systems beyond the limits accessible with standard approaches.
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Quantum Physics

Chiplet technology for large-scale trapped-ion quantum processors

Bassem Badawi, Philip C. Holz, Michael Raffetseder et al. (+6 more)

Trapped ions are among the most promising platforms for realizing a large-scale quantum information processor. Current progress focuses on integrating optical and electronic components into microfabricated ion traps to allow scaling to large numbers of ion qubits. Most available fabrication strategies for such integrated processors employ monolithic integration of all processor components and rely heavily on CMOS-compatible semiconductor fabrication technologies that are not optimized for the requirements of a trapped-ion quantum processor. In this work, we present a modular approach in which the processor modules, called chiplets, have specific functions and are fabricated separately. The individual chiplets are then combined using heterogeneous integration techniques. This strategy opens up the possibility of choosing the optimal materials and fabrication technology for each of the chiplets, with a minimum amount of fabrication limitations compared to the monolithic approach. Chiplet technology furthermore enables novel processor functionalities to be added in a cost-effective, modular fashion by adding or modifying only a subset of the chiplets. We describe the design concept of a chiplet-based trapped-ion quantum processor and demonstrate the technology with an example of an integrated individual-ion addressing system for a ten-ion crystal. The addressing system emphasizes the modularity of the chiplet approach, combining a surface ion trap manufactured on a glass substrate with a silicon substrate carrying integrated waveguides and a stack of 3D-printed micro-optics, achieving diffraction-limited focal spots at the ion positions.
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All authors Bassem Badawi, Philip C. Holz, Michael Raffetseder, Nicolas Jungwirth, Juris Ulmanis, Hans-Joachim Quenzer, Dirk Kähler, Thomas Monz, Philipp Schindler

Quantum Physics

High-harmonic generation from two weakly coupled molecules: a simple tight-binding model

Lina Bielke, Samuel Schöpa, Falk-Erik Wiechmann et al. (+2 more)

The generation of high harmonics is a strongly nonlinear effect that allows to probe properties of the target and to study electron dynamics in matter. It has been investigated in many different kinds of targets, including molecular gases, liquids and solids. Recently, high-harmonic generation was studied in organic molecular crystals by Wiechmann et al. [Nat. Commun. 16, 9890 (2025)]. It was found that the laser-polarization-dependent harmonic yield is sensitive to the weak couplings between nearest- and next-nearest-neighbor molecules. In this paper, the impact of the laser polarization angle and the intermolecular interaction on the harmonic yield is examined in detail using a simple but insightful two-dimensional tight-binding system that models a molecular dimer, i.e. two weakly coupled molecules. We find that the intensities of lower harmonic orders tend to maximize for a laser polarization direction aligning with the molecular axes, whereas higher harmonic orders rather show the strongest yield for a polarization direction along the intermolecular axis. We further demonstrate that the harmonic order at which the maximum flips from the molecular to the intermolecular direction strongly depends on the intermolecular coupling strength. To gain a deeper insight into the origins of the findings, we include a detailed adiabatic analysis, showing that the flipping of the maximum yield towards the intermolecular direction is already contained qualitatively in the adiabatically following states.
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All authors Lina Bielke, Samuel Schöpa, Falk-Erik Wiechmann, Franziska Fennel, Dieter Bauer

Quantum Physics

Quantum LLMs Using Quantum Computing to Analyze and Process Semantic Information

Timo Aukusti Laine

We present a quantum computing approach to analyzing Large Language Model (LLM) embeddings, leveraging complex-valued representations and modeling semantic relationships using quantum mechanical principles. By establishing a direct mapping between LLM semantic spaces and quantum circuits, we demonstrate the feasibility of estimating semantic similarity using quantum hardware. One of the key results is the experimental calculation of cosine similarity between Google Sentence Transformer embeddings using a real quantum computer, providing a tangible demonstration of a quantum approach to semantic analysis. This work reveals a connection between LLMs and quantum mechanics, suggesting that these principles can offer new perspectives on semantic representation and processing, and paving the way for future development of quantum algorithms for natural language processing.
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Quantum Physics

A unified optical platform for non-Gaussian and fault-tolerant Gottesman-Kitaev-Preskill states

Ozlem Erkilic, Aritra Das, Biveen Shajilal et al. (+3 more)

