{"total":23,"items":[{"citing_arxiv_id":"2605.21286","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Software Between Quantum and Machine Learning -- And Down to Pulses","primary_cat":"quant-ph","submitted_at":"2026-05-20T15:20:07+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09226","ref_index":15,"ref_count":4,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantum Injection Pathways for Implicit Graph Neural Networks","primary_cat":"quant-ph","submitted_at":"2026-05-09T23:51:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.","context_count":2,"top_context_role":"background","top_context_polarity":"background","context_text":"into a quantum state by a data-dependent unitary, processed by a parametrized quantum circuit (PQC), and read out through measurements of chosen observables [11], [12]. Applied to graph-structured data, this template has been instantiated as quantum graph kernels, quantum graph neural networks, hy- brid graph classifiers, and graph-generation pipelines [13]- [16]. Recent work has also used graph neural networks to an- alyze parameterized quantum circuits themselves [17]. These models, however, remainexplicit finite-depth architectures. This matters because depth is a central design constraint for PQC-based models, and one that equilibrium methods are well-positioned to relieve. Deeper circuits are more expressive"},{"citing_arxiv_id":"2605.07295","ref_index":15,"ref_count":9,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Distributed Switching Protocol for Quantum Networks","primary_cat":"quant-ph","submitted_at":"2026-05-08T06:04:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A distributed switching protocol for unbuffered quantum networks uses cooperative BSA selection and bi-path reservations to achieve high link success rates under load in simulations.","context_count":3,"top_context_role":"background","top_context_polarity":"background","context_text":"That controller receives, manages, and schedules all requests to utilize the resources in the network. However, in the distributed approach, there are no centralized controllers. Therefore, all nodes are required to collaborate to handle demands in the network. A. Motivation Recently, new architectures, routing protocols, and resource assignment schemes have been proposed for BSA pool al- location [16], [17] or EPPS pool allocation [18] through a single switch with centralized control. Those can extend the idea of quantum link establishment with two end nodes and one support node [19]. However, forwarding of photons in a switched network composed of a chain of multiple optical switches across single or multiple areas has not been thoroughly investigated."},{"citing_arxiv_id":"2605.00302","ref_index":10,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantum Data Loading for Carleman Linearized Systems: Application to the Lattice-Boltzmann Equation","primary_cat":"quant-ph","submitted_at":"2026-05-01T00:10:50+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"l=1 clLl ,(1) wherec l ∈C, eachL l ∈C N×N is either a unitary or specifically designed non-unitary matrix, and the imposed constraintN s ∼ O(poly(logN)). In this section, we demonstrate how to embed specific kinds of non-unitaryL l terms into unitary matrices resulting in an LCU withN s terms. We do this by building off of the approach introduced in [10, 11, 17], which requires the following definition. Definition 1.Henceforth, letV, W⊂C N be vector spaces overC. Suppose thatW⊂Vand thatQ:W→V is a unitary operator onW, i.e., for anyw 1, w2 ∈W,w † 1Q†Qw2 =w † 1w2. Then, a unitary operator Q:V→Vis said to be a unitary completion ofQif Qw=Qw,for allw∈W. Next, letQbe trivial onW ⊥, whereW ⊥ is the orthogonal complement ofW."},{"citing_arxiv_id":"2605.00118","ref_index":5,"ref_count":4,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Toward Secure Multitenant Quantum Computing: Circuit Affinity, Crosstalk Patterns, and Grouping Strategies","primary_cat":"quant-ph","submitted_at":"2026-04-30T18:18:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Crosstalk patterns between quantum circuits on IBM processors are predictable by circuit type and hardware architecture, with high intra-revision consistency and topological decoupling between lattice types.","context_count":2,"top_context_role":"background","top_context_polarity":"support","context_text":"computing evolves as a computational paradigm and demand for quantum computing as a service (QCaaS) increases, queue lengths and wait times increase proportionally. Multitenancy, a technique which allows multiple users to concurrently access shared hardware, improves hardware uti- lization [2]. However, this paradigm introduces security and integrity risks [3], [4], [5], [6], [7], [8], [9], [10]. This work focuses on crosstalk, which has been shown as an attack vector for fault-injection [3], [4], [5] as well as a side-channel for leaking information [6], [9]. Previous works have shown that specific circuit designs can be developed to reduce the integrity of quantum computation by producing excessive amounts of crosstalk through high-"},{"citing_arxiv_id":"2604.21274","ref_index":7,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Random Access Codes: Explicit Constructions, Optimality, and Classical-Quantum Gaps","primary_cat":"quant-ph","submitted_at":"2026-04-23T04:36:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Geometric characterization of optimal classical RACs with explicit constructions, optimality proofs for several families, and a quantum RAC establishing classical-quantum separation for the (2^k-1, k) family.