Quantum Physics
Quantum memory and scrambling from the perspective of a classical neural network
Dimitrios Maroulakos, Andrzej Wal, Marcin Kowalik et al. (+4 more)
Entropic uncertainty relations are universal quantifiers of fundamental uncertainties of
quantum measurements and are widely discussed in the
quantum metrology literature.
Quantum memory is a phenomenon related to the specific type of
quantum correlations that allows for reducing fundamental uncertainties of
quantum measurements. In the present work, the modified concept of
quantum memory for time-dependent problems is proposed. We compare the time-dependent formulation of
quantum memory with the out-of-time-ordered correlator (OTOC).
Quantum memory is a rigorous mathematical concept that requires demanding calculations. Thus, until now,
quantum memory has been discussed mainly for simple model systems and stationary problems. In the present work, we demonstrate that
quantum memory can also be studied for realistic and physically relevant systems, e.g., the atomic helical
spin chain, as well as the emergence and propagation of
quantum correlations in time. We found that
quantum memory manifests faster oscillations in time than OTOC and does not equilibrate. Furthermore, an artificial neural network is trained and asked to predict results for OTOC and
quantum memory. These results show that
quantum memory is more sensitive than OTOC in terms of broken inversion
symmetry and the nonreciprocal effect.
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All authors Dimitrios Maroulakos, Andrzej Wal, Marcin Kowalik, Czesław Jasiukiewicz, Rohit Kumar Shukla, Sunil K. Mishra, Levan Chotorlishvili
Quantum Physics
Nanoscale Sensing of Solid-State Samples with High Frequency Resolution
P. Alsina-Bolívar, I. Iriarte-Zendoia, D. B. Bucher et al. (+1 more)
To meet the growing demand for nanoscale surface analysis, nitrogen-vacancy (NV) centers offer a high-sensitivity alternative by leveraging their ability to operate in immediate proximity to the sample. In this work, we propose a
quantum control protocol designed to overcome the inherent challenges of solid-state environments, specifically by mitigating anisotropy and strong dipole-dipole interactions to enable the detection of isotropic chemical shifts at the nanoscale. To achieve this, our scheme synchronizes a slowly rotating
magnetic field with tailored RF decoupling and MW control of the NV sensors. We provide an analytical mapping that explicitly links the measured spectrum to the control sequence features and the underlying system parameters, enabling a straightforward characterization of the sample.
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All authors P. Alsina-Bolívar, I. Iriarte-Zendoia, D. B. Bucher, J. Casanova
Quantum Physics
Efficient Complex-Valued State Preparation on Bucket Brigade QRAM
Alessandro Berti and Francesco Ghisoni
Efficient
quantum state preparation is a critical component in
quantum algorithms that process large classical data, and it is fundamental to realizing
quantum advantage in domains such as machine learning,
quantum linear
algebra, and
quantum finance. Building on the framework of~\cite{berti2025efficient}, which integrates Bucket Brigade QRAM (BBQRAM) with a segment tree to achieve amplitude encoding in polylogarithmic query time, we present two improvements within the same architecture-aware framework. First, we remove the $U_{2\mathrm{CR}}$ subroutine by classically precomputing the rotation angles determined by the segment tree and storing these angles directly in the BBQRAM
cells. The tradeoff is that the classically loaded QRAM stores precomputed fixed-point angles rather than raw subtree weights. Second, we extend the construction to complex-valued
matrices $A \in \mathbb{C}^{M \times N}$ by storing a leaf phase alongside each precomputed rotation angle and using a two-step magnitude-then-phase procedure; the real signed case is naturally subsumed as a one-bit phase specialization. At unchanged $\mathcal{O}(\log_2^2(MN))$ BBQRAM query complexity, the QPU procedure reduces to BBQRAM
retrievals and controlled-rotation cascades, with $\mathcal{O}(MN)$
memory cells per
matrix and no reversible arithmetic on the QPU.
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Quantum Physics
Continuous Reset-Induced Phase Transition in Measurement-Free Random Quantum Circuits
Hinata Yokoyama, Kengo Anzai, Dina Syverud-Lindland et al. (+2 more)
We study a random
unitary quantum circuit with only reset channels, which has high feasibility for real
quantum devices. In particular, we investigate the many-body statistical physics properties, “reset-induced”
entanglement phase transitions comparing the classical statistical picture in the large “$d$” limit of qudits. In the property of the reset-induced phase transition the parameter of qudit $d$ is essential. That is, the transition properties induced by the reset channel significantly depend on $d$. We numerically elucidate this statement employing efficient stabilizer
circuit simulations for $d=2$. Specifically, critical fluctuations that grow with system size are observed near the transition point, and the finite-size scaling analyses yield data collapse consistent with a continuous mixed-state phase transition.} This behavior differs from expectations based on the classical statistical mapping in the large-$d$ limit.