Quantum technologies, encompassing communication, computation, and metrology, rely on the generation and control of non-Gaussian states of light. These states enable secure quantum communication, fault-tolerant quantum computation, and precision sensing beyond classical limits, yet their practical realisation remains a major challenge due to reliance on high-photon-number Fock states or strong non-linearities. Here we introduce a unified optical framework that removes this constraint, using only Gaussian inputs, optical parametric amplification, and heralded photon detection. Within a single architecture, we demonstrate the generation of photon-added squeezed states with near unit fidelity, cubic-phase-like states with strong non-linearities and fidelities above 98.5%, and squeezed-cat states exceeding 99% fidelity that can be iteratively bred into GKP grid states surpassing the 9.75 dB fault-tolerance threshold. Operating entirely below 3 dB of input squeezing, the approach provides a scalable, experimentally accessible platform that unites the state resources required for quantum communication, metrology, and computation within one coherent optical framework.
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All authors Ozlem Erkilic, Aritra Das, Biveen Shajilal, Ping Koy Lam, Timothy C. Ralph, Syed M. Assad

Quantum Physics

Constraint-Optimal Driven Allocation for Scalable QEC Decoder Scheduling

Dongmin Kim, Jeonggeun Seo, Yongtae Kim et al. (+1 more)

Fault-tolerant quantum computing (FTQC) requires fast and accurate decoding of Quantum Error Correction (QEC) syndromes. However, in large-scale systems, the number of available decoders is much smaller than the number of logical qubits, leading to a fundamental resource shortage. To address this limitation, Virtualized Quantum Decoder (VQD) architectures have been proposed to share a limited pool of decoders across multiple qubits. While the Minimize Longest Undecoded Sequence (MLS) heuristic has been introduced as an effective scheduling policy within the VQD framework, its locally greedy decision-making structure limits its ability to consider global circuit structure, causing inefficiencies in resource balancing and limited scalability. In this work, we propose Constraint-Optimal Driven Allocation (CODA), an optimization-based scheduling algorithm that leverages global circuit structure to minimize the longest undecoded sequence length. Across 19 benchmark circuits, CODA achieves an average 74% reduction in the longest undecoded sequence length. Crucially, while the theoretical search space scales exponentially with circuit size, CODA effectively bypasses this combinatorial explosion. Our evaluation confirms that the scheduling time scales linearly with the number of qubits, determined by physical resource constraints rather than the combinatorial search space, ensuring robust scalability for large-scale FTQC systems. These results demonstrate that CODA provides a global optimization-based, scalable scheduling solution that enables efficient decoder virtualization in large-scale FTQC systems.
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All authors Dongmin Kim, Jeonggeun Seo, Yongtae Kim, Youngsun Han

Quantum Physics

Full-counting statistics and quantum information of dispersive readout with a squeezed environment

Ming Li, JunYan Luo, Gloria Platero et al. (+1 more)

Motivated by the importance of dispersive readout in quantum technology, we study a prototypical dispersive readout setup that is probed by a squeezed vacuum in a time-reversal-symmetric fashion. To this end, we develop a full-counting-statistics framework for dispersive readout and analyze its measurement information, accompanied by a generalized mean-field approach suitable to deal with non-unitary dynamics. Distinct from conventional input-output theory, our full-counting-statistics approach enables the direct calculation of arbitrary-order cumulants for the measured cumulative (i.e., time-integrated) photonic distribution while maintaining applicability to nonlinear systems. The corresponding Fisher information exhibits an exponential dependence on the squeezing parameter and a robustness against residual nonlinearity, which can even approach the quantum Fisher information, setting an upper limit. This work introduces a conceptually streamlined and computationally efficient framework for continuous quantum measurements, making it well suited for widespread adoption in quantum technologies.
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All authors Ming Li, JunYan Luo, Gloria Platero, Georg Engelhardt

Quantum Physics

Detection of photon-level signals embedded in sunlight with an atomic photodetector

Laura Zarraoa, Tomas Lamich, Sondos Elsehimy et al. (+2 more)

