arXiv subjects

Kristel Michielsen

Publications and source records attributed to Kristel Michielsen.

At least 37 records · Page 2Linked to original sources

arXiv2025

Interplay of Confinement and Localization in a Programmable Rydberg Atom Chain

Analog quantum simulators promise access to complex many-body dynamics, yet their performance is ultimately set by how device imperfections compete with intrinsic physical mechanisms. Here we present an end-to-end study of correlation spreading in a programmable Rydberg-atom chain realizing a longitudinal-field transverse-field Ising model, focusing on the joint impact of confinement and effective disorder. Experiments performed on QuEra's Aquila quantum processor are benchmarked against large-scale coherent emulations using the Juelich Quantum Annealing Simulator (JUQAS), enabling the controlled inclusion of realistic hardware imperfections. In the ideal coherent limit, a tunable longitudinal field induces confinement of domain-wall excitations into mesonic bound states, leading to a progressive truncation of the correlation light cone. When experimentally relevant inhomogeneities and fluctuations are included, correlations instead saturate at finite distance even in the nominally deconfined regime, revealing localization driven by emergent disorder. The close quantitative agreement between noisy emulations and experimental data allows us to attribute the observed saturation to specific hardware error channels and to identify the dominant contribution. Our results establish a practical framework for diagnosing and modeling error-induced localization in Rydberg quantum processors, while demonstrating that confinement remains a robust and programmable mechanism for engineering non-ergodic dynamics on near-term quantum hardware.

quant-ph↗

arXiv2025

Diagnosing crosstalk in large-scale QPUs using zero-entropy classical shadows

As quantum processing units (QPUs) scale toward hundreds of qubits, diagnosing noise-induced correlations (crosstalk) becomes critical for reliable quantum computation. In this work, we introduce Zero-Entropy Classical Shadows (ZECS), a diagnostic tool that uses information of a rank-one quantum state tomography (QST) reconstruction from classical shadow (CS) information to make a crosstalk diagnosis. We use ZECS on trapped ion and superconductive QPUs, including ionq_forte (36 qubits), ibm_brisbane (127 qubits), and ibm_fez (156 qubits), using from 1,000 to 6,000 samples. With these samples, we use the ZECS to characterize crosstalk among disjoint qubit subsets across the full hardware. This information is then used to select low-crosstalk qubit subsets on ibm_fez for executing the Quantum Approximate Optimization Algorithm (QAOA) on a 20-qubit problem. Compared to the best qubit selection via Qiskit transpilation, our method improves solution quality by 10% and increases algorithmic coherence by 33%. ZECS offers a scalable and measurement-efficient approach to diagnosing crosstalk in large-scale QPUs.

quant-ph↗

arXiv2025

Learning-Driven Annealing with Adaptive Hamiltonian Modification for Solving Large-Scale Problems on Quantum Devices

We present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as short annealing times and integrated control errors. Unlike other iterative methods, LDA does not tune the annealing procedure (e.g. annealing time or annealing schedule), but instead learns about the problem structure to adaptively modify the problem Hamiltonian. By deforming the instantaneous energy spectrum, LDA suppresses transitions into high-energy states and focuses the evolution into low-energy regions of the Hilbert space. We demonstrate the efficacy of LDA by developing a hybrid quantum-classical solver for large-scale spin glasses. The hybrid solver is based on a comprehensive study of the internal structure of spin glasses, outperforming other quantum and classical algorithms (e.g., reverse annealing, cyclic annealing, simulated annealing, Gurobi, Toshiba's SBM, VeloxQ and D-Wave hybrid) on 5580-qubit problem instances in both runtime and lowest energy. LDA is a step towards practical quantum computation that enables today's quantum devices to compete with classical solvers.

quant-ph↗

arXiv2025

Quantum speed-up for solving the one-dimensional Hubbard model using quantum annealing

The Hubbard model has occupied the minds of condensed matter physicists for most part of the last century. This model provides insight into a range of phenomena in correlated electron systems. We wish to examine the paradigm of quantum algorithms for solving such many-body problems. The focus of our current work is on the one-dimensional model which is integrable, meaning that there exist analytical results for determining its ground state. In particular, we demonstrate how to perform a gate-based quantum computer simulation of quantum annealing for the Hubbard Hamiltonian. We perform simulations for systems with up to 40 qubits to study the scaling of required annealing time for obtaining the ground state. We find that for the half-filled cases considered, there is a substantial quantum speed-up over algorithms based on the Bethe-ansatz equations.

