{"total":19,"items":[{"citing_arxiv_id":"2606.27411","ref_index":98,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder","primary_cat":"quant-ph","submitted_at":"2026-06-25T12:56:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.24933","ref_index":15,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning","primary_cat":"quant-ph","submitted_at":"2026-06-22T10:21:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Self-Modulating QFWP adds adaptive modulation to quantum fast-weight updates and memory to improve stability and performance on sequential learning tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.24932","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation","primary_cat":"quant-ph","submitted_at":"2026-06-22T10:10:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18916","ref_index":41,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Exceptional-Point-Anchored Variational Quantum Eigensolver for Non-Hermitian Many-Body Phase Diagrams: Bridging Skin-Effect Topology and Entanglement Criticality on NISQ Hardware","primary_cat":"quant-ph","submitted_at":"2026-06-17T10:43:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"B-VQE is a biorthogonal variational quantum eigensolver with exceptional-point detection and importance sampling that simulates non-Hermitian many-body models on NISQ hardware with reported energy errors below 5e-3.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05387","ref_index":40,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines","primary_cat":"quant-ph","submitted_at":"2026-06-03T19:46:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01110","ref_index":39,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture","primary_cat":"physics.geo-ph","submitted_at":"2026-05-31T09:04:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Hybrid quantum-classical FBPINN for acoustic FWI achieves lower L1 velocity error than classical baselines in ~8x fewer iterations with ~33% fewer parameters on anomaly and checkerboard benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17145","ref_index":30,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding","primary_cat":"quant-ph","submitted_at":"2026-05-16T20:30:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13271","ref_index":19,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"OAM-Induced Lattice Rotation Reveals a Fractional Optimum in Fault-Tolerant GKP Quantum Sensing","primary_cat":"quant-ph","submitted_at":"2026-05-13T09:49:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Fractional OAM charge ℓ=1.5 induces an optimal 67.5° GKP lattice rotation that reduces error rate 23.9× with <0.2% loss in Fisher information and yields 41% higher metrological capacity.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"prior NOON-state study [18] - we optimize this family of states end-to-end for phase estimation under realistic noise. Differentiable quantum programming- the paradigm of embedding parameterized quantum circuits in automatic-differentiation frameworks and training them via gradient descent - has recently emerged as a unifying methodology for quantum machine learning and quan- tum control [19,20]. In parallel work, we have applied this paradigm to NOON-state phase estimation [18], achieving up to1775%improvement in classical Fisher information; here we apply the sameStra wberry Fields-TensorFlowframework independently to fault-tolerant GKP sensing, with OAM lattice geometry as the trainable degree of freedom. Our work applies this paradigm to fault-tolerant quantum sensing: by"},{"citing_arxiv_id":"2605.07473","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization","primary_cat":"quant-ph","submitted_at":"2026-05-08T09:20:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A bilevel optimization method turns QAOA into a fully connected QBM that achieves 0.9559 target state probability noiseless and retains top probability under realistic noise levels.