The detection of few-photon signals in a broadband background is an extreme challenge for photon counting, requiring filtering that accepts a narrow range of optical frequencies while strongly rejecting all others. Recent work [Zarraoa et. al, Phys. Rev. Res. 6, 033338 (2024)] demonstrated that trapped single atoms can act as low dark-count narrow-band photodetectors. Here we show that this “quantum jump photodetector” (QJPD) approach can also detect photon-level signals embedded in strong sunlight. Using a single rubidium atom as a QJPD, we count arrivals of individual narrow-band laser photons embedded in sunlight powers of order $10^{10}$ photons/s. We derive a rate-equation model for the atom's internal-state dynamics in sunlight, and find quantitative agreement with experiment. Using this model, we calculate the channel capacity over a noisy communication channel when sending weak coherent states and detecting them in the presence of sunlight, achieving a representative rate of 0.5 bits per symbol when sending 150 probe photons per 10 ms time-bin, embedded in 1 nW of sunlight (of order $10^{10}$ photons/s in the visible and near-infrared bands). The demonstration may benefit background-limited applications such as daytime light detection and ranging (LIDAR), remote magnetometry, and free-space classical and quantum optical communications.
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All authors Laura Zarraoa, Tomas Lamich, Sondos Elsehimy, Morgan W. Mitchell, Romain Veyron

Quantum Physics

Improved Ising Meson Spectroscopy Simulation on a Noisy Digital Quantum Device

Hao-Ti Hung, Isabel Nha Minh Le, Johannes Knolle et al. (+1 more)

The transverse-field Ising model serves as a paradigm for studying confinement and excitation spectra, particularly the emergence of $E_8$ symmetry near criticality. However, experimentally resolving the Ising meson spectroscopy required to verify these symmetries is challenging on near-term quantum hardware due to the depth of circuits required for real-time evolution. Here, we demonstrate improved spectroscopy of confined excitations using two distinct error-resilient circuit construction techniques on the IBM Torino device: first-order Trotter decomposition utilizing native fractional gates, and a tensor-network-based circuit compression via Riemannian optimization. By analyzing the Fourier spectrum of error-mitigated time-series data, we successfully identify key signatures of $E_8$ symmetry despite hardware noise. These results validate the viability of both circuit compression and hardware-efficient compilation for probing complex topological phenomena on NISQ devices.
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All authors Hao-Ti Hung, Isabel Nha Minh Le, Johannes Knolle, Ying-Jer Kao

Quantum Physics

Superchannel without Tears: A Generalized Occam's Razor for Quantum Processes

Yunlong Xiao

Quantum channels function as the operational primitives of quantum theory, while superchannels describe the most general transformations acting upon them. Yet the prevailing framework for superchannels is both internally inconsistent, owing to the coexistence of distinct Choi operator constructions, and structurally incomplete, lacking the analogue of representations that ground channel theory. We resolve these issues by combining tensor-network methods with a generalized Occam's razor introduced here, establishing a unified foundation for superchannels. Our framework establishes the connections between competing Choi formulations, develops the Kraus, Stinespring, and Liouville representations for superchannels, and provides a simplified derivation of the realization theorem that identifies the minimal memory required to implement a given transformation. These structural tools also enable characterizations of superchannels that destroy quantum correlations or causal structure, opening a systematic route to non-Markovian quantum dynamics.
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Quantum Physics

Qudits offer no advantages over dits for sending random messages

Ronit Shah

We consider the following simple scenario: Alice has one of many possible messages, drawn from a known distribution, and wants to maximize the probability that Bob guesses her message correctly. We prove that if Alice can send only a qudit to Bob, without preshared entanglement, there is never any advantage over sending him a classical dit. This result was previously known only for a uniform distribution. We also prove a mixed-state generalization of this result in the form of an upper bound on the success probability of discriminating between mixed quantum states with a single measurement. This bound is based solely on the dimension, probability distribution, and eigenvalues of the states and is sharp among such bounds.
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Quantum Physics

Quantum-Based Self-Attention Mechanism for Hardware-Aware Differentiable Quantum Architecture Search

Yuxiang Liu, Sixuan Li, Fanxu Meng et al. (+2 more)

