1157 papers
This paper demonstrates the first mid-circuit measurements in silicon spin qubits using a novel in-layer feedforward approach that leverages backaction-driven control to bypass classical routing, thereby addressing critical latency and power challenges for future fault-tolerant quantum computers.
This paper introduces a new universality paradigm for hybrid qubit-qumode quantum computing based on trigonometric continuous-variable gates, demonstrating their effectiveness through a deterministic ancilla-based implementation and a successful simulation of the lattice sine-Gordon model, including ground state preparation, real-time dynamics, and kink profile extraction.
This paper proposes a time-symmetric variational framework where Schrödinger dynamics emerge as the unique solution to a two-time boundary-value problem, replacing the standard dualism of unitary evolution and collapse with a single global action principle that naturally derives the Born rule and effective randomness from information-theoretic constraints.
This paper proposes a scalable and accurate tensor-network-based phase difference estimation algorithm for time series analysis that combines algorithmic error mitigation and iterative circuit optimization, achieving high precision in noiseless simulations and demonstrating significant progress on IBM Heron devices for models up to 52 qubits.
This study establishes high-harmonic generation in semiconductors as a viable platform for producing complex non-classical light, demonstrating that higher-order harmonics exhibit squeezing and entanglement while inter-order heralded measurements successfully engineer quantum non-Gaussian states with sub-Poissonian statistics.
This paper extends the principle of relativity to finite-mass quantum observers by establishing operational requirements for transition amplitude equivalence and the inaccessibility of an observer's own motion state, resulting in a fully relative formulation of quantum mechanics with observer-dependent Hilbert spaces, novel uncertainty relations, and experimentally testable signatures.
This study investigates qubit error bursts in superconducting processors, confirming their consistency with ionizing radiation while identifying two novel, device-specific signatures in Dolan junction processors: a quasiparticle pumping mechanism that accelerates equilibrium recovery under increased -pulsing and an anomalous burst rate surge occurring days after cooldown.
This paper demonstrates that optimizing superconducting qubit readout integration time for maximum throughput, rather than single-shot fidelity, reduces state certification time by approximately 9–11% by accounting for T1 relaxation and hardware overheads through a Chernoff-based stochastic channel model.
This review highlights silicon as a premier platform for quantum networks by summarizing the current state of the art and challenges in developing coherent single-photon emitters and scalable spin-photon interfaces using color centers and erbium dopants within its advanced nanophotonic structures.
This paper introduces a classical quantum-inspired self-attention mechanism integrated into GPT-1, which significantly outperforms standard self-attention in character error rate, word error rate, and cross-entropy loss while incurring only a modest increase in inference time.
This study demonstrates that incorporating biologically motivated, state-dependent synaptic feedback into a Lipkin-Meshkov-Glick quantum brain model significantly reshapes its phase diagram by expanding the paramagnetic phase and displacing critical boundaries, a phenomenon rigorously characterized through ground-state Husimi distributions, Wehrl entropy, and mean-field dynamical analysis.
This paper demonstrates that a lattice gauge theory on a multi-graph with an odd number of links exhibits symmetry-protected topological order and charge deconfinement via fractionalized solitons, driven by a Peierls-like instability that intertwines gauge flux ordering with bond order waves.
This paper proposes a tensor-network-based robust control method that effectively mitigates many-body quantum crosstalk in large-scale systems, achieving order-of-magnitude fidelity improvements for multi-qubit operations and state preparation on up to 50 qubits.
This paper introduces a Variational Quantum Transduction (VQT) framework that leverages variational tools to systematically optimize quantum transduction protocols, achieving superior performance over existing non-adaptive schemes while demonstrating that current Gaussian adaptive strategies are already near-optimal.
This paper presents a resource-efficient method for emulating Majorana Zero Mode braiding on a superconducting trijunction quantum processor by introducing direct braiding operators that significantly reduce gate overhead compared to traditional adiabatic evolution approaches.
The authors propose and demonstrate the axial anomaly as a robust benchmark for quantum computing by successfully simulating anomalous axial-charge production in lattice gauge theories on the "Reimei" trapped-ion quantum computer, reproducing the exact anomaly coefficient within statistical uncertainties without error mitigation.
This paper presents an overflow-safe, hardware-efficient parallel minimum-weight perfect matching decoder that utilizes a truncated polynomial ring framework to reduce intermediate bit-length requirements by over 99.9% while maintaining polylogarithmic runtime, thereby enabling the first practical experimental demonstration of this approach for fault-tolerant quantum computation.
This paper proposes a systematic approach to enhance Variational Quantum Eigensolvers for SU(2) lattice gauge theories by utilizing a spin-network basis and a specialized state preparation ansatz to efficiently simulate gauge-invariant excitations while mitigating barren plateaus, demonstrated through noise-resilient simulations on a minimal 3+1 dimensional toy model.
This paper demonstrates the implementation of a variational algorithm for preparing Gibbs states of a transverse-field Ising model on IonQ's trapped-ion devices, revealing that hardware-induced thermal fluctuations cause "digital heating" that degrades state fidelity as system size and inverse temperature increase.
This paper introduces the Adaptive Interpolating Quantum Transform (AIQT) as a data-adaptive alternative to Fourier-based sparse amplitude encoding, which significantly reduces reconstruction error on financial and image datasets while maintaining quadratic gate scaling and eliminating the need for quantum sampling during training.