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"The target HamiltonianH 1 corresponding to the energy function is given by: H1 =b 1σ1 z +b 2σ2 z +b 3σ3 z +b 4σ4 z +w 12σ1 z σ2 z +w 13σ1 z σ3 z +w 14σ1 z σ4 z +w 23σ2 z σ3 z +w 24σ2 z σ4 z +w 34σ3 z σ4 z (24) The quantum state evolution equation derived from the QAOA circuit is given by: exp(iβpH0) exp(iγpH1)· · ·exp(iβ 2H0) exp(iγ2H1) exp(iβ1H0) exp(iγ1H1)|φ(0)⟩(25) wherepdenotes the number of QAOA circuit layers. Accordingly, the basic structure of an arbitraryk-th layer can be constructed as follows: H H H H Rz(2b1γk) Rz(2b2γk) Rz(2b3γk) Rz(2b4γk) Rx(2βk) Rx(2βk) Rx(2βk) Rx(2βk) Rz(2w12γk) q1 q2 q3 q4 Rz(2w13γk) Rz(2w14γk) Rz(2w23γk) Rz(2w34γk)Rz(2w24γk) Figure 1: Structure of the single-layer QAOA circuit for the 4-qubit fully connected"},{"citing_arxiv_id":"2604.25631","ref_index":7,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Local tensor-train surrogates for quantum learning models","primary_cat":"quant-ph","submitted_at":"2026-04-28T13:33:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"variational hybrid quantum-classical algorithms.New Journal of Physics, 18(2):023023, February 2016. URL:http://dx.doi.org/10.1088/1367-2630/18/2/023023,doi:10.1088/1367-2630/18/ 2/023023. [6] K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii. Quantum circuit learning.Phys. Rev. A, 98:032309, Sep 2018. URL:https://link.aps.org/doi/10.1103/PhysRevA.98.032309,doi: 10.1103/PhysRevA.98.032309. [7] Vedran Dunjko and Hans J. Briegel. Machine learning & artificial intelligence in the quantum domain, 2017. URL:https://arxiv.org/abs/1709.02779,arXiv:1709.02779. [8] Yunfei Wang and Junyu Liu. A comprehensive review of quantum machine learning: from nisq to fault tolerance.Reports on Progress in Physics, 87(11):116402, October 2024. URL:http://dx."},{"citing_arxiv_id":"2604.19320","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Single-shot quantum neural networks with amplitude estimation","primary_cat":"quant-ph","submitted_at":"2026-04-21T10:29:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"forms where shot execution is expensive. 2.2. Quantum amplitude estimation Quantum amplitude estimation (AE) is a quantum algorithm designed to estimate the probability amplitude associated with a target subspace more efficiently than classical MC sampling. Its foundation lies in Grover's algo- rithm [2] and the principle of amplitude amplification [23]. 5 Figure 1: Illustration of the architecture of QNN and our proposed inference framework.a. A conventional QNN inference framework using Monte Carlo sampling.b. Measurement probability of the conventional sampling, which gives the error scalingO(1/ √ N)with measurement shotsN.c. Our proposed QNN inference framework using a quantum amplitude estimation protocol."},{"citing_arxiv_id":"2604.19832","ref_index":18,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach","primary_cat":"quant-ph","submitted_at":"2026-04-20T23:03:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13735","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Reachability Constraints in Variational Quantum Circuits: Optimization within Polynomial Group Module","primary_cat":"quant-ph","submitted_at":"2026-04-15T11:21:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12323","ref_index":13,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum-Enhanced Single-Parameter Phase Estimation with Adaptive NOON States","primary_cat":"quant-ph","submitted_at":"2026-04-14T05:55:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Gradient-descent optimization of eight circuit parameters in a Strawberry Fields model yields CFI gains of 153% to 1775% and 8x to 133x more useful events per pulse versus Afek et al. (2010) for N=2-5, reaching 82% of Heisenberg limit at N=2 and 58% at N=5.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"1038/nphoton.2010.268 [11] Knill, E., Laflamme, R., & Milburn, G. J. A scheme for efficient quantum computation with linear optics.Nature 409, 46-52 (2001).doi:10.1038/35051009 [12] Gerry, C. C. & Hach, E. E. Genera- tion of NOON states via cross-Kerr in- teraction and homodyne measurement. Physical Review A82, 063804 (2010). doi:10.1103/PhysRevA.82.063804 [13] Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. Quantum circuit learning. Physical Review A98, 032309 (2018). doi:10.1103/PhysRevA.98.032309 [14] Kaubruegger, R., Vasilyev, D. V., Schulte, M., Hammerer, K., & Zoller, P. Quantum variational op- timisation of Ramsey interferom- etry and atomic clocks.Physical Review Letters123, 260505 (2019). doi:10."