The automated design of parameterized quantum circuits for variational algorithms in the NISQ era faces a fundamental limitation, as conventional differentiable architecture search relies on classical models that fail to adequately represent quantum gate interactions under hardware noise. We introduce the Quantum-Based Self-Attention for Differentiable Quantum Architecture Search (QBSA-DQAS), a meta-learning framework featuring quantum-based self-attention and hardware-aware multi-objective search. The framework employs a two-stage quantum self-attention module that computes contextual dependencies by mapping architectural parameters through parameterized quantum circuits, replacing classical similarity metrics with quantum-derived attention scores, then applies position-wise quantum transformations for feature enrichment. Architecture search is guided by a task-agnostic multi-objective function jointly optimizing noisy expressibility and Probability of Successful Trials (PST). A post-search optimization stage applies gate commutation, fusion, and elimination to reduce circuit complexity. Experimental validation demonstrates superior performance on VQE tasks and large-scale Wireless Sensor Networks. For VQE on H$_2$, QBSA-DQAS achieves 0.9 accuracy compared to 0.89 for standard DQAS. Post-search optimization reduces discovered circuit complexity by up to 44% in gate count and 47% in depth without accuracy degradation. The framework maintains robust performance across three molecules and five IBM quantum hardware noise models. For WSN routing, discovered circuits achieve 8.6% energy reduction versus QAOA and 40.7% versus classical greedy methods, establishing the effectiveness of quantum-native architecture search for NISQ applications.
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All authors Yuxiang Liu, Sixuan Li, Fanxu Meng, Zaichen Zhang, Xutao Yu

Quantum Physics · Cross-listed from cond-mat.mes-hall

Su-Schrieffer-Heeger model driven by sequences of two unitaries: periodic, quasiperiodic, aperiodic, and random protocols

Maitri Ganguli and Diptiman Sen

We study the effect of driving the Su-Schrieffer-Heeger model using two unitary operators $U_1$ and $U_2$ in different combinations; the unitaries differ in the values of the inter-cell hopping amplitudes. Specifically, we study the cases where the unitaries are applied periodically, quasiperiodically, aperiodically and randomly. For a periodic protocol, when $U_1 = e^{-i H_1 T/2}$ and $U_2 = e^{-i H_2 T/2}$ are applied alternately, we find that end modes may appear, but the number of end modes does not always agree with the winding number which is a $Z$-valued topological invariant. We then study the Loschmidt amplitude ($LA$) starting with a initial state which is an end mode of $H_1$. We find that the $LA$ exhibits pronounced oscillations whose Fourier transform has a peak at a frequency which is equal to the quasienergy of an end mode of $U$. Next, when $U_1$ and $U_2$ are applied in a quasiperiodic or aperiodic way (we consider the Fibonacci and Thue-Morse protocols as examples), we study the Loschmidt echo ($LE$) starting with an initial state which is an end mode of the Hamiltonian $H_1$. When the inter-cell hoppings differ by a small amount denoted by $\epsilon$, and the time period $T$ of each unitary is also small, the distance between the unitaries is found to be proportional to $\epsilon T$. We then find that the $LE$ oscillates around a particular value for a very long time before decaying to zero. The deviation of the value of the $LE$ from 1 scales as $\epsilon^2$ for a fixed value of $T$, while the time after which the $LE$ starts decaying to zero has an interesting dependence on $\epsilon$ and $T$. Finally, when $U_1$ and $U_2$ are applied in a random order, the $LE$ rapidly decays to zero with increasing time. We have presented a qualitative understanding of the above results.
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Quantum Physics

Quantum feature encoding optimization

Tommaso Fioravanti, Brian Quanz, Gabriele Agliardi et al. (+3 more)

Quantum Machine Learning (QML) holds the promise of enhancing machine learning modeling in terms of both complexity and accuracy. A key challenge in this domain is the encoding of input data, which plays a pivotal role in determining the performance of QML models. In this work, we tackle a largely unaddressed aspect of encoding that is unique to QML modeling – rather than adjusting the ansatz used for encoding, we consider adjusting how data is conveyed to the ansatz. We specifically implement QML pipelines that leverage classical data manipulation (i.e., ordering, selecting, and weighting features) as a preprocessing step, and evaluate if these aspects of encoding can have a significant impact on QML model performance, and if they can be effectively optimized to improve performance. Our experimental results, applied across a wide variety of data sets, ansatz, and circuit sizes, with a representative QML approach, demonstrate that by optimizing how features are encoded in an ansatz we can substantially and consistently improve the performance of QML models, making a compelling case for integrating these techniques in future QML applications. Finally we demonstrate the practical feasibility of this approach by running it using real quantum hardware with 100 qubit circuits and successfully achieving improved QML modeling performance in this case as well.
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All authors Tommaso Fioravanti, Brian Quanz, Gabriele Agliardi, Edgar Andres Ruiz Guzman, Ginés Carrascal, Jae-Eun Park

