arXiv subjects

Michael J. Biercuk

Publications and source records attributed to Michael J. Biercuk.

At least 19 recordsLinked to original sources

arXiv2026

GNSS-free quantum gravity-aided navigation and fine-scale marine surveying with a strapdown quantum gravimeter

Global navigation satellite systems (GNSS) are often disrupted or unavailable at sea, and unaided inertial navigation systems (INS) drift without correction. Quantum-sensing-based gravity map matching offers a passive, infrastructure-free aid, but field demonstrations of GNSS-free quantum gravimetric navigation have not been reported. Here we perform gravity map matching and fine-resolution gravity survey with a mobile quantum gravimeter aboard a 29 m surface vessel. We hybridize an atomic sensor with a classical accelerometer for bias stabilization and independently mechanize a navigation-grade IMU, all installed in an uncontrolled cabin with no environmental stabilization or calibration. Operated in both gimbaled and strapdown configurations over identical traversals, the hybrid sensor corrected the inertial solution over an 83 km maritime trajectory by referencing locally measured gravity to a satellite-derived anomaly map. Gravity-aiding constrains INS drift and delivers bounded positioning at nautical-mile-level accuracy, with GNSS excluded throughout the measurement chain. In a separate GNSS-referenced mode, the same system surveyed coastal routes up to Sea State 4, achieving mGal-level agreement with gravimetric maps and sub-mGal repeatability and stability, with gimbaled and strapdown operation performing comparably. Resolved anomalies reach an along-track scale of ~300 m, 50X finer than the satellite map's half-power wavelength. A 56 h stationary test shows atom referencing lowers long-term drift ~70X versus the classical channel alone. These results provide the first same-instrument comparison of gimbaled and strapdown mobile quantum gravimetry and the first fully GNSS-independent gravity-map-matching navigation demonstration using a quantum gravimeter, pointing toward compact, autonomous-platform-ready quantum sensing for GNSS-denied maritime navigation and survey.

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arXiv2026

Experimental demonstration of the Quantum Fourier Transform on up to 100 qubits using a convolutional compilation strategy

We present and experimentally validate the `Convolutional QFT': a constructive compilation strategy for the Quantum Fourier Transform (QFT) subroutine on a linear nearest neighbor (LNN) qubit topology. We first introduce a novel strategy that compiles the $n$-qubit QFT onto an LNN topology using only $n^2 - n$ $CX$ gates, matching requirements of a direct compilation on an all-to-all architecture. We then derive the convolutional variant used in our experiments, which requires an additional two $CX$ gates in total, and is realized via a compact, translation-invariant kernel circuit gadget that traverses a quantum register. We demonstrate the power of the convolutional compilation strategy on the IBM Quantum Platform by executing QFT benchmarking circuits. We measure a process fidelity of 11.4% at 50 qubits, and 1.8% at 80 qubits. The correct output state remains clearly distinguishable above background noise up to 100 qubits. These results constitute the largest experimental QFT demonstrated on any quantum computing hardware to date.

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arXiv2026

Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor

We report experimental digital quantum simulation of the one-dimensional Fermi-Hubbard model on a superconducting quantum processor at a scale beyond the reach of exact statevector simulation and challenging for state-of-the-art tensor-network methods. We encode this problem using up to 120 qubits through an efficient mapping that reduces circuit complexity, and we improve accuracy through error suppression to simulate dynamical evolution using up to 90 Trotter steps. From a vacancy defect introduced in the middle of an $L=31$-site (62-qubit) Néel initial state, we directly observe spin-charge separation to $t=9$ in natural units and quantitatively extract velocities $v_c$, $v_s$, which match classical simulations across model parameters, and analytics in appropriate regimes. We then extend experiments to $L=60$ fermions (120 qubits) and evolution times to $t=6$ using 30 Trotter steps. Quantum-processor outputs agree quantitatively with approximate classical simulations performed using a time-dependent variational principle (TDVP) solver and we observe that increasing the TDVP bond dimension through $χ= 4096$ expands the range of evolution times within which agreement has RMSE $\sim 1\%$ before the approaches diverge. Owing to the large scale of the simulation and the use of overhead-free error-suppression techniques, for simulated evolution times at the limit of quantum/classical agreement ($t\gtrsim 5$ in natural hopping units), the wall-clock runtime of the quantum processor is nearly three orders of magnitude faster than TDVP simulations with $χ= 4096$ and implemented using the ITensor and TeNPy packages. These results establish contemporary digital quantum processors as a versatile, quantitatively accurate, and competitive platform for the study of fermionic many-body dynamics in regimes where leading classical methods can become prohibitively expensive.

