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Holger Haas

Publications and source records attributed to Holger Haas.

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Observable Estimation in the Absence of Classical Verification

The predictive success of quantum mechanics underpins many areas of modern science, even as the exact simulation of large, interacting quantum systems remains beyond the reach of classical computation. This success has been enabled by the remarkable advancement of scalable numerical approximation methods, which often demonstrate practical accuracy despite the absence of formal guarantees. As quantum simulation pushes into regimes where these approximations struggle, a fundamental challenge arises: How can quantum outcomes be trusted when reliable classical benchmarks are unavailable? Here, we establish a framework for the independent validation of quantum estimates in this setting and present evidence that they provide the most credible result among several considered methods, in the absence of an immediately accessible ground-truth solution. We apply our framework to the semi-scrambling dynamics of a physical model that strains several leading classical simulation methods yet remains experimentally accessible, in part through our introduction of the \textit{operator Loschmidt echo}. We systematically design a series of experiments using quantum heuristics that, taken together, test the underlying assumptions and provide strong confidence in the observable estimates obtained from the quantum computer. We then show how this framework can be extended to place accuracy bounds on quantum estimates via careful characterization and manipulation of the device noise, transforming the problem of validating the observable estimation to validating the noise model. These results establish a route towards trusted quantum computation for scientific discovery, independent of classical verification.

quant-ph

Dimensionality reduction for closed-loop quantum gate calibration

Numerical gate design typically makes use of high-dimensional parameterizations enabling sophisticated, highly expressive control pulses. Developing efficient experimental calibration methods for such gates is a long-standing challenge in quantum control, as on-device calibration requires the optimization of noisy experimental data over high-dimensional parameter spaces. To improve the efficiency of calibrations, we present a systematic method based on SVD stacking for reducing the dimensionality of the parameter space traversed in gate calibration, starting from an arbitrary high-dimensional pulse representation. We use this approach to design and calibrate an $X_{\pi/2}$ gate robust against amplitude and detuning errors, as well as an $X_{\pi/2}$ gate robust against coherent errors due to a spectator qubit.

quant-ph

Diagonalization of large many-body Hamiltonians on a quantum processor

The estimation of low energies of many-body systems is a cornerstone of computational quantum sciences. Variational quantum algorithms can be used to prepare ground states on pre-fault-tolerant quantum processors, but their lack of convergence guarantees and impractical number of cost function estimations prevent systematic scaling of experiments to large systems. Alternatives to variational approaches are needed for large-scale experiments on pre-fault-tolerant devices. Here, we use a superconducting quantum processor to compute eigenenergies of quantum many-body systems on two-dimensional lattices of up to 56 sites, using the Krylov quantum diagonalization algorithm, an analog of the well-known classical diagonalization technique. We construct subspaces of the many-body Hilbert space using Trotterized unitary evolutions executed on the quantum processor, and classically diagonalize many-body interacting Hamiltonians within those subspaces. These experiments show that quantum diagonalization algorithms are poised to complement their classical counterpart at the foundation of computational methods for quantum systems.

quant-ph

Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer

A universal quantum computer can simulate diverse quantum systems, with electronic structure for chemistry offering challenging problems for practical use cases around the hundred-qubit mark. While current quantum processors have reached this size, deep circuits and large number of measurements lead to prohibitive runtimes for quantum computers in isolation. Here, we demonstrate the use of classical distributed computing to offload all but an intrinsically quantum component of a workflow for electronic structure simulations. Using a Heron superconducting processor and the supercomputer Fugaku, we simulate the ground-state dissociation of N$_2$ and the [2Fe-2S] and [4Fe-4S] clusters, with circuits up to 77 qubits and 10,570 gates. The proposed algorithm processes quantum samples to produce upper bounds for the ground-state energy and sparse approximations to the ground-state wavefunctions. Our results suggest that, for current error rates, a quantum-centric supercomputing architecture can tackle challenging chemistry problems beyond sizes amenable to exact diagonalization.

quant-ph

Nanometer-Scale Nuclear Magnetic Resonance Diffraction with Sub-Ångstrom Precision