Quantum Physics · Cross-listed from cond-mat.mes-hall

Quantum Coulomb drag signatures of Majorana bound states

Zi-Wei Li, Jiaojiao Chen, Wei Xiong et al. (+2 more)

Majorana bound states (MBSs), with their non-Abelian statistics and topological protection, are key candidates for fault-tolerant quantum computation. However, their unambiguous identification in solid-state systems remains a fundamental challenge. Here, we present a theoretical study demonstrating that drag transport in a capacitively coupled double quantum dot system offers a robust and nonlocal probe of weakly coupled MBSs. Using the master equation approach, we investigate both steady-state and transient dynamics and uncover a distinctive signature of MBSs, namely the emergence of pronounced split peaks in the drag transconductance, directly linked to inter-MBS coupling. We further show that the dynamics of quantum coherence is correlated with the emergence and enhancement of MBS-induced split peaks in the drag transconductance. A comparative analysis with trivial subgap states reveals key differences, that is, MBS-induced transconductance peaks are symmetric and exhibit characteristic splitting, while trivial-state features are generally asymmetric and lack such robust splitting behavior. These findings establish experimentally accessible criteria for distinguishing MBSs from trivial subgap states and provide a practical framework for probing Majorana physics through nonlocal transport.
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All authors Zi-Wei Li, Jiaojiao Chen, Wei Xiong, Xiao Xue, Zeng-Zhao Li

Quantum Physics

Estimating Local Observables via Cluster-Level Light-Cone Decomposition

Junxiang Huang, Yunxin Tang and Xiao Yuan

Simulating large quantum circuits on hardware with limited qubit counts is often attempted through methods like circuit knitting, which typically incur sample costs that grow exponentially with the number of connections cut. In this work, we introduce a framework based on Cluster-level Light-cone analysis that leverages the natural locality of quantum workloads. We propose two complementary algorithms: the Causal Decoupling Algorithm, which exploits geometric disconnections in the light cone for sampling efficiency, and the Algebraic Decomposition Algorithm, which utilizes algebraic expansion to minimize hardware requirements. These methods allow simulation costs to depend on circuit depth and connectivity rather than system size. Together, our results generalize Lieb-Robinson-inspired locality to modular architectures and establish a quantitative framework for probing local physics on near-term quantum devices by decoupling the simulation cost from the global system size.
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Quantum Physics · Cross-listed from physics.chem-ph

Efficient Quantum Simulation of Non-Adiabatic Molecular Dynamics with Precise Electronic Structure

Tianyi Li, Yumeng Zeng, Qiming Ding et al. (+6 more)

In the study of non-adiabatic chemical processes such as photocatalysis and photosynthesis, non-adiabatic molecular dynamics (NAMD) is an indispensable theoretical tool, which requires precise potential energy surfaces (PESs) of ground and excited states. Quantum computing offers promising potential for calculating PESs that are intractable for classical computers. However, its realistic application poses significant challenges to the development of quantum algorithms that are sufficiently general to enable efficient and precise PES calculations across chemical systems with diverse properties, as well as to seamlessly adapt existing NAMD theories to quantum computing. In this work, we introduce a quantum-adapted extension to the Landau-Zener-Surface-Hopping (LZSH) NAMD. This extension incorporates curvature-driven hopping corrections that protect the population evolution while maintaining the efficiency gained from avoiding the computation of non-adiabatic couplings (NACs), as well as preserving the trajectory independence that enables parallelization. Furthermore, to ensure the high-precision PESs required for surface hopping dynamics, we develop a sub-microhartree-accurate PES calculation protocol. This protocol supports active space selection, enables parallel acceleration either on quantum or classical clusters, and demonstrates adaptability to diverse chemical systems - including the charged H3+ ion and the C2H4 molecule, a prototypical multi-reference benchmark. This work paves the way for practical application of quantum computing in NAMD, showcasing the potential of parallel simulation on quantum-classical heterogeneous clusters for ab-initio computational chemistry.
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All authors Tianyi Li, Yumeng Zeng, Qiming Ding, Zixuan Huo, Xiaosi Xu, Jiajun Ren, Diandong Tang, Xiaoxia Cai, Xiao Yuan