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arXiv2026

Programmable coherent site-selective spin control in rotating Penning-trap ion crystals

Large ion crystals in Penning traps provide a platform for quantum simulation and sensing with hundreds of spins, but their continuous rigid-body rotation has so far limited flexible local qubit control. Here we demonstrate programmable coherent site-selective spin control across large rotating ${}^{9}\mathrm{Be}^{+}$ crystals in a Penning trap. A tightly focused off-resonant laser beam drives local $R_z$ phase rotations via differential AC Stark shifts. Beam steering synchronised with crystal rotation enables addressing of arbitrary ions throughout the crystal. Ramsey-based characterisation shows $R_z(π)$ gate fidelity of 0.950(4) and nearest-neighbour crosstalk error of 0.021(6). We demonstrate preparing spatially structured spin patterns, generating a biskyrmion spin texture in a single-layer crystal, and then extend the method to bilayer crystals where we perform layer-selective addressing operations. We further demonstrate dual-quadrature Ramsey sensing by imprinting a relative $π/2$ phase shift between spatial sub-ensembles, enabling simultaneous measurement of orthogonal spin components within a single experimental realisation. These results establish programmable local control in large rotating ion crystals, opening new routes for engineering spatially structured quantum states in multidimensional trapped-ion systems.

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arXiv2026

Experimental realisation of topological spin textures in a Penning trap

Quantum simulation with controllable many-body platforms offers a powerful route to exploring complex phases and dynamics that are difficult to access in natural materials. Among these, topological spin textures such as skyrmions are central to modern condensed-matter physics and play a key role in chiral quantum many-body systems. Their controlled realisation in large, programmable quantum platforms, however, remains an outstanding challenge. Here, we report deterministic generation and site-resolved reconstruction of topological spin textures in a two-dimensional crystal of more than 150 trapped ions. Using globally applied spin-dependent forces, we generate skyrmion configurations and reconstruct the full vector spin field with single-ion resolution, obtaining a winding number of 0.99$\pm$0.02 and a mean local fidelity of 0.87$\pm$0.04. In addition, we implement single-ion-resolved control to deterministically prepare domain-wall states, extending our approach to a broader class of non-uniform spin textures. These results establish trapped-ion crystals as a platform for engineering complex spin textures and open the door to exploring topology-dependent nonequilibrium dynamics in long-range interacting quantum systems.

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arXiv2026

Heterogeneous architectures enable a 138x reduction in physical qubit requirements for fault-tolerant quantum computing under detailed accounting

Quantum computer hardware is predicted to scale over hundreds of thousands of qubits coming online in the next decade. Despite significant theoretical and experimental QEC progress, quantum computer architecture has suffered a significant gap, with bottom-up physical-device-driven challenges largely disconnected from top-down QEC-code-driven considerations. In this work, we unify these two views, presenting a complete heterogeneous quantum computing architecture incorporating task-specific hardware selection and QEC encoding, and agnostic to code selection or physical qubit parameters. Our approach further enables special-purpose processing modules, and includes a full microarchitecture for fault-tolerant implementation of interfaces between quantum processing units and quantum memories. Using this architecture and a new fully featured compiler functioning across subsystems at the scale of $1,000$ logical qubits, we schedule and orchestrate a variety of algorithms down to hardware-specific instructions; a detailed accounting of all operations reveals up to 551x reduction in algorithmic logical error and up to 138x reduction in physical-qubit overhead compared to a monolithic baseline architecture. We then consider the factorization of 2048-bit RSA-integers; using an experimentally demonstrated grid-coupling topology, factoring RSA-2048 requires 381k physical qubits and 9.2 days, which can be reduced to 4.9 days via addition of an algorithm-specific accelerator for the Adder subroutine (requiring 439k qubits). Finally, assuming hypothetical long-range coupling, implementing quantum memory using qLDPC codes reduces the resources required for factoring to just 190k qubits and under 10 days. These results and the tooling we have built indicate that heterogeneous quantum-computer architectures can deliver significant, verifiable benefits on realistic hardware.

