Poster Session 2
Paper ID: 80
Authors: Lukas Tarra, Nikolaus Würkner and Andreas Deutschmann-Olek
Title: Optimal Control of Number Squeezing in Split Bose-Einstein Condensates via Truncated Wigner Dynamics
Abstract: The generation of strongly number-squeezed states in Bose-Einstein condensates (BECs) is a central objective in quantum metrology and precision interferometry. However, accurately capturing and controlling quantum fluctuations during the splitting process in BEC experiments remains a significant challenge. While our previous work on splitting of BECs offers a robust framework for fast manipulation of the condensate without exciting classical collective dynamics, achieving precise control over quantum fluctuations of the split condensate - namely number imbalance and relative phase - requires going beyond mean-field dynamics. In this work, we present a layered optimal control approach to achieve significant squeezing of the number imbalance and relative phase in a split BEC. After splitting the BEC fast in a mean-field optimal way, we model quantum fluctuations as a two-mode Bose-Hubbard model and use a Truncated Wigner Approximation. This approach captures the evolution of number and phase fluctuations in the resulting Bosonic Josephson junction of the tunnel-coupled BECs. We then formulate an optimal control problem designed to optimize squeezing by utilizing the temporal modulation of the coupling strength between the two condensates as our control input. Finally, we introduce a model-based approach that maps the theoretically optimized coupling strength to the actual physical potential of the experimental setup. This provides a pathway toward generating highly squeezed states of BECs experimentally in the near future.
Paper ID: 81
Authors: Juntao Tu, Yuanlong Wang, Shuming Cheng, Shuixin Xiao and Zhibo Hou
Title: Estimation of a sparse multi-qubit Hamiltonian via compressed sensing
Abstract: Hamiltonian estimation is an effective approach in studying the structure and dynamical evolution of quantum systems. The difficulty in estimating the Hamiltonian is that an N-qubit Hamiltonian has 4^N-1 unknown parameters, requiring exponentially many equations for information extraction. In this paper we develop a method based on compressed sensing to estimate the Hamiltonian of a multi-qubit system. We identify a problem where as N increases, the common sufficient condition (Restricted Isometry Property) for compressed sensing often fails, obstructing the application of compressed sensing in (N>= 3)-qubit Hamiltonian estimation. To solve this problem, we propose a ``scale transformation" technique to restore RIP and ensure a compressive estimation of a k-sparse Hamiltonian using only O(k\log(N/k)) equations. In the numerical examples, we estimate the Hamiltonians of two 6- and 30-qubit systems, demonstrating the effectiveness of the method.
Paper ID: 87
Authors: Arnaud Remi, François Damanet and Christophe Geuzaine
Title: Solving Helmholtz problems on a quantum annealer
Abstract: Solving Helmholtz problems using finite elements leads to the resolution of a linear system which is challenging to solve for classical computers. We investigate how quantum annealers could address this challenge. We map the linear system arising from the finite element discretisation of the Helmholtz problem into a Quadratic Unconstrained Binary Optimization (QUBO) problem. Coupled with an adaptive precision refinement loop, this approach enables high-precision solutions despite the limited qubit counts of current hardware. We identify two key parameters in the success of the algorithm: the condition number of the linear system and hardware integrated control errors. Finally, we perform a Hamiltonian gap scaling analysis, providing insights into the potential for quantum annealing-based methods to outperform classical algorithms.
[1] A. Rémi, F. Damanet, C. Geuzaine, Phys. Rev. A 113, 012622 (2026).