Quantum Physics

Universal Sensitivity Bound for Thermal Quantum Dynamic Sensing

Rui Zhang, Yang Yang, Wenkui Ding et al. (+1 more)

This work unifies the equilibrium and non-equilibrium frameworks of quantum metrology within the context of many-body systems. We investigate dynamic sensing schemes to derive an upper bound on the quantum Fisher information for probe states in thermal equilibrium with their environment. We establish that the dynamic quantum Fisher information for a thermal probe state is upper bounded by the degree of non-commutation between the transformed local generator and the Hamiltonian for the thermal state. Furthermore, we show that this upper bound scales as the square of the product of the inverse temperature and the evolution time. In the low-temperature limit, we establish an additional upper bound expressed as the seminorm of the commutator divided by the energy gap. We apply this thermal dynamic sensing scheme to various models, demonstrating that the dynamic quantum Fisher information satisfies the established upper bounds.
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All authors Rui Zhang, Yang Yang, Wenkui Ding, Xiaoguang Wang

Quantum Physics · Cross-listed from cs.LG

Training Energy-Based Models with Non-MCMC Samplers and Efficient Temperature Estimation

Kentaro Kubo and Hayato Goto

Efficient sampling from Boltzmann distributions over discrete variables is a fundamental operation in a wide range of applications. While fast non-MCMC samplers have recently emerged as promising alternatives to conventional MCMC methods, their practical use for probabilistic learning remains hindered by the difficulty of estimating the effective temperature of the generated samples. In this work, we begin by introducing Langevin simulated bifurcation (LSB), a Boltzmann sampler that enables fast and parallel sampling with accuracy comparable to sequential MCMC methods. To address the challenge of unknown effective temperature, we propose conditional expectation matching (CEM), an efficient estimation method applicable to energy-based models (EBMs) with exploitable conditional independence structures. Building on these components, we further develop a learning framework, termed sampler adaptive learning (SAL), which adaptively adjusts the model temperature to match that of the distribution induced by fast non-MCMC sampling. We demonstrate the effectiveness of LSB, CEM, and SAL on semi-restricted Boltzmann machines (SRBMs), a class of EBMs that are difficult to train using conventional approaches. LSB achieves orders-of-magnitude acceleration over Gibbs sampling while maintaining comparable or higher accuracy, and CEM enables accurate temperature estimation of the resulting distribution with negligible computational overhead. As a consequence, SAL enables efficient training of SRBMs and outperforms conventional Boltzmann machine learning methods on synthetic spin-glass datasets. In addition, the trained models achieve strong performance across multiple tasks. These results establish LSB as a fast and accurate Boltzmann sampler and provide key insights that enable practical applications of fast non-MCMC sampling methods via efficient temperature estimation with CEM.
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Monday 1 December

Quantum Physics

QGShap: Quantum Acceleration for Faithful GNN Explanations

Haribandhu Jena, Jyotirmaya Shivottam and Subhankar Mishra

Graph Neural Networks (GNNs) have become indispensable in critical domains such as drug discovery, social network analysis, and recommendation systems, yet their black-box nature hinders deployment in scenarios requiring transparency and accountability. While Shapley value-based methods offer mathematically principled explanations by quantifying each component's contribution to predictions, computing exact values requires evaluating $2^n$ coalitions (or aggregating over $n!$ permutations), which is intractable for real-world graphs. Existing approximation strategies sacrifice either fidelity or efficiency, limiting their practical utility. We introduce QGShap, a quantum computing approach that leverages amplitude amplification to achieve quadratic speedups in coalition evaluation while maintaining exact Shapley computation. Unlike classical sampling or surrogate methods, our approach provides fully faithful explanations without approximation trade-offs for tractable graph sizes. We conduct empirical evaluations on synthetic graph datasets, demonstrating that QGShap achieves consistently high fidelity and explanation accuracy, matching or exceeding the performance of classical methods across all evaluation metrics. These results collectively demonstrate that QGShap not only preserves exact Shapley faithfulness but also delivers interpretable, stable, and structurally consistent explanations that align with the underlying graph reasoning of GNNs. The implementation of QGShap is available at https://github.com/smlab-niser/qgshap.
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Quantum Physics