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All authors Hinata Yokoyama, Kengo Anzai, Dina Syverud-Lindland, Yoshihito Kuno, Hiroaki Matsueda
Quantum Physics
Local tensor-train surrogates for quantum learning models
Sreeraj Rajindran Nair and Christopher Ferrie
A key bottleneck in
quantum machine learning is the computational cost of repeated
quantum circuit evaluations during the inference phase. To address this, we present a framework for constructing fast, cheap, provably accurate classical tensor-train surrogates of fully trained
quantum machine learning models within local patches of their input data space. The approach combines Taylor
polynomial approximation with a tensor-train (TT) representation and embeds it in a statistical learning paradigm via empirical risk minimization. In our analysis, the Taylor-TT construction serves as a deterministic error certificate proving that the TT hypothesis class contains a good approximation; empirical risk minimization then provably recovers a surrogate with controlled generalization error and explicit bounds. This translates into three independently controllable error sources: (i) Taylor truncation error controlled by the patch radius $r$ and
polynomial degree $p$, (ii) TT approximation error controlled by the bond dimension $\chi$, and (iii) statistical estimation error. While the parameter count scales polynomially in the number of data dimensions $N$, i.e., $d_{\mathrm{eff}} = N(p+1)\chi^2$ rather than the naive $(p+1)^N$, the worst-case constants inherit an exponential factor through the tensor-product feature norm during Taylor
polynomial embedding onto TT. This cleanly separates representation complexity from feature-induced constants. Our risk bounds and sample complexity depend explicitly on the local patch radius $r$.
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Quantum Physics
Robustness of fiber-optic attenuators to 1061-nm sub-nanosecond pulsed laser radiation in quantum key distribution systems
Daria Ruzhitskaya, Irina Zhluktova, Anastasiya Ponosova et al. (+10 more)
The security of
quantum key distribution (QKD) systems relies on the physical integrity of their components. While
laser-damage attacks (LDAs) using high-power continuous-wave (cw)
lasers have been well studied, the threat posed by pulsed
lasers at alternative wavelengths remains underestimated. Here, we experimentally investigated the stability of four types of fiber-optic attenuators under exposure to sub-picosecond pulses at 1061 nm with average power reaching 1 W. Mechanical variable attenuators with blocking elements and fixed air-gap attenuators show resistance to this attack. MEMS-based variable attenuators exhibit increased attenuation or irreversible damage that causes a permanent reduction in attenuation of approximately 3.8 dB. For fixed attenuators with an absorption element, we demonstrate that initial pulsed irradiation significantly lowers the optical damage threshold of the components compared to direct cw attacks. The attenuation reduction achieved is up to 7 dB at a 1 W cw
laser at 1550 nm. These results highlight the possibility of establishing a hidden side-channel for eavesdropping attacks and underscore the insufficiency of existing countermeasures against sophisticated LDA scenarios.
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All authors Daria Ruzhitskaya, Irina Zhluktova, Anastasiya Ponosova, Fedor Ushakov, Andrey Zverev, Galina Tertyshnikova, Tianyi Xing, Kirill Min'kov, Daniil Trefilov, Anqi Huang, Vladimir Kamynin, Vladimir Tsvetkov, Vadim Makarov
Quantum Physics
Near-identical photons from distant quantum dot-cavity devices
Thibaut Pollet, Victor Guilloux, Duc-Duy Tran et al. (+13 more)
Scalable optical
quantum technologies require interference between large numbers of indistinguishable single-
photons emitted by independent sources.
Semiconductor quantum dots are known to be excellent on-demand sources of single-
photons. They show record efficiency when inserted into optical cavities to control their spontaneous emission and generate trains of near identical
photons over microsecond timescales. However, generating perfectly identical
photons from distant cavity-based sources has remained a long-standing challenge. It requires precise matching of the emission wavelengths and emission dynamics, while simultaneously minimizing spectral noise across all time scales for distant emitters in uncorrelated environments. Here, we report on the nanofabrication of a large number of
quantum dot-cavity sources with ultra-low spectral noise and wavelength dispersion. The high source efficiency and the use of two tuning mechanisms enable precise optimization of the spectral overlap between distant sources. With this approach, we demonstrate a two-
photon indistinguishability of $88\pm1$ % between
photons emitted from two distant sources. Remarkably, this value reaches the upper bound set by the intrinsic indistinguishability of
photons emitted successively by each source. These results represent a key milestone for scaling
photon-based
quantum technologies.
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All authors Thibaut Pollet, Victor Guilloux, Duc-Duy Tran, Anton Pishchagin, Stephen Wein, Joseph A. Sulpizio, William Hease, Petr Stepanov, Petr Steindl, Nico Margaria, Samuel Mister, Martina Morassi, Aristide Lemaître, Thi Huong Au, Sébastien Boissier, Pascale Senellart
Quantum Physics
One Coordinate at a Time: Convergence Guarantees for Rotosolve in Variational Quantum Algorithms
Sayantan Pramanik and M Girish Chandra
In this paper, we resolve an open question in the field of optimization
algorithms for training parametrized
quantum circuits: Does the popular Rotosolve
algorithm converge? Until now, interpolation-based coordinate descent methods such as Rotosolve have mostly been treated as heuristics, lacking any formal convergence guarantees. We rigorously analyze Rotosolve, and show that it converges to $\varepsilon$-stationary points if the optimization landscape is non-convex and smooth; and to $\varepsilon$-suboptimal points if the objective function additionally obeys the Polyak-Lojasiewicz (PL) condition. Further, we derive explicit worst-case rates of convergence in the finite
quantum measurement regime. These rates are contrasted against those from a similar coordinate-based method: Randomized Coordinate Descent (RCD). Although in the worst case their rates are, prima facie, equivalent, we present arguments for a more nuanced comparison between the two. We highlight that Rotosolve is hyperparameter-free, and implicitly uses first and second derivatives in its updates. Finally, we supplement our theoretical findings with numerical experiments from
Quantum Machine Learning; and compare the performance of Rotosolve against RCD,
Stochastic Gradient Descent, Simultaneous Perturbation
Stochastic Approximation, and Randomized
Stochastic Gradient Free methods.