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arXiv2026

Integrated error-suppressed pipeline for quantum optimization of nontrivial binary combinatorial optimization problems on gate-model hardware at the 156-qubit scale

We introduce a novel hybrid quantum-classical variational optimization method for unconstrained binary combinatorial optimization problems on gate-model quantum computers, integrating a custom variational ansatz, staged feedback-based dual variational parameter update strategies, efficient parametric compilation, automated error suppression during hardware execution, and scalable O($n$) classical post-processing to correct for bitflip errors. Without this integrated approach, we show that standard circuit execution at scale produces output indistinguishable from random sampling, establishing the necessity of each pipeline component. We benchmark the method on IBM superconducting quantum computers for classically nontrivial optimization problems, where the optimization is conducted on hardware with no use of classical simulation or prior knowledge of the solution. For Max-Cut on random regular graphs with topologies not matched to device connectivity, the method achieves approximation ratios of 100% for unweighted 3-regular graphs up to 156 nodes, weighted regular graphs up to 80 nodes, and weighted 7-regular graphs up 50 nodes. Applied to higher-order binary optimization, the method finds the ground state energy of 127- and 156-qubit spin-glass models matched to device topology with linear, quadratic, and cubic interaction terms, achieving approximation ratios of at least 99.5% across all instances tested. The method consistently outperforms a classical local solver across all problems. Where published results on identical problem instances are available, our method demonstrates competitive or superior performance. These results demonstrate that an appropriately engineered approach enables gate-model quantum computers to produce high-quality solutions for nontrivial binary optimization problems at the 156 qubit scale, where naive implementations are insufficient for good performance.

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arXiv2025

No need to calibrate: characterization and compilation for high-fidelity circuit execution using imperfect gates

We propose and validate on real quantum computing hardware a new method for extended two-qubit gate set design, replacing iterative, fine calibration with fast characterization of a small number of gate parameters which are then tracked and corrected in circuit compilation. Coherent contributions to the pulse unitary that would traditionally be considered sources of error are treated as part of the gate definition, and compensated in software via single-qubit rotations. This approach enables rapid device-wide generation of high-fidelity two-qubit entangling gates, which are combined with standard calibrated gates to produce an expanded gate set. We show how these gates are directly usable as part of a quantum compiler, synthesizing generic two-qubit circuit blocks into minimal-duration sequences of the characterized gates interleaved with compensating single-qubit rotations. Benchmarking against circuits compiled using the default $CX$ gate alone on 127-qubit IBM hardware shows up to 7X improvement in success probability for Quantum Fourier Transform circuits up to 26 qubits, and up to 9X lower mean-square error in Trotter simulations of the one-dimensional transverse-field Ising model. Our hardware-agnostic characterization and compilation methodology makes it practical to scale up expressive gate sets on quantum computing architectures while minimizing the need for onerous fine-tuning of low-level control waveforms.

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arXiv2025

Achieving computational gains with quantum error-correction primitives: Generation of long-range entanglement enhanced by error detection