Paper ID: 92
Authors: Xiang Li, Yong Wang, Lijun Liu, Yuanlong Wang, Qi Yu, Yiguang Hong and Shuming Cheng
Title: Direct QuantumQuantum State Tomography with Kirkwood-Dirac Quasiprobability
Abstract: Quantum state tomography is a central tool for the verification, calibration, and feedback control of quantum devices, yet practical schemes must carefully balance measurement overhead, computational cost, and statistical accuracy. We develop a systematic framework for direct quantum state estimation based on the Kirkwood-Dirac (KD) quasiprobability. Our key observation is that, under a mild complementary condition, a three-operator KD distribution provides an entrywise equivalent representation of the density matrix, so that each density-matrix element can be recovered directly from a KD coefficient through an explicit inversion formula. This feature is particularly attractive in settings where a coarse but informative state estimate is sufficient and low-latency processing is essential, such as rapid diagnosis of decohering subsystems or online quantum-control tasks requiring timely feedback. In such scenarios, the direct KD representation reduces the need for additional post-processing and therefore lowers the associated computational overhead. This representation naturally leads to a direct state-estimation procedure: the state estimate can be constructed directly from the observed KD data, without iterative optimization. Its physicality is then enforced by an additional projection onto the set of density matrices. Moreover, under a mutually unbiased choice of the reference and measurement bases, the inversion is provably optimally conditioned. On the measurement side, although KD quasiprobabilities can in principle be obtained through several protocols, standard approaches typically rely on weak measurements and post-selection, which are not experimentally friendly. To address this limitation, we introduce a SWAP-test-based protocol that directly extracts the real and imaginary parts of the KD data using only a constant number of projective measurement settings. To characterize the performance of this framework, we establish finite-sample guarantees under both the mean-squared error (MSE) and the trace-norm distance. A central difficulty is that, under the minimal number of measurement configurations, KD quasiprobabilities associated with different operators become statistically coupled, making the error analysis nontrivial. We overcome this issue by defining the relevant random variables at the level of individual measurement shots, which restores sample-wise independence and makes the analysis tractable. The resulting estimator achieves MSE scaling O(d^2/N), improving upon the O(d^3/N) scaling of standard Pauli-observable tomography. For rank-r target states, we further derive trace-norm guarantees with sample complexity O(r d^2/(epsilon^2 delta)), together with a matrix-Bernstein refinement O(r^2 d^2log(d/delta)/epsilon^2). Numerical simulations show that, under the same sample budget, KD tomography consistently achieves higher fidelity and lower MSE and trace-norm error than Pauli-based methods. Taken together, these results identify KD quasiprobability as a unified and operationally competitive framework for finite-sample quantum state tomography, combining directness, theoretical guarantees, and experimental feasibility, while also pointing to future extensions that exploit additional structure, such as low rank, to further improve scalability.
Paper ID: 93
Authors: Kaspar Schmerling, Andreas Deutschmann and Andreas Kugi
Title: Quantum State Preparation for Optimal Sensing of Impulse Disturbances in Feedback-Stabilized Resonators
Abstract: Sensing singular momentum kicks—such as atomic recoil or hypothetical dark matter interactions—relies on extracting precise impulse information from continuously monitored mechanical resonators. We present a framework for optimal sensing of impulses in Gaussian systems, which we further extend to non-equilibrium states, significantly improving impulse sensitivity for quantum and classical systems. First, we demonstrate optimal impulse detection from stochastic trajectories in feedback-stabilized systems, which is experimentally implemented on a nanomechanical trampoline resonator. Expanding upon this framework, we introduce an optimization-based dynamic state-preparation scheme to further advance the sensitivity of mechanical setups beyond the standard quantum limit. By modulating system parameters over time, we explicitly shape the estimation covariances to exploit non-equilibrium states, maximizing information gain exactly at the known impulse time.
Paper ID: 94
Authors: Grégoire Charleux
Title: Qubit control via hopping spins in carbon nanotube double quantum dots
Abstract: Electric-dipole spin resonance (EDSR) is the standard approach for high-fidelity single-spin control in carbon nanotube-based cQED architectures. However, it relies on high-frequency driving and complex control electronics, which can limit scalability. Here, we propose an alternative control paradigm based on spin-dependent hopping in carbon nanotube double quantum dots, requiring only low-frequency arbitrary waveform generators. Our approach exploits the spatial dependence of the effective g-tensor: shuttling the electron between the two dots rotates the local spin quantization axis, providing a mechanism for all-electrical single-qubit control. In the ideal limit of well-separated charge and spin energy scales, this process reduces to a sequence of rotations about fixed axes. However, in realistic carbon nanotube devices, this separation is not sufficient, and spin evolution during the shuttling phase plays a non-negligible role. We develop a theoretical framework that accounts for the full time-dependent charge–spin dynamics during the transfer, allowing us to move beyond the instantaneous-shuttling approximation. By projecting the resulting evolution onto the logical subspace, we extract effective qubit operations and construct optimized gate sequences. Our results demonstrate that high-fidelity control remains achievable despite the absence of clear scale separation, and identify the key parameters governing gate performance. These results establish hopping-based control as a promising low-bandwidth alternative to EDSR, opening new avenues for scalable spin qubit architectures in carbon nanotubes.
