Sdim is the first open-source qudit stabilizer simulator supporting all dimensions, enabling circuit evaluation and sampling for qudit fault-tolerant quantum computing research.
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A programmable silicon photonic chip excited with single photons implements quantum reservoir computing for quantum state tomography, entanglement measurement via negativity, and classical tasks, with an imperfection mitigation technique that improves accuracy over the classical regime.
Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
Hybrid Path-Sums offer a new symbolic framework with rewriting rules and assertions to represent, simplify, and verify properties of hybrid quantum-classical programs.
A new method for unitary synthesis on quantum hardware cuts CNOT gates by up to 36% and compiles up to 553 times faster than standard tools on square and heavy-hex lattices.
Extensive charge monitoring in free fermion systems creates discontinuities in hydrodynamic profiles and suppresses transport in the Zeno limit by treating the non-local Lindbladian as localized impurities within a GHD description.
HAML meta-learns a mapping from control inputs and device parameters to effective two-qubit Hamiltonian coefficients via simulation training, then adapts online with few measurements, recovering coefficients where Schrieffer-Wolff perturbation theory fails.
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.
Reformulation of Cartan-Khaneja-Glaser decomposition for SU(2^n) via involutive automorphisms and symmetric Lie algebra decompositions yields a stable recursive factorization with open-source Python code validated on SU(8) and SU(16).
A resource estimation framework for distributed fault-tolerant quantum computers based on lattice surgery identifies feasible hardware configurations for eight applications across thousands of setups, showing that architecture design must be guided by resource analysis for scalability.
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.
Presents a tensor-parallel distributed MPS method with block-cyclic partitioning and pivoted QR that emulates Google's RCS benchmark at bond dimension 16384 on 32 nodes, claiming three orders of magnitude better accuracy than prior methods.
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.
Detection operator in 2D electronic spectroscopy operationally defines dephasing, with coherent emission retaining standard T2 connection while population observables encode population redistribution leading to distinct apparent linewidths.
Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
Noise in LUCJ sampling for QSCI on N2 expands the configuration space beyond the ideal ansatz and, when paired with recovery, produces more accurate CI energies than noiseless sampling.
A 500-logical-qubit quantum computer could reject laboratory-confined theories by surpassing the Planck-scale operation rate of 2^491 m^{-3} s^{-1}, with a 1600-qubit machine limited by the observable universe.
Qimax parallelizes the extended stabilizer formalism for GPU execution and reports faster simulation of near-Clifford circuits than Qiskit or Pennylane in selected cases.
XGBoost models trained on ≤16-qubit data predict eigensolver hyperparameters and reduce error by 0.12% on 28-qubit systems.
Lecture notes and accompanying library teach replica tensor network methods to compute circuit-averaged observables in random quantum circuits by mapping them to classical statistical mechanics models.
A review surveying coupling mechanisms in superconducting qubit-mechanical resonator hybrids and their extension to optomechanical architectures for quantum sensing applications.
citing papers explorer
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Sdim: A Qudit Stabilizer Simulator
Sdim is the first open-source qudit stabilizer simulator supporting all dimensions, enabling circuit evaluation and sampling for qudit fault-tolerant quantum computing research.
-
Quantum and classical processing with photonic quantum machine learning
A programmable silicon photonic chip excited with single photons implements quantum reservoir computing for quantum state tomography, entanglement measurement via negativity, and classical tasks, with an imperfection mitigation technique that improves accuracy over the classical regime.
-
Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
-
Hybrid Path-Sums for Hybrid Quantum Programs
Hybrid Path-Sums offer a new symbolic framework with rewriting rules and assertions to represent, simplify, and verify properties of hybrid quantum-classical programs.
-
Architecture-aware Unitary Synthesis
A new method for unitary synthesis on quantum hardware cuts CNOT gates by up to 36% and compiles up to 553 times faster than standard tools on square and heavy-hex lattices.