quant-ph↗

arXiv2025

QUEST: QUantum-Enhanced Shared Transportation

We introduce ``Windbreaking-as-a-Service'' (WaaS) as an innovative approach to shared transportation in which larger ``windbreaker'' vehicles provide aerodynamic shelter for ``windsurfer'' vehicles, thereby reducing drag and fuel consumption. As a computational framework to solve the large-scale matching and assignment problems that arise in WaaS, we present \textbf{QUEST} (Quantum-Enhanced Shared Transportation). Specifically, we formulate the pairing of windbreakers and windsurfers -- subject to timing, speed, and vehicle-class constraints -- as a mixed-integer quadratic problem (MIQP). Focusing on a single-segment prototype, we verify the solution classically via the Hungarian Algorithm, a Gurobi-based solver, and brute-force enumeration of binary vectors. We then encode the problem as a Quadratic Unconstrained Binary Optimization (QUBO) and map it to an Ising Hamiltonian, enabling the use of the Quantum Approximate Optimization Algorithm (QAOA) and other quantum and classical annealing technologies. Our quantum implementation successfully recovers the optimal assignment identified by the classical methods, confirming the soundness of the QUEST pipeline for a controlled prototype. While QAOA and other quantum heuristics do not guarantee a resolution of the fundamental complexity barriers, this study illustrates how the WaaS problem can be systematically translated into a quantum-ready model. It also lays the groundwork for addressing multi-segment scenarios and potentially leveraging quantum advantage for large-scale shared-transportation instances.

quant-ph↗

arXiv2025

Scalable Hardware Maturity Probe for Quantum Accelerators via Harmonic Analysis of QAOA

As quantum processors begin operating as tightly coupled accelerators inside high-performance computing (HPC) facilities, dependable and reproducible behavior becomes a gating requirement for scientific and industrial workloads. We present a hardware-maturity probe that quantifies a device's reliability by testing whether it can repeatedly reproduce the provably global optima of single-layer Quantum Approximate Optimization Algorithm (QAOA) circuits. Using harmonic analysis, we derive closed-form upper bounds on the number of stationary points in the p=1 QAOA cost landscape for broad classes of combinatorial-optimization problems. These bounds yield an exhaustive yet low-overhead grid-sampling scheme with analytically verifiable outcomes. The probe integrates reliability-engineering notions like run-to-failure statistics, confidence-interval estimation, and reproducibility testing into a single, application-centric benchmark. Our framework supplies a standardized dependability metric for hybrid quantum-HPC (QHPC) workflows.

quant-ph↗

arXiv2025

Towards a Digital Twin of Noisy Quantum Computers: Calibration-Driven Emulation of Transmon Qubits

We develop a parametric error model to construct a digital twin of a superconducting transmon qubit device. The model parameters are extracted from hardware calibration data and supplementary benchmarking circuits, providing a dynamic, system-specific representation of noise and gate imperfections. Given the strong dependence of qubit performance on calibration procedures, our approach captures real-time device fluctuations. By incorporating predominant noise sources derived from underlying physical processes, we enhance the emulation's accuracy while reducing the data required for model fitting. Finally, we validate our model by comparing its predictions with experimental results from a 5-qubit QPU, achieving a mean total variation distance of 0.15 between the shot distributions. This digital twin can be leveraged for predictive performance analysis, error mitigation strategies, and the optimization of quantum protocols, contributing to more reliable quantum computations.

quant-ph↗

arXiv2025

Towards a Linear-Ramp QAOA protocol: Evidence of a scaling advantage in solving some combinatorial optimization problems

The Quantum Approximate Optimization Algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $\{γ_i, β_i\}_{i=0}^{p-1}$. While most prior work has focused on classically optimizing these parameters, we demonstrate that fixed linear ramp schedules, linear ramp QAOA (LR-QAOA), can efficiently approximate optimal solutions across diverse COPs. Simulations with up to $N_q=42$ qubits and $p=400$ layers suggest that the success probability scales as $P(x^*) \approx 2^{-η(p) N_q + C}$, where $η(p)$ decreases with increasing $p$. For example, in Weighted Maxcut instances, $η(10) = 0.22$ improves to $η(100) = 0.05$. Comparisons with classical algorithms, including simulated annealing, Tabu Search, and branch-and-bound, show a scaling advantage for LR-QAOA. We show results of LR-QAOA on multiple QPUs (IonQ, Quantinuum, IBM) with up to $N_q = 109$ qubits, $p=100$, and circuits requiring 21,200 CNOT gates. Finally, we present a noise model based on two-qubit gate counts that accurately reproduces the experimental behavior of LR-QAOA.