Performance Analysis of Quantum Support Vector Classifiers and Quantum Neural Networks

Tomás Villalba-Ferreiro, Eduardo Mosqueira-Rey and Diego Alvarez-Estevez

This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. By evaluating these models on the Iris and MNIST-PCA datasets, we find that quantum models tend to outperform classical approaches as the problem complexity increases. While QSVCs generally provide more consistent results, QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. Additionally, we analyze the impact of hyperparameter tuning, showing that feature maps and ansatz configurations significantly influence model accuracy. We also compare the PennyLane and Qiskit frameworks, concluding that Qiskit provides better optimization and efficiency for our implementation. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies
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Quantum Physics

Quantum-Classical Separation in Bounded-Resource Tasks Arising from Measurement Contextuality

Shashwat Kumar, Eliott Rosenberg, Alejandro Grajales Dau et al. (+280 more)

The prevailing view is that quantum phenomena can be harnessed to tackle certain problems beyond the reach of classical approaches. Quantifying this capability as a quantum-classical separation and demonstrating it on current quantum processors has remained elusive. Using a superconducting qubit processor, we show that quantum contextuality enables certain tasks to be performed with success probabilities beyond classical limits. With a few qubits, we illustrate quantum contextuality with the magic square game, as well as quantify it through a Kochen–Specker–Bell inequality violation. To examine many-body contextuality, we implement the N-player GHZ game and separately solve a 2D hidden linear function problem, exceeding classical success rate in both. Our work proposes novel ways to benchmark quantum processors using contextuality-based algorithms.
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All authors Shashwat Kumar, Eliott Rosenberg, Alejandro Grajales Dau, Rodrigo Cortinas, Dmitri Maslov, Richard Oliver, Adam Zalcman, Matthew Neeley, Alice Pagano, Aaron Szasz, Ilya Drozdov, Zlatko Minev, Craig Gidney, Noureldin Yosri, Stijn J. de Graaf, Aniket Maiti, Dmitry Abanin, Rajeev Acharya, Laleh Aghababaie Beni, Georg Aigeldinger, Ross Alcaraz, Sayra Alcaraz, Trond I. Andersen, Markus Ansmann, Frank Arute, Kunal Arya, Walt Askew, Nikita Astrakhantsev, Juan Atalaya, Ryan Babbush, Brian Ballard, Joseph C. Bardin, Hector Bates, Andreas Bengtsson, Martin Bigdeli, Alexander Bilmes, Simon Bilodeau, Felix Borjans, Alexandre Bourassa, Jenna Bovaird, Dylan Bowers, Leon Brill, Peter Brooks, Michael Broughton, David A. Browne, Brett Buchea, Bob B. Buckley, Tim Burger, Brian Burkett, Nicholas Bushnell, Jamal Busnaina, Anthony Cabrera, Juan Campero, Hung-Shen Chang, Silas Chen, Zijun Chen, Ben Chiaro, Liang-Ying Chih, Jahan Claes, Agnetta Y. Cleland, Bryan Cochrane, Matt Cockrell, Josh Cogan, Roberto Collins, Paul Conner, Harold Cook, William Courtney, Alexander L. Crook, Ben Curtin, Sayan Das, Laura De Lorenzo, Sean Demura, Agustin Di Paolo, Paul Donohoe, Andrew Dunsworth, Valerie Ehimhen, Alec Eickbusch, Aviv Moshe