1157 papers
This paper introduces a compositional "approximate sample and query" access model for quantum encoded data, demonstrating its utility in achieving polynomial improvements for distributed inner product estimation and partially characterizing the capabilities of time-limited fault-tolerant quantum circuits.
This paper establishes a generalized -pairing framework for interacting non-Hermitian Hubbard models in arbitrary dimensions, revealing novel phenomena such as asymmetric eigenoperators, spatially modulated states, and anomalous skin effects that unify and extend traditional symmetry concepts beyond Hermitian limits.
This paper demonstrates that the proposed quantum-optical multimode mixer model for gravitational redshift is only valid to first order under specific conditions and offers a partial solution requiring a transformation matrix with auxiliary modes at least equal in number to the photonic state modes.
This paper investigates the impact of truncating infinite bosonic basis sets on the accuracy of vibrational Hamiltonian spectra in quantum computing, specifically highlighting how basis closure disruption affects matrix element evaluation and variational convergence through numerical analysis of a double-well potential model.
This paper proposes a protocol for generating superpositions of orthogonally squeezed states in a qubit-oscillator system via quadratic coupling, evaluates its robustness against decoherence, and demonstrates its potential application in a new quantum error-correcting code.
This paper establishes a unified framework for classical and quantum speed limits by demonstrating that temporal Fisher information is bounded from above by physical costs (such as entropy production and Hamiltonian variance) and from below by statistical distances, thereby constraining the minimal time required for state transformations.
The authors develop a non-Hermitian generalization of the numerical renormalization group (NRG) method to non-perturbatively solve the non-Hermitian Kondo model, revealing a novel phase with a stable fixed point and complex eigenspectrum in the pseudogap regime while providing open-source code for further exploration.
This paper investigates quantum kinetically constrained models with symmetry to demonstrate how the interplay of constraints and chiral symmetry induces Hilbert space fragmentation and a parametric increase in zero modes, leading to the emergence of robust collective bound states that challenge ergodicity.
This paper introduces an efficient polylogarithmic decomposition method that embeds the Carleman linearized 1D Burgers' equation into a block-encoded system solvable by the Variational Quantum Linear Solver, achieving a two-qubit gate depth of and marking the first efficient data loading approach for such systems.
The authors developed a surface-electrode ion trap featuring a silicon-etched hole for collimated atomic loading, which enables efficient, isotope-selective sympathetic cooling and the direct generation of ion chains suitable for quantum architectures and precision measurements.
This paper investigates the performance of adiabatic evolution protocols initialized from finite-temperature Gibbs states, demonstrating that key benchmarks such as state diagonality and energy convergence scale polynomially with evolution time and system size, thereby enabling the recovery of thermal expectation values in both integrable and non-integrable systems.
This paper establishes Kirkwood-Dirac nonpositivity as a necessary resource for quantum computational advantage by demonstrating that quantum algorithms maintain a proper probability distribution throughout their execution only when the underlying states are Kirkwood-Dirac positive, thereby enabling efficient classical simulation and identifying new classically-simulable qubit states.
This paper introduces a systematic method using iterative polytope approximations within the probability estimation framework to significantly improve certified entropy rates and reduce device requirements for finite-size device-independent quantum random number generation and randomness amplification protocols.
This paper demonstrates that while depolarizing noise degrades nonstabilizerness, amplitude damping can paradoxically generate or enhance this crucial quantum resource, suggesting that noise can be leveraged rather than merely mitigated in quantum information processing.
This paper investigates the characterization and predictability of closed quantum systems with unknown parameters under energy constraints, revealing that specific measurement datasets can uniquely identify system properties ("self-testing") while demonstrating that future predictions may require superexponential precision or exhibit paradoxical phenomena like "aha! datasets" and "fog banks."
This paper presents a scalable architecture utilizing tunable nonlinear Purcell filters to achieve high-fidelity qubit readout, fast unconditional reset, and enhanced coherence protection for superconducting multi-qubit processors, demonstrating a promising pathway toward fault-tolerant quantum computing.
This paper demonstrates that finite thermodynamic resources, constrained by the third law of thermodynamics, prevent perfect intersubjectivity among quantum observers but establish attainable bounds for agreement and reproducibility that can be approximated through cooling or coarse-graining.
This paper proposes a "field digitization scaling" framework that treats the number of discrete field values as a renormalization group coupling, successfully applying it to relate the 2D classical clock model to the XY model and its quantum gauge theory counterpart to enable continuum limit analysis in quantum simulations.
This paper introduces Subsampling Factorization Machine Annealing (SFMA), an enhanced optimization algorithm that utilizes probabilistic training on subsampled datasets to achieve a superior balance between exploration and exploitation, thereby outperforming standard Factorization Machine Annealing in speed, accuracy, and scalability for large-scale black-box optimization problems.
This paper demonstrates through numerical experiments and analytical derivation that Quantum Generative Adversarial Networks (QGANs) with pure-state generators fail to generalize beyond the average training data representation, a limitation theoretically explained by a fidelity-based lower bound on discriminator quality.