quant-ph↗

arXiv2025

Quantum computing and artificial intelligence: status and perspectives

This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.

quant-ph↗

arXiv2025

Evaluating the performance of quantum processing units at large width and depth

Quantum computers have now surpassed classical simulation limits, yet noise continues to limit their practical utility. As the field shifts from proof-of-principle demonstrations to early deployments, there is no standard method for meaningfully and scalably comparing heterogeneous quantum hardware. Existing benchmarks typically focus on gate-level fidelity or constant-depth circuits, offering limited insight into algorithmic performance at depth. Here we introduce a benchmarking protocol based on the linear ramp quantum approximate optimization algorithm (LR-QAOA), a fixed-parameter, deterministic variant of QAOA. LR-QAOA quantifies a QPU's ability to preserve a coherent signal as circuit depth increases, identifying when performance becomes statistically indistinguishable from random sampling. We apply this protocol to 24 quantum processors from six vendors, testing problems with up to 156 qubits and 10,000 layers across 1D-chains, native layouts, and fully connected topologies. This constitutes the most extensive cross-platform quantum benchmarking effort to date, with circuits reaching a million two-qubit gates. LR-QAOA offers a scalable, unified benchmark across platforms and architectures, making it a tool for tracking performance in quantum computing.

quant-ph↗

arXiv2025

Transfer learning of optimal QAOA parameters in combinatorial optimization

Solving combinatorial optimization problems (COPs) is a promising application of quantum computation, with the Quantum Approximate Optimization Algorithm (QAOA) being one of the most studied quantum algorithms for solving them. However, multiple factors make the parameter search of the QAOA a hard optimization problem. In this work, we study transfer learning (TL), a methodology to reuse pre-trained QAOA parameters of one problem instance into different COP instances. This methodology can be used to alleviate the necessity of classical optimization to find good parameters for individual problems. To this end, we select small cases of the traveling salesman problem (TSP), the bin packing problem (BPP), the knapsack problem (KP), the weighted maximum cut (MaxCut) problem, the maximal independent set (MIS) problem, and portfolio optimization (PO), and find optimal $β$ and $γ$ parameters for p layers. We compare how well the parameters found for one problem adapt to the others. Among the different problems, BPP is the one that produces the best transferable parameters, maintaining the probability of finding the optimal solution above a quadratic speedup over random guessing for problem sizes up to 42 qubits and p = 10 layers. Using the BPP parameters, we perform experiments on IonQ Harmony and Aria, Rigetti Aspen-M-3, and IBM Brisbane of MIS instances for up to 18 qubits. The results indicate that IonQ Aria yields the best overlap with the ideal probability distribution. Additionally, we show that cross-platform TL is possible using the D-Wave Advantage quantum annealer with the parameters found for BPP. We show an improvement in performance compared to the default protocols for MIS with up to 170 qubits. Our results suggest that there are QAOA parameters that generalize well for different COPs and annealing protocols.

quant-ph↗

arXiv2025

Understanding the physics of D-Wave annealers: From Schrödinger to Lindblad to Markovian Dynamics

Understanding the physical nature of the D-Wave annealers remains a subject of active investigation. In this study, we analyze the sampling behavior of these systems and explore whether their results can be replicated using quantum and Markovian models. Employing the standard and the fast annealing protocols, we observe that the D-Wave annealers sample states with frequencies matching the Gibbs distribution for sufficiently long annealing times. Using Bloch equation simulations for single-qubit problems and Lindblad and Markovian master equations for two-qubit systems, we compare experimental data with theoretical predictions. Our results provide insights into the role of quantum mechanics in these devices.

quant-ph↗

arXiv2025

Performance of quantum annealing for 2-SAT problems with multiple satisfying assignments

Using a specially constructed set of hard 2-SAT problems with four satisfying assignments, we study the scaling and sampling performance of numerical simulation of quantum annealing as well as that of the physical quantum annealers offered by D-Wave. To this end, we use both the standard quantum annealing and reverse annealing protocols in both our simulations and on the D-Wave quantum annealer. In the case of ideal quantum annealing the sampling behavior can be explained by perturbation theory and the scaling behavior of the time to solution depends on the scaling behavior of the minimum energy gap between the ground state and the first excited state of the annealing Hamiltonian. The corresponding results from the D-Wave quantum annealers do not fit to this ideal picture, but suggest that the scaling of the time to solution from the quantum annealers matches those calculated from the equilibrium probability distribution.