-
Generalized hydrodynamics of free fermions under extensive-charge monitoring
Extensive charge monitoring in free fermion systems creates discontinuities in hydrodynamic profiles and suppresses transport in the Zeno limit by treating the non-local Lindbladian as localized impurities within a GHD description.
-
Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning
HAML meta-learns a mapping from control inputs and device parameters to effective two-qubit Hamiltonian coefficients via simulation training, then adapts online with few measurements, recovering coefficients where Schrieffer-Wolff perturbation theory fails.
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Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
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.
-
Cartan-Khaneja-Glaser decomposition of $SU(2^n)$ via involutive automorphisms
Reformulation of Cartan-Khaneja-Glaser decomposition for SU(2^n) via involutive automorphisms and symmetric Lie algebra decompositions yields a stable recursive factorization with open-source Python code validated on SU(8) and SU(16).
-
Architecting Distributed Quantum Computers: Design Insights from Resource Estimation
A resource estimation framework for distributed fault-tolerant quantum computers based on lattice surgery identifies feasible hardware configurations for eight applications across thousands of setups, showing that architecture design must be guided by resource analysis for scalability.
-
Sampling (noisy) quantum circuits through randomized rounding
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
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Learning-Optimized Qubit Mapping and Reuse to Minimize Inter-Core Communication in Modular Quantum Architectures
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.
-
Tensor-Parallel Emulation of Quantum Circuits with Block-Cyclic Distributed Matrix Product States
Presents a tensor-parallel distributed MPS method with block-cyclic partitioning and pivoted QR that emulates Google's RCS benchmark at bond dimension 16384 on 32 nodes, claiming three orders of magnitude better accuracy than prior methods.
-
A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG
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.
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Detection Defines Dephasing in Two-Dimensional Electronic Spectroscopy of Materials: Coherent Field Emission versus Incoherent Population Observables
Detection operator in 2D electronic spectroscopy operationally defines dephasing, with coherent emission retaining standard T2 connection while population observables encode population redistribution leading to distinct apparent linewidths.
-
Shuttling of $\mathbb{Z}_4$ parafermions in an electronic ladder model
Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
-
MCMit: Mid-Circuit Measurement Error Mitigation
MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.
-
Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
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Noise and Configuration Recovery Impact on Quantum Selected Configuration Interaction
Noise in LUCJ sampling for QSCI on N2 expands the configuration space beyond the ideal ansatz and, when paired with recovery, produces more accurate CI energies than noiseless sampling.
-
Probing the Planck scale with quantum computation
A 500-logical-qubit quantum computer could reject laboratory-confined theories by surpassing the Planck-scale operation rate of 2^491 m^{-3} s^{-1}, with a 1600-qubit machine limited by the observable universe.
-
Qimax: Efficient quantum simulation via GPU-accelerated extended stabilizer formalism
Qimax parallelizes the extended stabilizer formalism for GPU execution and reports faster simulation of near-Clifford circuits than Qiskit or Pennylane in selected cases.
-
Accelerating Quantum Eigensolver Algorithms With Machine Learning
XGBoost models trained on ≤16-qubit data predict eigensolver hyperparameters and reduce error by 0.12% on 28-qubit systems.
-
Lecture Notes on Replica Tensor Networks for Random Quantum Circuits
Lecture notes and accompanying library teach replica tensor network methods to compute circuit-averaged observables in random quantum circuits by mapping them to classical statistical mechanics models.
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Fundamentals and Applications of Hybrid Electro- and Opto-mechanical system coupled to Superconducting Qubit: A Short Review
A review surveying coupling mechanisms in superconducting qubit-mechanical resonator hybrids and their extension to optomechanical architectures for quantum sensing applications.
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The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
A synthesis of expert insights from the ADAC Quantum Computing Working Group and member survey on the complementary roles of quantum and classical high-performance computing in future hybrid infrastructures.
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Review of Superconducting Qubit Devices and Their Large-Scale Integration
A review summarizing superconducting qubit types, DiVincenzo criteria implementations, coherence limits from defects, and large-scale integration strategies for quantum computing.