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Quantum Physics
Optimizing ground state preparation protocols with autoresearch
Luis Mantilla Calderón, Jérôme F. Gonthier, Ignacio Gustin et al. (+2 more)
Artificial intelligent
language-model based coding
agents have significantly changed the way we interact with computers in our day-to-day, as it is common to use them to create, improve, and run programming scripts only using natural
language.
Agent code updates can be better guided when such programs can be executed and scored automatically rather than judged by human preference. In
quantum computing and classical
quantum simulation settings, ground-state preparation has a parallel structure: candidate protocols can be ranked by estimated energies and other proxies indicating proper
quantum-state convergence. In this work, we study how autoresearch, a code optimization strategy based on coding
agents, can be used to optimize hyperparameter choices of different ground-state preparation and sampling protocols, including the variational
quantum eigensolver (VQE), density
matrix renormalization group (DMRG), and auxiliary-field
quantum Monte Carlo (AFQMC). We validate the viability and capacity of this method on simple
spin models and molecular
Hamiltonians. Across all three settings, the
agent mutates simple baselines into complex protocols with improved energy proxies while operating under constrained space-time computational budgets. We conclude with discussions of other
quantum routines that support executable scalar scoring, enabling evolutionary coding
agents to automate a substantial portion of the protocol-tuning work that would otherwise be required manually.
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All authors Luis Mantilla Calderón, Jérôme F. Gonthier, Ignacio Gustin, Varinia Bernales, Alán Aspuru-Guzik
Quantum Physics
Quantum annealing inspired algorithms for the NISQ Era
Rijul Sachdeva, Vrinda Mehta, Manpreet Singh Jattana et al. (+2 more)
We study
algorithms inspired by
quantum annealing that are suited for the NISQ era. First, we analyze approximate
quantum annealing (AQA), which employs a discretized annealing ansatz in which the time step and the number of layers are allowed to deviate from a faithful implementation of
quantum annealing. Parameter scans identify regimes that reproduce annealing-like behavior with reduced resources, making them more suitable for NISQ devices. The resulting parameters can then be used as an effective warm start for the
quantum approximate optimization
algorithm (QAOA), improving its performance compared to random initializations. We also introduce evolving
Hamiltonian quantum optimization (EHQO), a multistep variational scheme that guides the optimization process through intermediate
Hamiltonians derived from the standard annealing
Hamiltonian. Numerical simulations on sets of hard 2-SAT instances suggest that
quantum annealing-inspired
algorithms provide practical strategies for enhancing variational
quantum optimization.
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All authors Rijul Sachdeva, Vrinda Mehta, Manpreet Singh Jattana, Kristel Michielsen, Fengping Jin
Quantum Physics
Bond-dimension scaling of a local-refinement advantage over hyperoptimized tensor-network contraction on Sycamore like topologies
Rubén Darío Guerrero
We identify a missing local-refinement stage in the cotengra tensor-network contraction pipeline and show that its impact grows monotonically with bond dimension on the connectivity graph of Sycamore-like
topologies. Appending a nearest-neighbor interchange (NNI) search to the \cotengra{} output at matched 8-s wallclock yields a median predicted cost-model gap $\Delta\fT$ at $n{=}500$ that grows monotonically and approximately linearly in $\chi$, from $\sim\!15$~bits at $\chi{=}2$ to $\sim\!116$~bits at $\chi{=}16$ (Fig.~\ref{fig:chi_sweep}), with the refiner winning on $25/25$ seeds at every tested $\chi$. Two control families – random $3$-regular and QAOA $p{=}2$ interaction graphs – show median $|\Delta\fT| \leq 0.71$~bits across both controls at every $\chi$, with refiner win rate falling toward chance as $\chi$ grows; the signal is
topology-specific, not a generic refinement-budget effect. An ablation establishes that refinement itself, not the four-axis Pareto acceptance rule, drives the gain ($|\Delta\fT| \lesssim 0.1$ bits between scalar and Pareto arms at $\chi{=}2$). The Sycamore-
circuit envelope (App.~\ref{em:sec:results:syccirc}) reports the corresponding refinement on actual random
circuits at depths $m \in \{4, 6, 8, 10, 12\}$, where the refiner wins on $5/5$ instances at every depth. The advantage is therefore largest precisely in the bond-dimension regime relevant to physical contraction.