quant-ph↗

arXiv2025

Unraveling Reverse Annealing: A Study of D-Wave Quantum Annealers

D-Wave quantum annealers offer reverse annealing as a feature allowing them to refine solutions to optimization problems. This paper investigates the influence of key parameters, such as annealing times and reversal distance, on the behavior of reverse annealing by studying models containing up to 1000 qubits. Through the analysis of theoretical models and experimental data, we explore the interplay between quantum and classical processes. Our findings provide a deeper understanding that can better equip users to fully harness the potential of the D-Wave annealers

quant-ph↗

arXiv2025

Quantum Annealing based Power Grid Partitioning for Parallel Simulation

Graph partitioning has many applications in powersystems from decentralized state estimation to parallel simulation. Focusing on parallel simulation, optimal grid partitioning minimizes the idle time caused by different simulation times for the sub-networks and their components and reduces the overhead required to simulate the cuts. Partitioning a graph into two parts such that, for example, the cut is minimal and the subgraphs have equal size is an NP-hard problem. In this paper we show how optimal partitioning of a graph can be obtained using quantum annealing (QA). We show how to map the requirements for optimal splitting to a quadratic unconstrained binary optimization (QUBO) formulation and test the proposed formulation using a current D-Wave QPU. We show that the necessity to find an embedding of the QUBO on current D-Wave QPUs limits the problem size to under 200 buses and notably affects the time-to-solution. We finally discuss the implications on near-term implementation of QA in combination to traditional CPU or GPU based simulation.

eess.SY↗

arXiv2024

Scalable General Error Mitigation for Quantum Circuits

In quantum computing, error mitigation is a method to improve the results of an error-prone quantum processor by post-processing them on a classical computer. In this work, we improve the General Error Mitigation (GEM) method for scalability. GEM relies on the use of a matrix to represent the device error, which requires the execution of $2^{n+1}$ calibration circuits on the quantum hardware, where $n$ is the number of qubits. With our improved method, the number of calibration runs is independent of the number of qubits and depends only on the number of non-zero states in the output distribution. We run 1853 randomly generated circuits with widths between 2-7 qubits and depths between 10-140 gates on IBMQ superconducting devices. The experiments show that the mitigation works comparably well to GEM, while requiring a fraction of the calibration runs. Finally, an experiment to mitigate errors in a 100 qubit circuit demonstrates the scalable features of our method.

quant-ph↗

arXiv2024

Observation of Josephson Harmonics in Tunnel Junctions

Approaches to developing large-scale superconducting quantum processors must cope with the numerous microscopic degrees of freedom that are ubiquitous in solid-state devices. State-of-the-art superconducting qubits employ aluminum oxide (AlO$_x$) tunnel Josephson junctions as the sources of nonlinearity necessary to perform quantum operations. Analyses of these junctions typically assume an idealized, purely sinusoidal current-phase relation. However, this relation is only expected to hold in the limit of vanishingly low-transparency channels in the AlO$_x$ barrier. Here we show that the standard current-phase relation fails to accurately describe the energy spectra of transmon artificial atoms across various samples and laboratories. Instead, a mesoscopic model of tunneling through an inhomogeneous AlO$_x$ barrier predicts percent-level contributions from higher Josephson harmonics. By including these in the transmon Hamiltonian, we obtain orders of magnitude better agreement between the computed and measured energy spectra. The presence and impact of Josephson harmonics has important implications for developing AlO$_x$-based quantum technologies including quantum computers and parametric amplifiers. As an example, we show that engineered Josephson harmonics can reduce the charge dispersion and the associated errors in transmon qubits by an order of magnitude, while preserving their anharmonicity.

quant-ph↗

arXiv2024

On the oxygen $p$ states in superconducting nickelates

While key attention in transition-metal oxides is usually devoted to the $d$ states of the transition-metal ion, the O$(2p)$ states usually also carry important physics. We here examine these $p$ states in representatives of the novel superconducting nickelates, as described in realistic dynamical mean-field theory. Since the materials are located on the boundary between Mott-Hubbard and charge-transfer systems, the role of oxygen is expectedly subtle. Strong reduction of doped holes on oxygen and first asymmetry effects are featured in infinite-layer nickelates. A pronounced nature of bridging $p_z$ orbitals is identified in the La$_3$Ni$_2$O$_7$ system.

cond-mat.str-el↗