946 papers
This paper proposes and validates a method for studying scaling dimensions of three-dimensional conformal field theories using near-term quantum simulators, demonstrating high accuracy with as few as 20 qubits on the Ising model to solve a problem that is currently difficult for classical computers.
This paper establishes an information-theoretic no-go theorem proving that classical ontological models constrained to reuse a single ontic state space across multiple interventions inevitably incur an irreducible contextual information cost, thereby identifying contextuality as a fundamental limitation of such classical representations that quantum theory circumvents by relaxing the single-variable assumption.
This paper demonstrates that numerically optimized time-dependent driving can overcome the fidelity limitations of qubit reset caused by polaron formation beyond the Born-Markov approximation by actively steering system-environment correlations, even in realistic multilevel transmon systems with filtered environments.
This paper demonstrates that by actively controlling the time-dependent coupling between a transmon qubit and its dissipative environment to reverse detrimental polaron formation, it is possible to achieve rapid, high-fidelity qubit reset in non-Markovian systems.
This paper introduces hysteretic squashed entanglement (), a robust conditional entanglement monotone for mixed many-body states that effectively filters out classical contributions to detect genuine quantum correlations and quantify topological order in systems like the transverse-field Ising model.
This paper argues that quantum computational advantage has indeed been achieved, despite the lack of consensus, and outlines future directions for theory and experiments in the field.
This paper establishes a universal structural principle showing that smoothing optimizers are clipped probability vectors, which enables the derivation of optimal, state-independent bounds relating smoothed quantum divergences of arbitrary orders, including the hypothesis testing divergence.
This paper numerically demonstrates that while trivially scaling a fluxonium-transmon coupler architecture leads to significant crosstalk limiting gate fidelity, reducing coupling strengths and dynamically detuning idle transmons can suppress spectator errors to below $10^{-4}$, thereby enabling scalable high-fidelity two-qubit operations.
This paper proposes a Velocity Verlet-based optimization algorithm for Variational Quantum Eigensolvers that leverages an inertial velocity term to efficiently navigate complex energy landscapes, demonstrating superior performance over standard optimizers like L-BFGS-B in achieving chemical accuracy and lower final energies for H and LiH molecules.
This paper introduces a self-consistent mean-field quantum optimization algorithm that decomposes classical Ising Hamiltonians into independent subproblems influenced by a variational common environment, enabling the solution of large-scale problems on current quantum hardware through extensive numerical simulations and experimental applications.
This paper experimentally demonstrates the efficient frequency conversion of atomic biphotons to the telecom band using a diamond-type atomic ensemble, successfully preserving their temporal waveforms, nonclassical antibunching, and strong quantum correlations to enable practical interfaces for distributed quantum communication.
This paper provides a comprehensive introductory overview of quantum mechanics, tracing its foundational postulates and exploring how various interpretations—from Copenhagen and de Broglie-Bohm to many-worlds and objective collapse models—address central issues such as measurement, nonlocality, contextuality, and the emergence of classical reality.
This paper introduces a resource-theoretic framework featuring a "resource impact functional" to establish rigorous, state-independent bounds and operational diagnostics that quantify the maximum influence of initial site-basis coherence on transport performance and spectroscopic readouts in excitonic energy transfer systems.
This paper demonstrates that quantum emitters coupled to a two-dimensional dissipative photonic graphene with exceptional rings can achieve decoherence-free interactions and stabilization in dissipation-robust quasilocalized states through dissipation engineering, offering a promising pathway for protecting quantum coherence in high-dimensional photonic environments.
This paper investigates how the temporal resolution of homodyne detection and digital data processing constraints in realistic experimental setups impact the faithful acquisition and reconstruction of non-Gaussian quantum states generated via continuous-wave light.
This paper presents a fiber-based source of narrowband heralded single photons in the telecom C-band, achieved by generating photon pairs via spontaneous four-wave mixing in a germanium-doped photonic crystal fiber and filtering them with a UV-written fiber Bragg grating to enable efficient coupling to quantum memories.
This review systematically analyzes noise sources in photonic quantum machine learning, examining their impact on algorithm performance and exploring characterization and mitigation strategies to guide the development of robust, scalable systems.
This paper proposes an approximate mapping of molecular Hamiltonians to a system-bath model, where a two-orbital active space is encoded by two qubits and the remaining electronic excitations are modeled as a bosonic bath, enabling high-accuracy calculations of vertical excitation energies on near-term quantum computers.
This paper introduces ZX-flow, a new "ZX-native" criterion based on Pauli semiwebs that enables the efficient extraction of deterministic computations from ZX-diagrams while being robustly preserved under Clifford rewrites, unlike previous flow criteria that require restrictive graph-state forms.
This paper introduces a variational quantum dimension reduction framework that utilizes parameterized quantum circuits and the Quantum Fidelity Divergence Rate metric to efficiently identify and remove redundant memory degrees of freedom in recurrent quantum models, thereby enabling the learning of minimal, scalable architectures without requiring explicit state reconstructions.