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20912","ref_index":60,"ref_count":4,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantum-HPC Software Stacks and the openQSE Reference Architecture: A Survey","primary_cat":"quant-ph","submitted_at":"2026-04-22T01:56:58+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":2,"top_context_role":"background","top_context_polarity":"background","context_text":"Executors run assigned tasks on a target hardware, be it cloud, HPC or QPU. Currently, Tierkreis supports executors forSlurm, portable batch system (PBS) and pjsub on the HPC side; and Nexus and direct QPU submission on the quantum side. An example of a hybrid biochemistry application using Tierkreis combines existing HPC chemistry application with quantum kernels in a complex workflow [70]. 5.8 Stack 8: Quantum Brilliance Quantum Brilliance (QB) providesQHPC capabilities through its queue utility (QTIL) [53]. Imple- mented in Go (Golang),QTIL is responsible for quantum resource allocation and task scheduling, interfacing with the QPU web server via API calls. Within the QB framework,QTIL integrates with the client SDK and the quantum development"},{"citing_arxiv_id":"2604.19869","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Practical HPCQC Integration with QDMI: A Real-Hardware Case Study with IQM Systems","primary_cat":"quant-ph","submitted_at":"2026-04-21T18:00:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A QDMI-based adapter for IQM quantum hardware enables reusable integration with Slurm and Qiskit in HPC centers, with open-source code provided.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15985","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Machine Learning and Deep Learning in Quantum Materials: Symmetry, Topology, and the Rise of Altermagnets","primary_cat":"cond-mat.mes-hall","submitted_at":"2026-04-17T12:02:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"science, allowing researchers to predict ground -state energies, lattice constants, and electronic band structures with high accuracy [1-3]. However, the success of DFT has birthed a new crisis. The computational cost of standard DFT calculations scales roughly as the cu be of the number of electrons (O(N 3)), rendering the simulation of large supercells, disordered systems, or complex magnetic textures prohibitively expensive [4]. While ML offers a surrogate, high -fidelity ground truth data remains essential; recent advances [5] demonstrate that many -body Quantum Monte Carlo (QMC) methods can surpass standard DFT in property prediction for 2D materials, providing a higher-accuracy tier of training data for future architectures. When one considers the combinatorial vastness of chemical"},{"citing_arxiv_id":"2604.14955","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Three ways to share a QPU: Scheduling strategies for hybrid Quantum-HPC applications","primary_cat":"quant-ph","submitted_at":"2026-04-16T12:52:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Three scheduling strategies for hybrid quantum-HPC systems cut classical resource use by up to 64% or boost QPU utilization depending on workload balance, validated on real hardware.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":", Chen, Y., Stade, Y., Geiger, M., et al., 2023. Challenges in hpcqc integration, in: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 405-406. doi:10.1109/QCE57702.2023.10304. [25] Engineering, E.C., 2025. Hybrid classical-quantum cluster- ing aggregation.https://github.com/E4-Computer-Engineering/ clustering-mis. [26] Esposito, A., Haus, U.U., 2025. Slurm heterogeneous jobs for hybrid classical-quantum workflows.arXiv:2506.03846. [27] Esposito, A., Jones, J.R., Cabaniols, S., Brayford, D., 2023. A Hy- brid Classical-Quantum HPC Workload, in: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), IEEE Computer Society, Los Alamitos, CA, USA."},{"citing_arxiv_id":"2604.05505","ref_index":22,"ref_count":9,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Qurator: Scheduling Hybrid Quantum-Classical Workflows Across Heterogeneous Cloud Providers","primary_cat":"quant-ph","submitted_at":"2026-04-07T06:58:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.","context_count":3,"top_context_role":"background","top_context_polarity":"background","context_text":"tion for Computing Machinery, New York, NY, USA, Article 1, 11 pages. doi:10.1145/2832105.2832109 [55] Nils Quetschlich, Lukas Burgholzer, and Robert Wille. 2023. MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing.Quantum(2023). doi:10.22331/q-2023-07- 20-1062MQT Bench is available athttps://www.cda.cit.tum.de/ mqtbench/. [56] Andrei Radulescu and Arjan JC Van Gemund. 2000. Fast and effective task scheduling in heterogeneous systems. InProceedings 9th het- erogeneous computing workshop (HCW 2000)(Cat. No. PR00556). IEEE, 229-238. doi:10.1109/HCW.2000.843747 [57] Andrei Radulescu and Arjan J. C. Van Gemund. 