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Quantum Physics
Quantum Optimization Methods for the Generalized Traveling Salesman Problem
Maximilian Zorn, Melinda Braun, Michael Ertl et al. (+4 more)
This paper studies
quantum optimization baselines for the Generalized Traveling Salesman Problem (GTSP), a clustered routing problem that naturally models variant selection and sequencing problems under discrete alternatives. We propose a novel GTSP QUBO formulation focused on maintaining feasible solutions for
quantum annealing, as well as a hardware-executable gate-based pipeline utilizing the
Quantum Approximate Optimization
Algorithm (QAOA). We implement a constrained QAOA variant using an XY-mixer, which preserves the stepwise Hamming weight in the ideal
circuit model, while feasibility with respect to the full GTSP constraints is tracked explicitly during post-processing. We compare the two
quantum optimization paradigms on problem instances from GTSPLIB, an established
benchmark dataset, and validate against classical state-of-the-art solvers. To mitigate current
quantum hardware size limitations, we further extend a preprocessing method to reduce the node count in instance clusters, constructing new NISQ-friendly instances from reduced subsets. Across all tested instances,
quantum solvers often produce competitive solution quality when tested on smaller graphs, but exhibit higher runtimes and a sharp degradation in feasibility and scalability as instance size grows. Our evaluation highlights where
quantum optimizers can already succeed and which algorithmic bottlenecks, like sampling rates, runtime issues, and other practical failure modes, remain as open problems.
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All authors Maximilian Zorn, Melinda Braun, Michael Ertl, Tommy Kiss, Sara Juarez Oropeza, Claudia Linnhoff-Popien, Jonas Stein
Quantum Physics
Defect-Adaptive Lattice Surgery on Irregular Boundary Surface-Code Patches
GunSik Min, Youshin Chung, Yujin Kang et al. (+1 more)
Defect-adaptive surface-code methods construct valid logical patches on imperfect hardware, but fault-tolerant computation also requires certifying that requested logical operations remain executable on the resulting irregular geometries. We address this problem for
lattice-surgery merges whose interfaces intersect deformed boundaries, unavailable checks, or gauge-inferred operators. Our compiler constructs an admissible seam-measurement set whose elements associate effective seam supports with raw-outcome selectors and schedule metadata. A GF(2) row-space test then determines whether the requested joint parity is realizable modulo pre-merge stabilizer constraints, returning either an explicit parity-extraction rule or a dual witness of non-realizability within the evaluated set. Paired ablations isolate the contribution of the proposed measurement-set construction rules. We further apply the test to configurations generated by Snakes and Ladders (SnL) on 15,000 clustered boundary-defect maps. Adding the proposed boundary-reconstruction rows increases the certification rate from 61.88% to 62.79%, with the gain concentrated in strongly clustered regimes. The framework therefore provides a compiler step that separates defect-adaptive patch construction from the certification and extraction of
lattice-surgery parity measurements.
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All authors GunSik Min, Youshin Chung, Yujin Kang, Jun Heo
Quantum Physics
Simon's Algorithm for the Even-Mansour Cipher on Quantum Hardware
Anina Köhler, Jakob Murauer, Tim Heine et al. (+2 more)
Simon's
algorithm is a
polynomial period-finding
algorithm that has been used to exploit the algebraic structure of specific symmetric ciphers, showing that exponential speedups in their cryptanalysis are theoretically possible. While the theoretical framework for an attack using Simon's
algorithm on the Even-Mansour cipher is well-established, practical implementations on noisy intermediate-scale
quantum (NISQ) hardware remain limited. This paper presents a proof of concept
quantum cryptanalysis of the Even-Mansour cipher using Simon's period-finding
algorithm on NISQ hardware. For N = 3 and N = 4, we successfully demonstrate secret key recovery for N-bit constructions on the ibm_miami processor. Our experiments also identify a scaling limitation in the classical pre-processing stage: The DORCIS
circuit optimization tool encountered a
memory bottleneck at N = 5, preventing the generation of optimized
circuits for larger key lengths. Our results suggest firstly that Simon's
algorithm is effective for the Even-Mansour cipher for short bit lengths on current
quantum hardware. Secondly, while DORCIS is effective for the small-scale S-boxes for which it was designed, there remains a need for the investigation of more scalable and efficient synthesis tools capable of handling larger and more general permutations in the context of Even-Mansour ciphers.
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All authors Anina Köhler, Jakob Murauer, Tim Heine, Stefan Rosemann, Tobias Hemmert
Quantum Physics
Quantum-Accelerated Gowers $U_2$ Norm for Bent Boolean Functions
Rajdeep Dwivedi, C. A. Jothiwashran, Sugata Gangopadhyay et al. (+1 more)
Bent Boolean functions extremal objects that maximally resist affine approximation are notoriously hard to construct for large numbers of variables. We propose a hybrid
quantum-classical genetic
algorithm (GA) that uses a
quantum circuit to evaluate the Gowers $U_2$ norm as the evolutionary fitness function. Our central contribution is a complexity-theoretic separation: the
quantum evaluation
circuit requires only $3n$
qubits and $\bigO(n^2)$ two-
qubit gates per function query, whereas the classical computation of the exact Gowers $U_2$ norm demands $\bigO(2^{2n})$ arithmetic operations an exponential overhead that renders it infeasible for $n \gtrsim 25$. We validate the framework on $n=6$ and $n=8$ variable systems. For $n=8$, our classical GA run extended to 1000 generations achieves best fitness $\Utwof = 0.250000$ exactly the theoretical bent threshold $2^{-n/4}$ with average fitness $0.257267$, confirming that the Gowers $U_2$ norm is a superior fitness criterion over Walsh-Hadamard spectral flatness.