},{"citing_arxiv_id":"2604.05986","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Machine Learning for particle scattering entanglement classification","primary_cat":"quant-ph","submitted_at":"2026-04-07T15:13:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A 4-qubit QCNN classifies entanglement thresholds from fermion density profiles in the Thirring model more effectively than comparable classical CNNs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.04414","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks","primary_cat":"cs.ET","submitted_at":"2026-04-06T04:43:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"The transpilation pipeline converts an abstract vendor-independent circuitCabs into a backend-native circuitCnative through a sequence of compiler passes tailored to the target backend's gate set and connectivity. The correctness criterion requires thatCnative and Cabs produce the same unitary (up to a global phase) on all valid input states: U(C native) =e iϕU(C abs), ϕ∈[0,2π).(2) 4.3 Supported Backends Table 1 summarizes the quantum backends currently supported by our HAL, along with their key characteristics. Figure 2 provides a visual representation of the compatibility between our framework and each hardware platform. P. Kumaresan et al. 9/27 Table 1: Supported quantum hardware backends and their characteristics. Qubit counts reflect hardware available at the time of writing. Provider Device Qubits Technology Access IBM Quantum Brisbane 127 Superconducting Qiskit Runtime IBM Quantum Osaka 127 Superconducting Qiskit Runtime Amazon Braket IonQ Aria 25 Trapped Ion Braket SDK Amazon Braket Rigetti Ankaa-3 84 Superconducting Braket SDK Azure Quantum IonQ Harmony 11 Trapped Ion Azure SDK Azure Quantum Quantinuum H1 20 Trapped Ion Azure SDK IonQ (Direct) Forte 36 Trapped Ion IonQ API Rigetti (Direct) Ankaa-3 84 Superconducting pyQuil 4.4 Backend Selection and Routing When a user does not specify a target backend, the HAL provides an automatic backend se- lection mechanism that optimizes for a user-specified objective. LetB ={B1,B 2,...,Bm} represent the set of available backends. For each backendBk, the HAL computes a com- posite suitability scoresk based on the circuit characteristics and the backend's calibration data: sk =α·fidelity(Bk,C) +β·connectivity(Bk,C)−γ·queue_time(Bk),(3) where fidelity(Bk,C )estimates the expected circuit fidelity based on the average gate error rate and the circuit depth,connectivity(Bk,C )measures the degree to which the circuit's qubit interactions match the backend's coupling map, andqueue_time(Bk)is the estimated wait time "},{"citing_arxiv_id":"2604.03346","ref_index":46,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks","primary_cat":"quant-ph","submitted_at":"2026-04-03T10:24:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"sical data vectorx= (x1, . . . , xD) ∈X⊂R D, andOis a Hermitian observable. We have the hypothesis function generated byQ(θ) as fQ(θ)(x) =⟨0|U(x,θ) †OU(x,θ)|0⟩. The workflow of QPINNs is shown in Figure 1: we begin by choosing a quantum modelQ(θ) con- sisting of a PQCUand an observableO, which together define the hypothesis functionf Q(θ). Using the parameter shift rule [46], we obtain esti- mated derivatives off Q(θ). From these derivatives and guided by the PDE we want to solve, we eval- uate a loss function defined in Equations (5) and (6), then apply gradient descent or other algo- rithms to update the circuit parametersθ. In principle, each update brings the model's output closer to the true solution of the PDE."},{"citing_arxiv_id":"2506.19714","ref_index":18,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Conservative quantum offline model-based optimization","primary_cat":"quant-ph","submitted_at":"2025-06-24T15:20:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"COM-QEL integrates conservative objective models with quantum extremal learning to produce more reliable solutions than standard QEL on offline benchmark optimization tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.02625","ref_index":14,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers","primary_cat":"cs.LG","submitted_at":"2025-02-04T14:44:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Bayesian PSR with Gaussian processes and GradCoRe accelerates VQE SGD by reusing observations and minimizing per-step costs while reducing to standard PSR in special cases.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}