Paper ID: 100
Authors: Shruti Jain, Tobias Hartung, Karl Jansen and Hernan Leövey
Title: Markov Chain Monte Carlo with Imaginary Time Evolution for Quantum Portfolio Optimisation
Abstract: Portfolio optimisation is a fundamental problem in finance that involves allocating capital across assets to balance expected returns and risk. This problem can be formulated as the minimisation of a cost function subject to constraints such as asset selection limits, budget bounds, and discrete allocations. The combinatorial nature of these constraints results in an exponentially growing search space, making exact solutions computationally challenging to find. In this work, we propose a Variational Quantum Algorithm(VQA)inspired approach to optimise a real-world cost function subject to constraints. We construct a parameterised quantum circuit to approximate Imaginary Time Evolution (ITE). ITE evolves a quantum state in imaginary time, causing higher-energy components to decay exponentially faster than lower-energy ones, so that the state naturally converges to the ground state of the Hamiltonian. Exact ITE follows a strictly downhill path in the cost-function landscape, causing the cost to decrease monotonically toward a minimum. But since ITE is non-unitary, an effective approximation is required for implementation on quantum hardware. At the same time, the trigonometric structure of parameterised quantum circuits leads to highly oscillatory, non-convex optimisation landscapes with multiple local minima. To address these challenges, we explore Markov Chain Monte Carlo(MCMC)based optimisers, enhanced with supplementary data to accelerate convergence, escape local minima more effectively, and approach the optimal value more reliably. We present convergence plot comparisons against standard optimisers such as COBYLA, BFGS, and COBYQA. Additionally, we provide a priori estimates of resource requirements and hyperparameter selection, thus enabling the choice of ideal hyperparameters that could lead to better optimised values based on intuitive metrics like final resolution, global search space exploration, and acceptance rates.
Paper ID: 37
Authors: Nirupam Basak and Goutam Paul
Title: Faster than MIP Decoding of Graph Codes with a Single Logical Qubit
Abstract: The maximum-likelihood decoding (MLD), which is optimal for any error-correcting code, is known to be NP-hard. The standard technique for solving such MLD problems for quantum states is typically done using a mixed-integer program (MIP). In this work, we develop a faster decoding method than the MIP for graph codes, a class of stabilizer quantum errorcorrecting codes constructed from graph states. Our proposed decoder exploits the structural properties of the underlying graph states. Although different error patterns may yield the same syndrome, we demonstrate that the post-measurement state follows a well-defined structure determined by the projective syndrome measurement. Building on this idea, we introduce a hierarchical decoder in which each level can be solved in polynomial time. Additionally, this decoder achieves optimal decoding performance at the lower levels of the hierarchy. This strategy avoids the need to perform full maximum-likelihood decoding of graph codes. Numerical results illustrate the effectiveness of the proposed approach.
Paper ID: 38
Authors: Krishnakanta Barik and Goutam Paul
Title: Quantum Approximate Optimization for Decoding of Low-Density Parity-Check Codes
Abstract: Decoding Low-Density Parity-Check (LDPC) codes is a fundamental problem in coding theory, and Belief Propagation (BP) is one of the most popular methods for LDPC code decoding. However, BP may encounter convergence issues and suboptimal performance, especially for short-length codes and in high-noise channels. The Quantum Approximate Optimization Algorithm (QAOA) is a type of Variational Quantum Algorithm (VQA) designed to solve combinatorial optimization problems by minimizing a problem-specific cost function. In this paper, we present a QAOA-based decoding framework for LDPC codes by formulating a decoding cost function that incorporates both parity-check constraints and soft channel reliability information. The resulting optimization problem is solved using QAOA to search for low-energy configurations corresponding to valid codewords. We test the proposed method through extensive numerical experiments and compare its performance with BP decoding. The experimental results demonstrate that the QAOA-based decoder achieves a higher probability of correctly recovering the transmitted codeword than BP across multiple experimental settings.