Quantum-assisted evaluation faithfully reproduces the classical trajectory up to finite-sampling noise, and our complexity analysis demonstrates that for $n > 25$ the
quantum evaluator provides a decisive computational advantage on fault-tolerant hardware.
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All authors Rajdeep Dwivedi, C. A. Jothiwashran, Sugata Gangopadhyay, Vishvendra Singh Poonia
Quantum Physics
Sector-dominant graph-local drivers for path-window barrier Hamiltonians on the Boolean hypercube
Takiko Sasaki and Tetsuji Tokihiro
We study finite-size adiabatic state preparation on Boolean hypercubes using graph-local drivers built from sector/path coordinates related to monotone Gray-code representatives. The construction is not presented as a new all-$n$ Gray-code existence
theorem; rather, it provides finite representatives, explicitly checked through the cases used in the numerical experiments, for testing problem-dependent graph-local drivers. For ordinary diagonal-cost transverse-field annealing, the ordering does not yield a robust advantage, and we include this negative result as a baseline. For non-diagonal target
Hamiltonians whose geometry is expressed in the same sector/path coordinates, hybrid drivers combining sector, path-window, and small transverse-field components can substantially improve the final ground-state fidelity in centered barrier instances. Reproduction runs from the accompanying code confirm a representative centered original-window barrier value of approximately \(0.9799\) for the fixed-control hybrid parameters \((w,\alpha,\epsilon)=(8,0.50,0.15)\), while also showing that the improvement is target-class dependent. Randomized and ablation controls indicate that the dominant contribution is the sector-preserving skeleton, with strict one-bit completion acting as a secondary refinement. We provide code, finite certificates, CSV files, validation logs, and reproduction scripts to make the finite-size claims traceable.
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Quantum Physics
Quantum Compressed Sensing Enables Image Classification with a Single Photon
Yanshan Fan, Jianyong Hu, Shuxiao Wu et al. (+9 more)
Image classification is a core task of intelligent sensing, conventionally follows a sequential imaging then processing pipeline. However, redundant high-dimensional image reconstruction is inherently inefficient, especially in
photon limited scenarios. Here we report a
photon level image classification method using
quantum compressed sensing, which reformulates the classification task as a sparse signal measurement problem directly oriented toward class labels. By exploiting the parallelism of photonic
quantum superposition states, a single
photon can be encoded the complete spatial information of a high-dimensional image. Through a diffractive deep neural network, we physically construct a dedicated measurement basis aligned with the class space, enabling signal-dependent adaptive compressive measurement. Ideally, our method can extract class information via a single
quantum projective measurement, reducing the required number of measurements from the logarithmic scaling O(Klog(N/K)) of classical compressed sensing to the constant-order information-theoretic limit M = K = 1. Experimental results show that a classification accuracy of 69.0% can be achieved by using a single-
photon detection event as the decision criterion, while it increases to 95.0% with four-
photon detection events. This work demonstrates image classification at the energy efficiency limit and introduces a measurement as decision framework. It provides a foundation for intelligent sensing systems that operate under extreme
photon budgets and harsh environments.
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All authors Yanshan Fan, Jianyong Hu, Shuxiao Wu, Zhixing Qiao, Guosheng Feng, Changgang Yang, Jianqiang Liu, Ruiyun Chen, Chengbing Qin, Guofeng Zhang, Liantuan Xiao, Suotang Jia
Quantum Physics · Cross-listed from physics.optics
Polarization-preserving wavefront rotator
Suman Karan, Aman Srivastava, Pratham Sachin Todkar et al. (+1 more)
A K-mirror rotates the wavefront of an incident optical field. However, the rotation always introduces polarization changes in the transmitted field. This is a serious concern for applications ranging from astronomical image derotation to orbital angular momentum spectrum characterization in photonic
quantum technology. Recent efforts have shown that the polarization change can be minimized significantly, but these require either a very small base angle that limits the field of view, or mirrors with a customized refractive index. Making the transmitted polarization state completely independent of the rotation angle has remained an open problem. In this work, we show that placing half-wave plates before and after a K-mirror and rotating them synchronously at half the K-mirror rotation angle makes the polarization change in the transmitted field exactly independent of the rotation angle. This works for any wavefront rotator, any base angle, any mirror refractive index, and any input state of polarization. We experimentally demonstrate the approach using a K-mirror with a base angle of $30^{\circ}$, which gives the largest field of view among practical designs, and find a mean polarization error of ~1%, limited only by the retardance imperfection of commercially available half-wave plates. This has significant practical implications for applications that require precise wavefront rotation without polarization change.
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All authors Suman Karan, Aman Srivastava, Pratham Sachin Todkar, Anand K. Jha
Quantum Physics
Zachary Macaskill-Smith, Unmol Sharma, Melissa Warner et al. (+2 more)
Minor
embedding is a required compilation step for
quantum annealing, mapping logical problem graphs onto sparse hardware
topologies. Despite its central role in determining solution quality, no standardized
benchmark exists for comparing
embedding algorithms: prior studies use incompatible graph libraries, inconsistent metrics, and non-reproducible experimental setups, making cross-
algorithm comparisons unreliable. We present Ember (
Embedding Minor
Benchmark for Evaluative Reproducibility), an open-source benchmarking framework addressing this gap. Ember provides a standardized
algorithm interface with seeded, reproducible execution infrastructure; a diverse graph library of 24,016 instances spanning structured, random, and physics-motivated problem types not previously used in
embedding benchmarks; and a unified analysis pipeline supporting all three current D-Wave hardware
topologies (Chimera, Pegasus, Zephyr). We evaluate five
algorithms across the full library on Chimera and find that no
algorithm dominates universally: rankings vary systematically with graph structure, and the best
algorithm depends on the family being embedded. We also examine the effects of hardware
topology (including Pegasus and Zephyr),
qubit error rates, and evaluate a reinforcement-learning approach (CHARME) within a narrower test set. Ember is available at https://github.com/zachmacsmith/ember and is installable via pip install ember-qc.
