1134 papers
This paper establishes a rigorous framework demonstrating that quantum contextuality is an intrinsic and necessary feature of universal fault-tolerant quantum computation, providing a new classification criterion for identifying which subsystem stabilizer codes and protocols can support universal gate sets.
This paper proposes a novel interpretation of quantum mechanics that derives the Born rule and explains the wave-like nature of reality by modeling particles as undergoing time-symmetric, non-real stochastic diffusion processes, thereby challenging the concept of physical superposition and offering a pathway to explain the emergence of classical behavior in macroscopic objects.
This paper formulates a WKB approach for quasiparticles with quartic dispersion, demonstrating that higher-order Airy-type functions and their hyperasymptotic corrections are essential for matching wave functions at turning points, leading to a generalized Bohr-Sommerfeld quantization condition that includes non-perturbative corrections even in the absence of tunneling.
This paper demonstrates that while dissipative ground state preparation for local stabilizer-encoded Hamiltonians can exponentially suppress errors under circuit-level noise without the overhead of full fault tolerance, dissipative quantum computation offers no greater noise resilience than the standard quantum circuit model.
This paper presents and experimentally demonstrates a hybrid quantum error correction and logical dynamical decoupling strategy on IBM transmon devices that significantly suppresses logical errors and enables the creation of high-fidelity entangled logical qubits.
This paper introduces new "vertical" intertwining operators that change the particle number in the quantum Calogero model for integer coupling, complementing existing "horizontal" operators to form a grid structure that enables the derivation of Liouville charges and a new basis of non-symmetric integrals through iterated intertwining.
This paper demonstrates that the maximum work extractable from a quantum system, quantified by its free energy, can be achieved via a universal protocol that does not require prior knowledge of the input state, thereby removing fundamental operational restrictions and extending optimal work extraction to infinite-dimensional systems.
This paper demonstrates that while stochastic Ising machines exhibit longer autocorrelation times for sampling neural-network quantum states of Heisenberg models, their massive parallelism projects a significant speed-up of 100 to 10,000 times over standard Metropolis-Hastings sampling, enabling efficient large-scale quantum simulations without requiring direct hardware deployment.
This paper demonstrates that while staggered fermions fail to capture (2+1)D topological phases in lattice QED, Wilson fermions successfully enable the realization of diverse topological states like Chern insulators and quantum spin Hall phases, thereby resolving ambiguities in Hamiltonian formulations and providing a theoretical foundation for future quantum simulations on near-term quantum computing platforms.
This study demonstrates that the interplay between magnon-induced Brillouin light scattering and optical spin-orbit coupling enables the nonreciprocal transformation of a Gaussian beam into an optical vortex beam by simultaneously breaking temporal symmetry via magnetic ordering and spatial symmetry via light focusing, thereby revealing that magnons can control the total angular momentum of light.
This paper presents the first experimental realization of a superconducting qubit system coupled to a coplanar-waveguide resonator lattice with flexible connectivity and gapped flat bands, demonstrating generalized readout techniques and photon-mediated interactions that pave the way for simulating diverse quantum spin models in various geometric spaces.
This paper proposes a non-Hermitian quantum computing model capable of solving all NP and problems in polynomial time by incorporating a non-unitary gate, demonstrating that its extraordinary computational power stems from the exponentially large physical resources required for implementation.
This paper establishes that the adversary bound from query complexity precisely characterizes univariate Quantum Signal Processing protocols and provides a framework for extending these results to the multivariate setting by reducing protocol existence and minimal space computation to feasibility and rank minimization problems within the adversary bound formalism.
This paper proposes a Quantum Physics-Informed Neural Network (QPINN) framework, supported by a GPU-accelerated simulation library and enhanced with energy conservation constraints to mitigate "black hole" barren plateaus, which solves 2D time-dependent Maxwell's equations with higher accuracy and fewer parameters than classical PINN baselines.
This paper introduces a nonlinear witness for dimensionally restricted nonlocality and demonstrates that its associated Popescu-Rohrlich box fraction serves as a resource for secure quantum key distribution, even in the absence of certified entanglement.
This paper establishes a unified framework based on homological algebra that guarantees the preservation of logical qubit isomorphism when embedding Calderbank-Shor-Steane (CSS) codes into modified structures with additional physical qubits and parity checks, while demonstrating how previous constructions fit within this general theory.
The paper introduces the Unitary Variational Quantum-Neural Hybrid Eigensolver (U-VQNHE), a scalable algorithm that enforces unitary neural transformations to resolve the normalization and divergence issues of its non-unitary predecessor, thereby significantly reducing measurement overhead while maintaining improved accuracy and stability for ground state estimation.
This paper proposes a quantum version of the EM algorithm for training semi-quantum restricted Boltzmann machines, demonstrating that this information-geometric approach effectively circumvents the barren plateau problem and outperforms gradient-based methods in quantum generative modeling.
This paper demonstrates that a classical solver utilizing a clustering approximation can efficiently simulate NMR spectra with linear resource scaling, thereby challenging the assumption that such problems inherently require exponential resources and necessitating a re-evaluation of the potential for quantum advantage in this domain.
This paper demonstrates that while block encoding the 3D heterogeneous Poisson equation for fracture flow offers exponential memory savings and a runtime advantage over classical methods, the inability to improve the effective condition number through separate preconditioner encoding remains a significant barrier to realizing full quantum advantage.