Paper ID: 40
Authors: Gia Dang, Jim Basilakis, Weisheng Si and Belal Alsinglawi
Title: Implementing CCZ Gates with Variation of Gate Teleportation for Quantum Homomorphic Encryption on NISQ Platform
Abstract: While quantum computing technologies are revolutionising key industries, distributed quantum hardware services are dominated by quantum providers such as IBM, Google, and AWS. It raises critical data security concerns across sectors such as banking, defence, and healthcare. To address this issue, Quantum Homomorphic Encryption (QHE) has emerged as a solution that enables computations on encrypted quantum data while preserving privacy. Despite its promise, deploying QHE remains challenging due to circuit complexity and the noise in today’s quantum systems. In this work, we confront these barriers directly by implementing QHE on Noisy Intermediate-scale Quantum (NISQ) devices using the Variation of Gate Teleportation (VGT) scheme. In particular, we focus on implementing the CCZ gate, a key non-Clifford gate that makes the set of quantum gates universal when combined with Clifford gates. By leveraging the techniques from the Classical Quantum Circuit (CQC)-QHE framework proposed by Ortega et al. in 2025, our implementation reduces computational cost and improves resource efficiency. As a result, our approach can support 7 qubits and 14 T-gates in the circuit without large errors, improving on existing QHE implementations.
Paper ID: 41
Authors: Adam Lawrence and Sam Olds
Title: Learning Representations of Quantum Datacenter Network States
Abstract: Automated resource management in quantum datacenters requires a deep understanding of network states, including the instantaneous availability of QPUs, optical switches, and entanglement links. However, current approaches lack general-purpose learned representations. We present a graph neural network encoder that learns such representations, capturing both structural topology and dynamic resource availability. Our architecture combines TransformerConv layers with three complementary positional encoding schemes (distancebased, random-walk, Laplacian) to model multi-scale structural and dynamic information. We train on 10,000 synthetic states from 15 topology instances spanning four architectural families (Clos, Fat-Tree, BCube, QFly) using a multi-task objective that integrates metric learning, utilization regression at three granularities, and identity classification. On held-out test topologies from training families, the encoder achieves mean absolute errors of 2.9% ± 0.3% (graph-level), 3.0%±0.3% (node-level), and 1.1%±0.2% (edgelevel) across five random seeds, matching or outperforming MLP, GCN, and GAT baselines. The learned representations demonstrate strong crossarchitecture generalization: evaluation on three entirely unseen topology families (Ring, Grid, Dragonfly) yields only 24% degradation in graph-level error. Downstream task evaluation confirms practical utility and without task-specific fine-tuning, encoder embeddings enable 95.3% accuracy (0.992 ROC-AUC) for entanglement path feasibility prediction, outperforming raw features and frozen baseline embeddings by 2.8 percentage points while maintaining sub-3ms inference latency. These results establish that learned quantum network representations capture transferable resource dynamics, providing a foundation for scalable network control.
Paper ID: 43
Authors: Brennan Bell, Andreas Trügler, Konstantin Beyer and Paul Erker
Title: Hardware-Agnostic Modeling of Quantum Side-Channel Leakage via Conditional Dynamics and Learning from Full Correlation Data
Abstract: We study a sequential coherent side-channel model in which an adversarial probe qubit interacts with a target qubit during a hidden gate sequence. Repeating the same hidden sequence for N shots yields an empirical full-correlation record: the joint histogram b Pg(b) over probe bit-strings b ∈ {0, 1}k, which is a sufficient statistic for classical post-processing under identically and independently distributed (i.i.d.) shots but grows exponentially with circuit depth. We first describe this sequential probe framework in a coupling- and measurementagnostic form, emphasizing the scaling of the observation space and why exact analytic distinguishability becomes intractablewith circuit depth. We then specialize to a representative instantiation (a controlled-rotation probe coupling with fixed projective readout and a commuting Rx gate alphabet) where we (i) derive a depth-dependent leakage envelope whose maximizer predicts a coupling band as a function of depth if the measurement data is reduced to marginal statistics, and (ii) provide an operational decoder, via machine learning, a single parameter-conditioned map from b Pg to Alice’s per-step gate labels, generalizing across coupling and noise settings without retraining.
