Initialization of mechanical modes in the quantum ground state is crucial for their use in quantum information and quantum sensing protocols. In quantum processors, impurity of the modes' initial state affects the infidelity of subsequent quantum algorithms. In quantum sensors, excitations out of the ground state contribute to the noise of the detector, and their prevalence puts a bound on rare events that deposit energy into the mechanical modes. In this work, we measure the excited-state populations of GHz-frequency modes in a high-overtone bulk acoustic wave resonator (HBAR). We find that the population of the first excited state can be as low as $P_p$=(1.2$\pm$5.5)$\times10^{-5}$, corresponding to an effective temperature of 25.2 mK, which are upper bounds limited by imperfections in the measurement process. These results compare favorably to the lowest populations measured in superconducting circuits. Finally, we use the measured populations to constrain the amplitude of high-frequency gravitational waves, the kinetic mixing strength of ultra-light dark matter, and non-linear modifications of the Schr\"{o}dinger equation describing wavefunction collapse mechanisms. Our work establishes HBARs as a versatile resource for quantum state initialization and studies of fundamental physics. less

Scaling beyond individual quantum devices via distributed quantum computing relies critically on high-fidelity quantum state transfers between devices, yet the quantum interconnects needed for this are currently unavailable or expected to be significantly noisy. These limitations can be bypassed by simulating ideal state transfer using quasiprobability decompositions (QPDs). Wire cutting, for instance, allows this even without quantum interconnects. Nevertheless, QPD methods face drawbacks, requiring sampling from multiple circuit variants and incurring substantial sampling overhead. While prior theoretical work showed that incorporating noisy interconnects within QPD protocols could reduce sampling overhead relative to interconnect quality, a practical implementation for realistic conditions was lacking. Addressing this gap, this work presents a generalized and practical QPD for state transfer simulation using noisy interconnects to reduce sampling overhead. The QPD incorporates a single tunable parameter for straightforward calibration to any utilized interconnect. To lower practical costs, the work also explores reducing the number of distinct circuit variants required by the QPD. Experimental validation on contemporary quantum devices confirms the proposed QPD's practical feasibility and expected sampling overhead reduction under realistic noise. Notably, the results show higher effective state transfer fidelity than direct transfer over the underlying noisy interconnect. less

We propose a novel approach to generative adversarial networks (GANs) in which the standard i.i.d. Gaussian latent prior is replaced or hybridized with a quantum-correlated prior derived from measurements of a 16-qubit entangling circuit. Each latent sample is generated by grouping repeated shots per qubit into a binary fraction, applying the inverse Gaussian CDF to obtain a 16-dimensional Gaussian vector whose joint copula reflects genuine quantum entanglement, and then projecting into the high-dimensional space via a fixed random matrix. By pre-sampling tens of millions of bitstrings, either from a noiseless simulator or from IBM hardware, we build large pools of independent but internally quantum-correlated latents. We integrate this prior into three representative architectures (WGAN, SNGAN, BigGAN) on CIFAR-10, making no changes to the neural network structure or training hyperparameters. The hybrid latent representations incorporating hardware-derived noise consistently lower the FID relative to both the classical baseline and the simulator variant, especially when the quantum component constitutes a substantial fraction of the prior. In addition, we execute on the QPU in parallel to not only save computing time but also further decrease the FID up to 17% in BigGAN. These results indicate that intrinsic quantum randomness and device-specific imperfections can provide a structured inductive bias that enhances GAN performance. Our work demonstrates a practical pipeline for leveraging noisy quantum hardware to enrich deep-generative modeling, opening a new interface between quantum information and machine learning. All code and data are available at https://github.com/Neon8988/GAN_QN.git. less

To move towards the utility era of quantum computing, many corporations have posed distributed quantum computing (DQC) as a framework for scaling the current generation of devices for practical applications. One of these applications is quantum chemistry, also known as electronic structure theory, which has been poised as a "killer application" of quantum computing, To this end, we analyze five electronic structure methods, found in common packages such as Tequila and ffsim, which can be easily interfaced with the Qiskit Circuit Cutting addon. Herein, we provide insights into cutting these algorithms using local operations (LO) to determine their aptitude for distribution. The key findings of our work are that many of these algorithms cannot be efficiently parallelized using LO, and new methods must be developed to apply electronic structure theory within a DQC framework. less

We consider a pair of particles that interact in a one-dimensional setting via a delta-function potential. One of the particles is confined to a one-dimensional box, and the other particle is free. The free particle is incident from the left with specified energy, and it may cause changes in state of the confined particle before flying away to the left or to the right. We present a non-perturbative formulation and computational scheme that determines the probability of any such outcome, as a function of the initial state of the confined particle and the energy of the incident particle. less

We introduce a quantum channel to model the dissipative dynamics resulting from the coupling between spin and motional degrees of freedom in chains of neutral atoms with Rydberg interactions. The quantum channel acts on the reduced spin state obtained under the frozen gas approximation, modulating its elements with time-dependent coefficients. These coefficients can be computed exactly in the perturbative regime, enabling efficient modeling of spin-motion dephasing in systems too large for exact methods. We benchmark the accuracy of our approach against exact diagonalization for small systems, identifying its regime of validity and the onset of perturbative breakdown. We then apply the quantum channel to compute fidelity loss during transport of single-spin excitations across extended Rydberg chains in regimes intractable via exact diagonalization. By revealing the quantum-classical crossover, these results establish a bound on the maximum chain length for efficient entanglement distribution. The quantum channel significantly reduces the complexity of simulating spin dynamics coupled to motional degrees of freedom, providing a practical tool for estimating the impact of spin-motion coupling in near-term experiments with Rydberg atom arrays. less

Quantum field simulators provide unique opportunities for investigating the dynamics of quantum fields through tabletop experiments. A primary drawback of standard encoding schemes is their rigidity: altering the theory, its coupling geometry, metric structure, or simulation time typically requires redesigning the experimental setup, which imposes strong constraints on the types of dynamics and theories that can be simulated. Here, we introduce the Optical Time Algorithm (OTA) as a unifying framework, enabling the efficient simulation of large classes of free quantum field dynamics using a single optical circuit design that separates the time from the Hamiltonian's structure. By modifying the parameters of the optical elements, our method allows us to engineer timescales, coupling graphs, spacetime metrics, and boundary conditions, thereby facilitating the implementation of relativistic and non-relativistic, real- and complex-valued, short- and long-range quantum field theories on both flat and curved spacetimes. We exploit the OTA's configurability to investigate the spreading of quantum correlations in space and time for theories with continuously varying coupling ranges. Relevant features predicted by quantum field theory can be observed on systems of $10-20$ modes, which paves the ground for experimental implementations. less

We introduce a method to simulate open quantum many-body dynamics by combining time-dependent variational Monte Carlo (tVMC) with quantum trajectory techniques. Our approach unravels the Lindblad master equation into an ensemble of stochastic Schr\"odinger equations for a variational ansatz, avoiding the exponential cost of density matrix evolution. The method is compatible with generic ansatz\"e, including expressive neural-network wavefunctions. We derive the nonlinear stochastic equations of motion for the variational parameters and employ suitable Stratonovich numerical solvers. To validate our approach, we simulate quenches in the locally dissipative long-range Ising model in a transverse field, accurately capturing non-equilibrium magnetization and spin squeezing dynamics relevant to trapped-ion and Rydberg atom experiments. The framework is computationally efficient, scalable on high-performance computing platforms, and can be readily integrated into existing tVMC implementations. This work enables large-scale simulations of complex, dissipative quantum systems in higher dimensions, with broad implications for quantum technology and fundamental science. less