1999. FLB: Fast Load Balancing for Distributed-Memory Machines. InProceedings of the"},{"citing_arxiv_id":"2604.03445","ref_index":127,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management","primary_cat":"quant-ph","submitted_at":"2026-04-03T20:37:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"such as Qiskit Runtime [49] and Braket Hybrid Jobs [6, 93] bind classical and quantum resources via a session mechanism enabling co-allocation with dedicated QPU access. Late-Binding Runtimes:These middleware systems support loosely-coupled scenarios requir- ing runtime adaptability. Representative systems include QFaaS [84], Tierkreis [130], Qibo [33], Quantum Framework [127], Q-IRIS [78], Divi [102], and Orquestra [144]. Qiskit Serverless [51, 52] manages hybrid workloads using Apache Ray [81] for distributed execution, providing task decora- tors for resource mapping across CPUs, GPUs, and QPUs. Covalent [28, 24] models applications as DAGs with a dispatch service that maps tasks to user-defined executors via Dask [30]; however,"},{"citing_arxiv_id":"2602.04831","ref_index":76,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Review of Superconducting Qubit Devices and Their Large-Scale Integration","primary_cat":"quant-ph","submitted_at":"2026-02-04T18:19:32+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":1.0,"formal_verification":"none","one_line_summary":"A review summarizing superconducting qubit types, DiVincenzo criteria implementations, coherence limits from defects, and large-scale integration strategies for quantum computing.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"at a rate of 𝑒𝑒−� 𝐸𝐸𝐽𝐽/𝐸𝐸𝐶𝐶 and the anharmonicity only reduces as � 𝐸𝐸𝐽𝐽/𝐸𝐸𝐶𝐶 [72]. The transmon qubit is the most commonly used superconducting qubit for large -scale integrations [9], [13], [21], [73], [74]. It has achieved high 𝑇𝑇1 (hundreds of microseconds or even > 1𝑚𝑚𝑠𝑠 with appropriate engineering) [35] [75] and 𝑇𝑇2 ∗ times [25], [75], [76]. A transmon qubit is a charge-mode qubit, and it has a long 𝑇𝑇2 as its frequency is insensitive to charge noise. Its 𝑇𝑇1 is currently limited by two- level-system defects (see Section VIII) and other losses. Since a transmon qubit has less anharmonicity (Fig. 6), it can be approximated as an L-C tank oscillator with an additional non-linear term (−𝐸𝐸𝐽𝐽𝜑𝜑4/24) [14]."},{"citing_arxiv_id":"2512.07808","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout","primary_cat":"quant-ph","submitted_at":"2025-12-08T18:41:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LUNA achieves up to 10.95x area reduction and 30% lower latency for qubit readout using integrator-based preprocessing and LogicNet LUT synthesis with minimal fidelity loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.08153","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony","primary_cat":"quant-ph","submitted_at":"2025-10-09T12:35:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.11552","ref_index":71,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction","primary_cat":"quant-ph","submitted_at":"2025-06-13T08:02:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.21172","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Iceberg Beyond the Tip: Co-Compilation of a Quantum Error Detection Code and a Quantum Algorithm","primary_cat":"quant-ph","submitted_at":"2025-04-29T20:47:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.17548","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantum Autoencoder for Multivariate Time Series Anomaly Detection","primary_cat":"quant-ph","submitted_at":"2025-04-24T13:40:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A quantum autoencoder for multivariate time series anomaly detection achieves competitive performance with neural-network autoencoders using fewer trainable parameters.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.13532","ref_index":15,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration","primary_cat":"quant-ph","submitted_at":"2025-04-18T07:53:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Quantum walks integrated with variational circuits and CUDA-Q acceleration generate high-fidelity adaptive probability distributions for 1D financial modeling and 2D digit patterns.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.01164","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Diversity Methods for Improving Convergence and Accuracy of Quantum Error Correction Decoders Through Hardware Emulation","primary_cat":"quant-ph","submitted_at":"2025-04-01T20:04:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FPGA emulator tests 10^13 error patterns in 20 days and diversity BP decoder matches BP+OSD logical error rates with 30-80% average speed gains and far less post-processing for QLDPC codes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.02497","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG","primary_cat":"cs.SE","submitted_at":"2025-03-04T11:04:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2501.01434","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Simulating Bell inequalities with Qibo","primary_cat":"physics.ed-ph","submitted_at":"2024-12-18T11:43:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Educational modules and Qibo implementations for simulating Bell inequality violations to teach entanglement, hidden variables, and non-locality.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2409.00433","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"High-Precision Multi-Qubit Clifford+T Synthesis by Unitary Diagonalization","primary_cat":"quant-ph","submitted_at":"2024-08-31T12:10:32+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}