Paper ID: 45
Authors: Ban Tran, Nahid Binandeh Dehaghani, Rafal Wisniewski, Susan Mengel and A. Pedro Aguiar
Title: Quantum-Assisted Trainable-Embedding Physics-Informed Neural Networks for Parabolic PDEs
Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs) by embedding governing physical laws directly into the training objective. Recent advances in quantum machine learning have motivated hybrid quantum–classical extensions aimed at enhancing representational capacity while remaining compatible with near-term quantum hardware. In this work, we investigate trainable embedding strategies within quantum-assisted PINNs for solving parabolic PDEs, using oneand two-dimensional Heat equations as canonical benchmarks. We introduce two quantum-assisted architectures that differ in their embedding components. In the first approach, a classical feed-forward neural network generates trainable feature maps for quantum data encoding (FNN-TE-QPINN). In the second, the embedding stage is realized entirely by a parameterized quantum circuit (QNN-TE-QPINN), yielding a fully quantum feature map. Our findings emphasize the critical role of embedding design and support hybrid quantum–classical approaches for parabolic PDE modeling in the NISQ era.
Paper ID: 50
Authors: Rishi Koushik Reddy Thippireddy and Aswath Babu H
Title: Capacity and Architecture in Quantum-Generated Linear Classifiers: An Image Classification Study
Abstract: Expectation-value quantum classifiers form a broad class of supervised quantum machine learning models in which predictions are obtained from observables measured on parameterized quantum states. When expressed in a fixed operator basis, these models are equivalent to linear classifiers in an induced feature space, with weights determined by quantum expectation values. Within this setting, the empirical role of architectural design choices remains insufficiently understood. We study how circuit architecture influences performance in quantum-generated linear classifiers for image classification. Classical images are mapped to Pauli feature representations and weight matrices are generated by variational quantum circuits. A full factorial design evaluates five factors: feature representation, circuit capacity (number of circuits K), depth, ansatz family, and initialisation. Additive and interaction effects are quantified using analysis of variance on MNIST and Fashion-MNIST. Results show that capacity contributes primarily through additive logarithmic scaling, while circuit geometry and initialisation determine interaction structure. Feature representation has limited standalone impact but modulates architectural sensitivity. These findings provide a systematic empirical characterisation of architectural behaviour in quantum-generated linear classifiers.
Paper ID: 51
Authors: Vinay Maurya, Nikita Kumari and Manav Bhatnagar
Title: Adaptive Threshold Voting Photon-Counting Receiver for Quantum-Enabled Wireless Systems
Abstract: This paper presents a practical photon-counting receiver with adaptive threshold voting (ATV) for quantumenabled wireless optical links. The proposed receiver employs structured temporal repetition of coherent states combined with adaptive aggregation, where both per-bin detection thresholds and aggregation rules are optimized using analytically grounded statistical models. The proposed framework improves detection reliability in photon-starved regimes while maintaining low implementation complexity. Numerical results demonstrate that the ATV receiver achieves competitive performance relative to conventional photon-counting and single-shot Helstrom benchmark receivers through structured repetition and statistical postprocessing. In addition, the proposed framework introduces an inherent physical-layer security advantage arising from adaptive aggregation thresholds, which create decoding asymmetry against uninformed adversaries..
Paper ID: 5
Authors: Arnaud Remi, François Damanet and Christophe Geuzaine
Title: Variational quantum algorithm for solving Helmholtz problems with high order finite elements
Abstract: Discretizing Helmholtz problems via finite elements yields linear systems whose efficient solution remains a major challenge for classical computation. In this paper, we investigate how variational quantum algorithms could address this challenge. We first show that, for regular meshes, a block encoding of the operators A and A^†A arising from the high-order finite element discretization of Helmholtz problems can be designed, resulting in a quantum circuit of depth O(p^3 poly log(Np)) with N the number of elements and p the order of the finite elements. Then we apply our algorithm to a one-dimensional Helmholtz problem with Dirichlet and Neumann boundary conditions for various wavenumbers.