The resource overhead required to achieve net computational benefits from quantum error correction (QEC) limits its utility while current systems remain constrained in size, despite exceptional progress in experimental demonstrations. In this paper, we demonstrate that the strategic application of QEC primitives without logical encoding can yield significant advantages on superconducting processors--relative to any alternative error-reduction strategy--while only requiring a modest overhead. We first present a novel protocol for implementing long-range CNOT gates that relies on a unitarily prepared Greenberger-Horne-Zeilinger (GHZ) state as well as a unitary disentangling step; the protocol natively introduces an error-detection process using the disentangled qubits as flags. We demonstrate that it achieves state-of-the-art gate fidelities of over 85% across up to 40 lattice sites, significantly and consistently outperforming the best alternative measurement-based protocol without introducing any additional ancilla qubits. We then apply sparse stabilizer measurements to generate large GHZ states by detecting bit-flip and amplitude-damping errors. Employing this technique in combination with deterministic error suppression, we generate a 75-qubit GHZ state exhibiting genuine multipartite entanglement, the largest reported to date. The generation requires no more than 9 ancilla qubits and the fraction of samples discarded due to errors grows no higher than 78%, far lower than previous discard fractions required for tests using comparable numbers of fully encoded qubits. This work in total represents compelling evidence that adopting QEC primitives on current-generation devices can deliver substantial net benefits.

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arXiv2025

Quantum-assured magnetic navigation achieves positioning accuracy better than a strategic-grade INS in airborne and ground-based field trials

Modern navigation systems rely critically on GNSS, which in many cases is unavailable or unreliable (e.g. due to jamming or spoofing). For this reason there is great interest in augmenting backup navigation systems such as inertial navigation systems (INS) with additional modalities that reduce positioning error in the absence of reliable GNSS. Magnetic-anomaly navigation is one such approach, providing passive, non-jammable navigation through periodic position fixes obtained by comparing local measurements of Earth's crustal field against known anomaly maps. Despite its potential, existing MagNav efforts have been limited by magnetometer performance and platform noise; solutions addressing these problems have proven either too brittle or impractical for realistic deployment. Here we demonstrate a quantum-assured MagNav solution based on proprietary quantum magnetometers with by a novel denoising and map-matching algorithms. The system fits on fixed-wing drones or in the avionics bay of a commercial airliner. We present trials at altitudes up to 19000 feet, testing onboard and outboard quantum magnetometers comparing against a strategic-grade INS. Our MagNav solution achieves superior performance, delivering up to 46x better positioning error than the velocity-aided INS; the best final positioning accuracy we achieve is 22m or 0.006% of the flight distance. Airborne trials consistently achieve at least 11x advantage over the INS across varying conditions, altitudes, and flight patterns. The system learns model parameters online without special vehicle maneuvers providing robustness to various configuration changes (e.g. changing payload or latitude). Our trials also include the first successful MagNav performed in a ground vehicle using publicly-available anomaly maps, delivering bounded positioning error 7x lower than the INS, with both systems in strapdown configuration.

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arXiv2024

Learning to rank quantum circuits for hardware-optimized performance enhancement

We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware. We apply our method to the problem of layout selection, in which abstracted qubits are assigned to physical qubits on a given device. Circuit measurements performed on IBM hardware indicate that the maximum and median fidelities of logically equivalent layouts can differ by an order of magnitude. We introduce a circuit score used for ranking that is parameterized in terms of a physics-based, phenomenological error model whose parameters are fit by training a ranking-loss function over a measured dataset. The dataset consists of quantum circuits exhibiting a diversity of structures and executed on IBM hardware, allowing the model to incorporate the contextual nature of real device noise and errors without the need to perform an exponentially costly tomographic protocol. We perform model training and execution on the 16-qubit ibmq_guadalupe device and compare our method to two common approaches: random layout selection and a publicly available baseline called Mapomatic. Our model consistently outperforms both approaches, predicting layouts that exhibit lower noise and higher performance. In particular, we find that our best model leads to a $1.8\times$ reduction in selection error when compared to the baseline approach and a $3.2\times$ reduction when compared to random selection. Beyond delivering a new form of predictive quantum characterization, verification, and validation, our results reveal the specific way in which context-dependent and coherent gate errors appear to dominate the divergence from performance estimates extrapolated from simple proxy measures.