Elbag, Lior Ella, Mahmoud Elzouka, David Enriquez, Catherine Erickson, Vinicius S. Ferreira, Marcos Flores, Leslie Flores Burgos, Ebrahim Forati, Jeremiah Ford, Austin G. Fowler, Brooks Foxen, Masaya Fukami, Alan Wing Lun Fung, Lenny Fuste, Suhas Ganjam, Gonzalo Garcia, Christopher Garrick, Robert Gasca, Helge Gehring, Élie Genois, William Giang, Dar Gilboa, James E. Goeders, Ed Gonzales, Raja Gosula, Dietrich Graumann, Joel Grebel, Alex Greene, Jonathan A. Gross, Jose Guerrero, Tan Ha, Steve Habegger, Tanner Hadick, Monica Hansen, Matthew P. Harrigan, Sean D. Harrington, Jeanne Hartshorn, Stephen Heslin, Paula Heu, Oscar Higgott, Reno Hiltermann, Jeremy Hilton, Hsin-Yuan Huang, Mike Hucka, Ashley Huff, William J. Huggins, Evan Jeffrey, Shaun Jevons, Zhang Jiang, Xiaoxuan Jin, Cody Jones, Chaitali Joshi, Pavol Juhas, Andreas Kabel, Dvir Kafri, Hui Kang, Amir H. Karamlou, Ryan Kaufman, Kostyantyn Kechedzhi, Trupti Khaire, Tanuj Khattar, Mostafa Khezri, Seon Kim, Paul V. Klimov, Can M. Knaut, Bryce Kobrin, Alexander N. Korotkov, Fedor Kostritsa, John Mark Kreikebaum, Ryuho Kudo, Ben Kueffler, Arun Kumar, Vladislav D. Kurilovich, Vitali Kutsko, David Landhuis, Tiano Lange-Dei, Brandon W. Langley, Pavel Laptev, Kim-Ming Lau, Loïck Le Guevel, Emma Leavell, Justin Ledford, Joy Lee, Kenny Lee, Brian J. Lester, Wendy Leung, Lily L Li, Wing Yan Li, Alexander T. Lill, William P. Livingston, Matthew T. Lloyd, Aditya Locharla, Daniel Lundahl, Aaron Lunt, Sid Madhuk, Ashley Maloney, Salvatore Mandrà, Leigh S. Martin, Orion Martin, Eric Mascot, Paul Masih Das, Cameron Maxfield, Jarrod R. McClean, Matt McEwen, Seneca Meeks, Anthony Megrant, Kevin C. Miao, Reza Molavi, Sebastian Molina, Shirin Montazeri, Charles Neill, Michael Newman, Anthony Nguyen, Murray Nguyen, Chia-Hung Ni, Murphy Yuezhen Niu, Logan Oas, William D. Oliver, Raymond Orosco, Kristoffer Ottosson, Sherman Peek, David Peterson, Alex Pizzuto, Rebecca Potter, Orion Pritchard, Michael Qian, Chris Quintana, Ganesh Ramachandran, Arpit Ranadive, Matthew J. Reagor, Rachel Resnick, David M. Rhodes, Daniel Riley, Gabrielle Roberts, Roberto Rodriguez, Emma Ropes, Emma Rosenfeld, Dario Rosenstock, Elizabeth Rossi, David A. Rower, Kannan Sankaragomathi, Murat Can Sarihan, Kevin J. Satzinger, Sebastian Schroeder, Henry F. Schurkus, Aria Shahingohar, Michael J. Shearn, Aaron Shorter, Noah Shutty, Vladimir Shvarts, Volodymyr Sivak, Spencer Small, W. Clarke Smith, David A. Sobel, Barrett Spells, Sofia Springer, George Sterling, Jordan Suchard, Alexander Sztein, Madeline Taylor, Jothi Priyanka Thiruraman, Douglas Thor, Dogan Timucin, Eifu Tomita, Alfredo Torres, M. Mert Torunbalci, Hao Tran, Abeer Vaishnav, Justin Vargas, Sergey Vdovichev, Guifre Vidal, Catherine Vollgraff Heidweiller, Meghan Voorhees, Steven Waltman, Jonathan Waltz, Shannon X. Wang, Brayden Ware, James D. Watson, Travis Weidel, Theodore White, Kristi Wong, Bryan W. K. Woo, Christopher J. Wood, Maddy Woodson, Cheng Xing, Z. Jamie Yao, Ping Yeh, Bicheng Ying, Juhwan Yoo, Elliot Young, Grayson Young, Ran Zhang, Yaxing Zhang, Ningfeng Zhu, Nicholas Zobrist, Zhenjie Zou, Shruti Puri, Erik Lucero, Julian Kelly, Sergio Boixo, Yu Chen, Vadim Smelyanskiy, Hartmut Neven, Pedram Roushan, Michel Devoret