Paper ID: 19
Authors: Yash Abhijit Patil and Hariharan Ravishankar
Title: Cross-Problem Parameter Transfer for QAOA: From MaxCut to MAX-2-SAT
Abstract: The Quantum Approximate Optimization Algorithm (QAOA) is a promising approach for combinatorial optimization, but choosing good variational schedules is difficult due to rugged loss landscapes and depth-related trainability issues. A practical remedy is parameter transferability, where schedules tuned on one instance are reused on another. We study cross-problem transfer from MaxCut (donor) to MAX-2-SAT (acceptor). First, we present a notation-consistent pipeline that encodes MAX-2-SAT clauses into a QUBO and then an Ising Hamiltonian, producing a cost operator compatible with standard QAOA implementations. Building on this, we introduce a donor–acceptor framework that (i) represents problem structure with permutation-invariant graph and clause features, (ii) casts donor selection as a learning-to-rank task over candidate schedules, and (iii) transfers schedules to acceptors without re-tuning, with the option of warm starts. The paper offers an end-to-end recipe—covering dataset construction, Hamiltonian assembly, schedule reuse, and model-guided donor ranking—that can be reused for other clause-based problems. Our formulation aligns with recent work on QAOA parameter transfer while addressing the cross-problem setting and providing a clear blueprint for applying transferability beyond MaxCut to MAX-2-SAT.
Paper ID: 6
Authors: Jianlong Lu, Hanqiu Peng and Ying Chen
Title: Transformer-Based Neural Quantum Digital Twins for Many-Body Quantum Simulation and Optimal Annealing Schedule Design
Abstract: We introduce Transformer-based Neural Quantum Digital Twins (Tx-NQDTs) to simulate full adiabatic dynamics of many-body quantum systems, including ground and low-lying excited states, at low computational cost. Tx-NQDTs employ a graph-informed Transformer neural network trained to predict spectral properties (energy levels and gap locations) needed for annealing schedule design. We integrate these predictions with an adaptive annealing schedule design based on first-order adiabatic perturbation theory (FOAPT), which slows the evolution near predicted small gaps to maintain adiabaticity. Experiments on a D-Wave quantum annealer (N = 10, 15, 20 qubits, 12 control segments) show that Tx-NQDT-informed schedules significantly improve success probabilities despite hardware noise and calibration drift. The optimized schedules achieve success probabilities 2.2-11.7 percentage points higher than the default linear schedule, outperforming the D-Wave baseline in 44 of 60 cases. These results demonstrate a practical, data-driven route to improved quantum annealing performance on real hardware.
Paper ID: 14
Authors: Alan Daleth Hernandez Barreto, Maria Fernanda Velasco Campos, Karol Yenaro Morales Escobar and Jose Luis Perez Estudillo
Title: Nanosecond-Scale Neuromorphic Processing Kernels for Quantum Error Correction: A Hardware-Software Co-Design
Abstract: Real-time Quantum Error Correction (QEC) imposes strict timing constraints that challenge the integration of classical decoding algorithms into scalable control electronics. While approaches such as Minimum Weight Perfect Matching (MWPM) achieve high accuracy, their hardware realization often entails non-deterministic latency and significant resource consumption. This paper proposes a hardware-software co-design of a neuromorphic processing kernel based on Spiking Convolutional Neural Networks (ConvSNNs) for real-time QEC. By leveraging a "temporal folding" architecture, the spatiotemporal syndrome volume is compressed into a 2.5D feature map, bypassing the memory overhead of standard 3D convolutions. We introduce an optimized hardware implementation of Leaky Integrate-and-Fire (LIF) neurons utilizing a subtractive reset mechanism and static pre-activation batch normalization, achieving zero-cost stabilization in hardware. Furthermore, a hardware-aware calibration strategy is implemented to adapt neuronal firing thresholds to the code distance. This calibration optimizes the trade-off between noise suppression and signal sensitivity, reducing network switching activity to approximately 18% for enhanced energy efficiency. For a code distance of d = 3, the synthesized kernel utilizes 181 LUTs (< 2% of the device) [3], offering a deterministic and compact solution suitable for first-line decoding in cryogenic quantum control systems.