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arXiv2024

Efficient site-resolved imaging and spin-state detection in dynamic two-dimensional ion crystals

Resolving the locations and discriminating the spin states of individual trapped ions with high fidelity is critical for a large class of applications in quantum computing, simulation, and sensing. We report on a method for high-fidelity state discrimination in large two-dimensional (2D) crystals with over 100 trapped ions in a single trapping region, combining a hardware detector and an artificial neural network. A high-data-rate, spatially resolving, single-photon sensitive timestamping detector performs efficient single-shot detection of 2D crystals in a Penning trap, exhibiting rotation at about $25\,\mathrm{kHz}$. We then train an artificial neural network to process the fluorescence photon data in the rest frame of the rotating crystal in order to identify ion locations with a success rate of $~90\%$, accounting for substantial illumination inhomogeneity across the crystal. Finally, employing a time-binned state detection method, we arrive at an average spin-state detection fidelity of $94(2)\%$. This technique can be used to analyze spatial and temporal correlations in arrays of hundreds of trapped-ion qubits.

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arXiv2024

Resource-efficient context-aware dynamical decoupling embedding for arbitrary large-scale quantum algorithms

We introduce and implement GraphDD: an efficient method for real-time, circuit-specific, optimal embedding of dynamical decoupling (DD) into executable quantum algorithms. We demonstrate that for an arbitrary quantum circuit, GraphDD exactly refocuses both quasi-static single-qubit dephasing and crosstalk idling errors over the entire circuit, while using a minimal number of additional single-qubit gates embedded into idle periods. The method relies on a graph representation of the embedding problem, where the optimal decoupling sequence can be efficiently calculated using an algebraic computation that scales linearly with the number of idles. This allows optimal DD to be embedded during circuit compilation, without any calibration overhead, additional circuit execution, or numerical optimization. The method is generic and applicable to any arbitrary circuit; in compiler runtime the specific pulse-sequence solutions are tailored to the individual circuit, and consider a range of contextual information on circuit structure and device connectivity. We verify the ability of GraphDD to deliver enhanced circuit-level error suppression on 127-qubit IBM devices, showing that the optimal circuit-specific DD embedding resulting from GraphDD provides orders of magnitude improvements to measured circuit fidelities compared with standard embedding approaches available in Qiskit.

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arXiv2024

Benchmarking bosonic modes for quantum information with randomized displacements

Bosonic modes are prevalent in all aspects of quantum information processing. However, existing tools for characterizing the quality, stability, and noise properties of bosonic modes are limited, especially in a driven setting. Here, we propose, demonstrate, and analyze a bosonic randomized benchmarking (BRB) protocol that uses randomized displacements of the bosonic modes in phase space to determine their quality. We investigate the impact of common analytic error models, such as heating and dephasing, on the distribution of outcomes over randomized displacement trajectories in phase space. We show that analyzing the distinctive behavior of the mean and variance of this distribution - describable as a gamma distribution - enables identification of error processes, and quantitative extraction of error rates and correlations using a minimal number of measurements. We experimentally validate the analytical models by injecting engineered noise into the motional mode of a trapped ion system and performing the bosonic randomized benchmarking protocol, showing good agreement between experiment and theory. Finally, we investigate the intrinsic error properties in our system, identifying the presence of highly correlated dephasing noise as the dominant process.

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arXiv2024

In-situ-tunable spin-spin interactions in a Penning trap with in-bore optomechanics

Experimental implementations of quantum simulation must balance the controllability of the quantum system under test with decoherence typically introduced through interaction with external control fields. The ratio of coherent interaction strength to decoherence induced by stimulated emission in atomic systems is typically determined by hardware constraints, limiting the flexibility needed to explore different operating regimes. Here, we present an optomechanical system for in-situ tuning of the coherent spin-motion and spin-spin interaction strength in two-dimensional ion crystals confined in a Penning trap. The system introduces active optical positioners into the tightly constrained space of the bore of a superconducting magnet, allowing adjustability of the key hardware parameter which determines the ratio of coherent to incoherent light-matter interaction for fixed optical power. Using precision closed-loop piezo-actuated positioners, the system permits in-situ tuning of the angle-of-incidence of laser beams incident on the ion crystal up to $θ_{\text{ODF}}\approx 28^\circ$. We characterize the system using measurements of the induced mean-field spin precession under the application of an optical dipole force in ion crystals cooled below the Doppler limit through electromagnetically induced transparency cooling. These experiments show approximately a $\times2$ variation in the ratio of the coherent to incoherent interaction strength with changing $θ_{\text{ODF}}$, consistent with theoretical predictions. We characterize system stability over 6000 seconds; rigid mounting of optomechanics to the ion trap structure reduces differential laser movements to approximately $2\times 10^{-3}$ degrees per hour, enabling long-duration experiments. These technical developments will be crucial in future quantum simulations and sensing applications.