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All authors Zachary Macaskill-Smith, Unmol Sharma, Melissa Warner, Kálmán Varga, David A. B. Hyde
Quantum Physics · Cross-listed from physics.comp-ph
Deterministic Realization of Classical Dissipation on Quantum Computers
Muhammad Idrees Khan, Sauro Succi and Hua-Dong Yao
Lattice Boltzmann (LB) on
quantum devices must reconcile
unitary gate
evolution with the dissipative collision step. In the multiple-relaxation-time (MRT) class, we work in the common setting of modewise diagonal moment relaxation, $\delta m_r'=\lambda_r\,\delta m_r$ with $\lambda_r\in[-1,1]$ (overrelaxation if $\lambda_r<0$).
Embedding that contraction in a
unitary by block encoding or a linear combination of
unitaries (LCU) typically yields subunitary success probability that decays multiplicatively across modes, sites, and time, a key bottleneck for
quantum LB. For the dissipative MRT block alone we give a block-encoding-free construction: a signed two-rail population encoding, then a completely positive trace-preserving (CPTP) map (per-rail amplitude damping with survival $|\lambda_r|$ and, if $\lambda_r<0$, a rail SWAP) so that, after the decode, the map agrees with classical MRT relaxation exactly (expectations of the rail number operators, common encoding–decode scale). Trace preservation gives success probability $1$ for that substage. The main result is the dissipative MRT block; construction of the
equilibrium moment vector~$m^{\mathrm{eq}}=Mf^{\mathrm{eq}}$ (prescribed~$f^{\mathrm{eq}}$, host moment
matrix~$M$; notation as in Section~\ref{subsec:generic-mrt}), moment transforms, streaming, and boundaries are composed with it as in a standard host pipeline and lie outside the scope of the formal
theorem. Hybrid and fully coherent encodings, adaptive scales, Carleman-based context, and a one-rail no-go in the same nonnegative population framework are in the main text. Audits of the open-channel map on a long LBM collide-stream simulation and on stencil-free inputs both match the target to machine precision.
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Quantum Physics
Elucidating mechanism of optical cavities in superconducting strip single photon detectors using transmission line and impedance models
Hiroki Kutsuma and Taro Yamashita
We clarified the physical mechanism of superconducting strip single
photon detectors (SSPDs) with optical cavities by using transmission line and impedance models. By introducing the transmission line model, we derived the analytical formulae for the absorptance of SSPDs with optical cavities. We compared the absorptance obtained from the analytical formulae for SSPDs with single-side, double-side, and dielectric multi-layer optical cavities against the results of numerical simulations. The comparison showed that the results were nearly identical. By introducing the impedance model, it was clearly shown that the SSPDs with optical cavities achieved the maximum absorptance when their input impedance of the SSPDs with optical cavities matched the impedance of the input medium. The design concepts proposed in this study are applicable to other superconducting detectors, such as microwave kinetic inductance detectors and transition-edge sensors.
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Quantum Physics
A graph-aware bounded distance decoder for all stabilizer codes
Harikrishnan K J and Amit Kumar Pal
We formulate a bounded distance decoding strategy applicable to all stabilizer codes including both CSS and non-CSS code-families. The framework emerges out of the local Clifford equivalence between arbitrary stabilizer states and graph states. Using the graphical representation of the stabilizers and the syndromes, we constitute the bounded distance decoding as an adaptable generalization of maximum likelihood decoding, ensuring correction of all errors with weights upper bounded by a target weight. We show that strategic
pruning associated with a feed-forward network structure of the graph can reduce the search space and subsequently the runtime of the designed decoder. We demonstrate satisfactory performance of the bounded distance decoder in the case of the optimal non-CSS codes up to distance $d=11$ subjected to the depolarizing error on all
qubits, and near-optimal decoding for the color and the surface codes, both belonging to the CSS family, under the bit-flip errors on the
qubits. We also develop an open-source library, QGDecoder, enabling the graph-aware bounded distance decoding of arbitrary stabilizer codes.
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Quantum Physics
Yanqiao Wang and Jin-Peng Liu
We develop operator-output
quantum algorithms for
matrix equations and
matrix functions using
matrix-sign
embeddings. For each problem, an augmented
matrix $M$ is chosen so that the target operator is a block of $\text{sign}(M)$ or is recovered from blocks of $(I-\text{sign}(M))/2$. We approximate the half-plane sign by a logarithmic-sinc formula and implement the shifted inverse families by scaled multiplexing with node-dependent rebalancing. For ordinary Sylvester equations, this yields a block-encoding of the solution with query complexity linear in the relevant conditioning parameters and logarithmic in the inverse error tolerance, under either a field-of-values (FoV) gap or a strip-resolvent bound. The same method extends to generalized Sylvester and Lyapunov equations, principal square and inverse square roots,
matrix geometric means, and continuous-time algebraic Riccati equations (CARE), with explicit query-complexity and block-encoding-normalization bounds that cover non-normal and non-diagonalizable cases.