Achieving atomic resolution is the ultimate limit of magnetic resonance imaging (MRI), and attaining this capability offers enormous technological and scientific opportunities, from drug development to understanding the dynamics in interacting quantum systems. In this work, we present a new approach to nanoMRI utilizing nuclear magnetic resonance diffraction (NMRd) -- a method that extends NMR imaging to probe the structure of periodic spin systems. The realization of NMRd on the atomic scale would create a powerful new methodology for materials characterization utilizing the spectroscopic capabilities of NMR. We describe two experiments that realize NMRd measurement of $^{31}$P spins in an indium-phosphide (InP) nanowire with sub-Ångstrom precision. In the first experiment, we encode a nanometer-scale spatial modulation of the $z$-axis magnetization by periodically inverting the $^{31}$P spins, and detect the period and position of the modulation with a precision of $<0.8$ Å. In the second experiment, we demonstrate an interferometric technique, utilizing NMRd, for detecting an Ångstrom-scale displacement of the InP sample with a precision of 0.07 Å. The diffraction-based techniques developed in this work represent new measurement modalities in NMR for probing the structure and dynamics of spins on sub-Ångstrom length scales, and demonstrate the feasibility of crystallographic MRI measurements.

cond-mat.mes-hall

Numerical Engineering of Robust Adiabatic Operations

Adiabatic operations are powerful tools for robust quantum control in numerous fields of physics, chemistry and quantum information science. The inherent robustness due to adiabaticity can, however, be impaired in applications requiring short evolution times. We present a single versatile gradient-based optimization protocol that combines adiabatic control with effective Hamiltonian engineering in order to design adiabatic operations tailored to the specific imperfections and resources of an experimental setup. The practicality of the protocol is demonstrated by engineering a fast, 2.3 Rabi cycle-long adiabatic inversion pulse for magnetic resonance with built-in robustness to Rabi field inhomogeneities and resonance offsets. The performance and robustness of the pulse is validated in a nanoscale force-detected magnetic resonance experiment on a solid-state sample, indicating an ensemble-averaged inversion accuracy of $\sim 99.997\%$. We further showcase the utility of our protocol by providing examples of adiabatic pulses robust to spin-spin interactions, parameter-selective operations and operations connecting arbitrary states, each motivated by experiments.

quant-ph

Engineering Effective Hamiltonians

In the field of quantum control, effective Hamiltonian engineering is a powerful tool that utilises perturbation theory to mitigate or enhance the effect that a variation in the Hamiltonian has on the evolution of the system. Here, we provide a general framework for computing arbitrary time-dependent perturbation theory terms, as well as their gradients with respect to control variations, enabling the use of gradient methods for optimizing these terms. In particular, we show that effective Hamiltonian engineering is an instance of a bilinear control problem - the same general problem class as that of standard unitary design - and hence the same optimization algorithms apply. We demonstrate this method in various examples, including decoupling, recoupling, and robustness to control errors and stochastic errors. We also present a control engineering example that was used in experiment, demonstrating the practical feasibility of this approach.

quant-ph

High-Resolution Nanoscale Solid-State Nuclear Magnetic Resonance Spectroscopy

We present a new method for high-resolution nanoscale magnetic resonance imaging (nano-MRI) that combines the high spin sensitivity of nanowire-based magnetic resonance detection with high spectral resolution nuclear magnetic resonance (NMR) spectroscopy. By applying NMR pulses designed using optimal control theory, we demonstrate a factor of $500$ reduction of the proton spin resonance linewidth in a $(50\text{-nm})^{\text{3}}$ volume of polystyrene and image proton spins in one dimension with a spatial resolution below $2~\text{nm}$.

cond-mat.mes-hall

Tractable Simulation of Error Correction with Honest Approximations to Realistic Fault Models

In previous work, we proposed a method for leveraging efficient classical simulation algorithms to aid in the analysis of large-scale fault tolerant circuits implemented on hypothetical quantum information processors. Here, we extend those results by numerically studying the efficacy of this proposal as a tool for understanding the performance of an error-correction gadget implemented with fault models derived from physical simulations. Our approach is to approximate the arbitrary error maps that arise from realistic physical models with errors that are amenable to a particular classical simulation algorithm in an "honest" way; that is, such that we do not underestimate the faults introduced by our physical models. In all cases, our approximations provide an "honest representation" of the performance of the circuit composed of the original errors. This numerical evidence supports the use of our method as a way to understand the feasibility of an implementation of quantum information processing given a characterization of the underlying physical processes in experimentally accessible examples.

quant-ph