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arXiv2023

Direct observation of geometric phase in dynamics around a conical intersection

Conical intersections are ubiquitous in chemistry and physics, often governing processes such as light harvesting, vision, photocatalysis, and chemical reactivity. They act as funnels between electronic states of molecules, allowing rapid and efficient relaxation during chemical dynamics. In addition, when a reaction path encircles a conical intersection, the molecular wavefunction experiences a geometric phase, which can affect the outcome of the reaction through quantum-mechanical interference. Past experiments have measured indirect signatures of geometric phases in scattering patterns and spectroscopic observables, but there has been no direct observation of the underlying wavepacket interference. Here, we experimentally observe geometric-phase interference in the dynamics of a wavepacket travelling around an engineered conical intersection in a programmable trapped-ion quantum simulator. To achieve this, we develop a technique to reconstruct the two-dimensional wavepacket densities of a trapped ion. Experiments agree with the theoretical model, demonstrating the ability of analog quantum simulators -- such as those realised using trapped ions -- to accurately describe nuclear quantum effects.

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arXiv2023

Predicting molecular vibronic spectra using time-domain analog quantum simulation

Spectroscopy is one of the most accurate probes of the molecular world. However, predicting molecular spectra accurately is computationally difficult because of the presence of entanglement between electronic and nuclear degrees of freedom. Although quantum computers promise to reduce this computational cost, existing quantum approaches rely on combining signals from individual eigenstates, an approach that is difficult to scale because the number of eigenstates grows exponentially with molecule size. Here, we introduce a method for scalable analog quantum simulation of molecular spectroscopy, by performing simulations in the time domain. Our approach can treat more complicated molecular models than previous ones, requires fewer approximations, and can be extended to open quantum systems with minimal overhead. We present a direct mapping of the underlying problem of time-domain simulation of molecular spectra to the degrees of freedom and control fields available in a trapped-ion quantum simulator. We experimentally demonstrate our algorithm on a trapped-ion device, exploiting both intrinsic electronic and motional degrees of freedom, showing excellent quantitative agreement for a single-mode vibronic photoelectron spectrum of SO$_2$.

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arXiv2023

Experimental benchmarking of an automated deterministic error suppression workflow for quantum algorithms

Excitement about the promise of quantum computers is tempered by the reality that the hardware remains exceptionally fragile and error-prone, forming a bottleneck in the development of novel applications. In this manuscript, we describe and experimentally test a fully autonomous workflow designed to deterministically suppress errors in quantum algorithms from the gate level through to circuit execution and measurement. We introduce the key elements of this workflow, delivered as a software package called Fire Opal, and survey the underlying physical concepts: error-aware compilation, automated system-wide gate optimization, automated dynamical decoupling embedding for circuit-level error cancellation, and calibration-efficient measurement-error mitigation. We then present a comprehensive suite of performance benchmarks executed on IBM hardware, demonstrating up to > 1000X improvement over the best alternative expert-configured techniques available in the open literature. Benchmarking includes experiments using up to 16 qubit systems executing: Bernstein Vazirani, Quantum Fourier Transform, Grover's Search, QAOA, VQE, Syndrome extraction on a five-qubit Quantum Error Correction code, and Quantum Volume. Experiments reveal a strong contribution of Non-Markovian errors to baseline algorithmic performance; in all cases the deterministic error-suppression workflow delivers the highest performance and approaches incoherent error bounds without the need for any additional sampling or randomization overhead, while maintaining compatibility with all additional probabilistic error suppression techniques.

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