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Quantum Physics
Entanglement Dynamics in a Two Transmon Qubit System under Continuous Measurement and Postselection
Roson Nongthombam and Amarendra K. Sarma
We investigate the role of continuous measurement and postselection in the dynamics and
entanglement of a transmon-cavity-transmon coupled system. In the dispersive regime, characterized by a large detuning between the transmons and the cavity, the two transmons interact via virtual excitation of the cavity, giving rise to an effective transmon-transmon coupling. In addition to this coherent interaction, each transmon undergoes spontaneous emission, which is continuously monitored through independent detection channels. By incorporating realistic detector inefficiencies, we analyze both efficient and imperfect monitoring scenarios and demonstrate that postselection significantly slows down the decay of
entanglement compared to the unmonitored case. We formulate the
stochastic master equation for the coupled system, derive the corresponding postselected master equation, and investigate the dynamics through the Liouvillian superoperator spectrum. In the interaction frame, we identify the emergence of an exceptional point and characterize the associated broken and unbroken PT-symmetric phases. We show how these phases influence the system dynamics and the corresponding
entanglement behavior. Our results provide insight into how continuous measurement and postselection affect
entanglement in dissipative
quantum systems, with potential applications in
quantum information processing.
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Quantum Physics
Millikelvin digital-to-analog converter for superconducting quantum processors
Ruizi Hu, Zongyuan Li, Zhancheng Yao et al. (+19 more)
Scaling superconducting
quantum processors is increasingly constrained by the wiring, heat load, and calibration overhead associated with delivering high-resolution analog signals from room temperature to
qubits at millikelvin temperature. Here we demonstrate a superconducting digital-to-analog converter (DAC) integrated with high-coherence fluxonium
qubits in a multi-chip module architecture. The DACs generate persistent analog flux signals for tuning
qubit parameters and are programmed deterministically using single-flux-
quantum (SFQ) pulses, providing a digital interface compatible with established SFQ routing and demultiplexing technologies. Operating at millikelvin temperature, the DACs enable in-situ tuning of fluxonium
qubits without measurable degradation of
qubit coherence. The presented device provides a static control primitive for flux-tunable
qubits, enabling parameter homogenization and eliminating the need for individual room-temperature DC bias lines. These results establish SFQ-programmable millikelvin DACs as a building block for digitally controlled superconducting
quantum processors.
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All authors Ruizi Hu, Zongyuan Li, Zhancheng Yao, Yufei Wu, Qiang Zhang, Yining Jiao, Quan Guan, Lijing Jin, Wangwei Lan, Chengyao Li, Lu Ma, Liyong Mao, Huijuan Zhan, Ze Zhan, Ran Gao, Lijuan Hu, Kannan Lu, Xizheng Ma, Tenghui Wang, Peng Xiang, Chunqing Deng, Shasha Zhu
Quantum Physics
Nonlinearity-enhanced Quantum Sensing in Discrete Time Crystal Probes
Rozhin Yousefjani, Shaikha Al-Naimi, Saif Al-Kuwari et al. (+1 more)
Discrete time
crystals are non-
equilibrium phases of matter in periodically driven systems, characterized by robust subharmonic oscillations and broken discrete time-translation
symmetry. Their long-lived coherent dynamics and resilience to imperfections make them promising resources for
quantum sensing. A disorder-free discrete-time
crystal probe can provide the
quantum-enhanced estimation of the coupling parameter. Here, we extend this sensing mechanism to nonlinear interactions and show that this nonlinear profile strongly enhances the sensing precision by increasing the system-size scaling exponent of the
quantum Fisher information. Our analytical discussion separates a rigorous seminorm upper bound from the physically relevant scaling realized by product-state probes in the time
crystal regime. Numerically, we find that the
quantum Fisher information retains its quadratic long-time growth with the number of Floquet cycles, while its system-size exponent increases approximately linearly with the nonlinearity exponent, identifying nonlinearity as a resource for
quantum-enhanced sensitivity. We further show that stronger nonlinearities shrink the time
crystal stability window, making the probe more sensitive to small deviations from the resonant condition. We also analyze the effect of imperfect pulses and show that such imperfections can enhance, rather than suppress, the information encoded in the evolved state. Finally, we discuss a digital implementation of the nonlinear DTC sensing protocol using superconducting
qubits.
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All authors Rozhin Yousefjani, Shaikha Al-Naimi, Saif Al-Kuwari, Abolfazl Bayat
Quantum Physics
Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes
Kien X. Nguyen and Ilya Safro
We study parameter transferability for the
Quantum Approximate Optimization
Algorithm (QAOA) across multiple combinatorial optimization problem classes from a parameter generation perspective. Specifically, a meta-optimizer is trained on one problem class and deployed on another during test time. Prior work employs a Long Short-Term
Memory network to emulate QAOA optimization trajectories, but the learned dynamics usually collapse to near-identical paths, limiting cross-problem transfer efficiency. In this paper, we present a problem-aware graph-conditioned meta-optimizer for QAOA that learns to generate parameter trajectories over a fixed horizon, providing strong initializations with only a few steps. The optimizer is conditioned on compact graph
embeddings and trained end-to-end using differentiable feedback from the QAOA objective, avoiding the need for ground-truth angles. We evaluate across multiple graph problem classes, including MaxCut, Maximum Independent Set, Maximum Clique, and Minimum Vertex Cover. We report both solution quality and feasibility-aware metrics where constraints apply. Results across a comprehensive empirical study consisting of 64 settings show that the learned optimizer can reduce optimization effort and improve performance over standard initialization, while exhibiting transferable behavior across graph families and problem types.
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Quantum Physics · Cross-listed from cond-mat.mes-hall
Mixed-State Topology in One-Dimensional Open Floquet Systems
Görkem D. Dinc, Alexander Schnell and Andy M. Martin
We investigate the non-
equilibrium topology of the steady state of a periodically driven, dissipative Su-Schrieffer-Heeger (SSH) chain using the ensemble
geometric phase (EGP) $\phi_{\mathrm{EGP}}$, a generalisation of the Zak phase to mixed states. The coupling to thermal reservoirs is treated microscopically within Floquet-Born-Markov theory without the rotating-wave approximation. We classify the steady state rather than the generator of the dynamics, using its purity spectrum as the analogue of a band structure. We show that, within the EGP framework, the $\mathbb{Z}\times\mathbb{Z}$ structure of isolated
chiral-symmetric Floquet SSH systems generally does not carry over to the steady state. Instead, the steady state carries a single invariant whose type is set by an underlying
symmetry. When this
symmetry holds, the invariant is $\mathbb{Z}_2$-valued, with quantised $\phi_{\mathrm{EGP}}=\pm\pi/2$ at all times and finite temperatures. The EGP can switch within a driving period, tracking the presence of edge modes in the open-boundary purity spectrum and thereby establishing a bulk-boundary correspondence. The fraction of each period spent in the nontrivial regime varies continuously with the driving frequency, and the switching disappears if the dependence of the dissipator on the drive is neglected. When the
symmetry is broken, $\phi_{\mathrm{EGP}}$ is no longer quantised, but its winding over a drive period $T$ defines an invariant $\nu_T \in \mathbb Z$, with no purity-spectrum bulk-boundary correspondence. We propose that its changes signal temperature- and frequency-driven topological phase transitions. Our formalism extends to general quadratic fermionic systems with linear bath couplings.
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Quantum Physics
Hardware Realization of a Hamiltonian Simulation Algorithm for Time-Domain Maxwells Equations
Gautam Sharma, Apurva Tiwari, Niladri Gomes et al. (+3 more)
We present the first
quantum-hardware implementation of a
Hamiltonian simulation
algorithm that produces signed vector-field solutions to the time-domain Maxwells equations using a Schrodingerisation-based approach. The electromagnetic fields are discretized using finite-difference operators, and the resulting non-
unitary matrices are mapped to Bell-basis Trotter blocks, enabling efficient
circuit construction. We introduce a measurement procedure that retrieves not only field amplitudes, but also physical directions of the electric and
magnetic field values at select spatial points. Implementing this logic on
quantum hardware relies on relative-phase-based sign reconstruction. Numerical results obtained using IonQ QPU, show good agreement with analytical solutions of
benchmark problems in two dimensions and on simulators; in three dimensions. We further extend our approach to compute fields scattered from simple bodies, by enforcing appropriate boundary conditions. Our work lays the foundational steps towards realizing
quantum-hardware solutions for computational electromagnetics.
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All authors Gautam Sharma, Apurva Tiwari, Niladri Gomes, Jezer Jojo, J. Eric Bracken, Jay Pathak
Quantum Physics
Transmitter-device-independent quantum key distribution
Qiang Zeng, Abhishek Mishra, Haoyang Wang et al. (+1 more)
Transmitter-side device dependence is a longstanding yet implicit problem in
quantum key distribution (QKD), in contrast to the thoroughly addressed measurement-side case.
Quantum steering, which intrinsically distinguishes the roles of sender and receiver in
entanglement certification, offers a natural route to lifting trust assumptions on the transmitter. Existing one-sided device-independent (1sDI) protocols that exploit
quantum steering as the security resource typically operate in a tripartite architecture, whereas adapting them to a transmitter-receiver architecture can introduce a signaling loophole. Here we conceptually formalize the transmitter-device-independence on the basis of faithful
quantum steering and propose a practical transmitter-device-independent (TDI) QKD protocol within the 1sDI trust model. The TDI scheme operationalizes the implicit no-signaling condition by enforcing a causal isolation between the input information and the transmitted
quantum states, thereby eliminating the associated transmitter-side device vulnerabilities. In a proof-of-principle experiment, we validate the feasibility of the TDI protocol, achieving a key rate in the asymptotic limit of 410~bps over 27~km spool fiber. By delivering TDI security while retaining strong loss tolerance, our approach helps bridge the gap between security and practicality for real-world QKD deployments.
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All authors Qiang Zeng, Abhishek Mishra, Haoyang Wang, Zhiliang Yuan