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Explore our latest research and publications in quantum computing

Scaling advantage with quantum-enhanced memetic tabu search for LABS

Alejandro Gomez Cadavid, Pranav Chandarana, Sebastián V. Romero, Jan Trautmann, Enrique Solano, Taylor Lee Patti, Narendra N. Hegade

We introduce quantum-enhanced memetic tabu search (QE-MTS), a non-variational hybrid algorithm that achieves state-of-the-art scaling for the low-autocorrelation binary sequence (LABS) problem. By seeding the classical memetic tabu search (MTS) with high-quality initial states from digitized counterdiabatic quantum optimization (DCQO), our method suppresses the empirical time-to-solution scaling to O(1.24^N) for sequence length N between 27 and 37. We measure time-to-solution in objective-function evaluations and benchmark each system size using 100 independent replicates (each comprising 100 randomized seeds), enabling a robust distributional scaling analysis. This scaling surpasses the best-known classical heuristic O(1.34^N) and improves upon the O(1.46^N) of the quantum approximate optimization algorithm at a 6x reduction in circuit depth. A two-stage bootstrap analysis confirms the scaling advantage and projects a crossover point at N of roughly 47 and beyond, where QE-MTS is expected to outperform its classical counterpart. Overall, the results illustrate a practical hybrid-sequential workflow in which shallow quantum circuits provide biased initial populations that measurably improve the scaling of a high-performance classical metaheuristic. A Kipu Quantum and NVIDIA collaboration, published in Quantum Machine Intelligence 8, 92 (2026).

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Off-line quantum-advantage feature extraction for industrial production

Carlos Flores-Garrigós, Gabriel D. Alvarado Barrios, Qi Zhang, Anton Simen, Enrique Solano

Quantum computing is no longer a lab curiosity for academic research. Industrial processors exceeding 100 qubits are commercially accessible and, for the first time, can extract information from data in ways that classical algorithms struggle to match. The most direct way to monetize this capability for industrial production today is quantum feature extraction: turning raw business data (images, customer records, molecules, or sensor readings) into richer representations that outperform standard machine learning models. There is one obstacle, however, that stands between today's demonstrations and tomorrow's production systems: every sample of data costs a quantum computing execution, making per-sample processing on quantum hardware unviable at enterprise scale. This work introduces quantum feature surrogates, a framework developed by Kipu Quantum that breaks this bottleneck. Instead of asking the quantum computer to look at every single sample, the framework lets it process a small, carefully chosen subsample whose distribution faithfully represents the full set. A simple classical surrogate model then learns the quantum-induced patterns and applies them to the rest of the dataset at near-zero cost. The quantum processor stops being a per-sample engine and becomes a teacher of representations, while production inference runs entirely on classical hardware. We demonstrate at least 5× fewer quantum executions for the same accuracy and substantially more as data volumes grow, classical inference at deployment with no quantum queue or per-prediction latency penalty, and accuracy matching the full quantum baseline on a real satellite-image benchmark (87% vs. an 84% ResNet-50 classical baseline, equalling the full quantum pipeline at one fifth of the quantum cost). The framework is industry-ready across satellite image classification, customer analytics, medical imaging, drug screening, churn prediction, and many more high-volume enterprise workloads.

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Hybrid Quantum-Classical Model That Combines Spatial-Temporal EEG and Digitized Counterdiabatic Quantum Features for Motor Imagery Classification

Carter RE, Wieczorek MA, Pacheco-Spann LM, Li F, Dougherty JM, Johnson PW, Wenzel B, Pandey M, Dalal A, Solano E, Yilmazer K, Soekadar SR, Thielen KR, Bruce CJ

A hybrid quantum-classical model for motor imagery classification from electroencephalogram (EEG) waveforms, combining deep-learning spatial-temporal feature extraction with digitized counterdiabatic quantum features. Trained on 50 participants from a published, de-identified data set and evaluated on a held-out participant, the model reached 88.8% classification accuracy (AUROC 0.962) on motor-imagery EEG, demonstrating that quantum-enriched features can robustly classify brain signals and setting a foundation for quantum computing in healthcare. A Mayo Clinic and Kipu Quantum collaboration.

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Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware

Carlos Flores-Garrigós, Anton Simen, Qi Zhang, Enrique Solano, Narendra N. Hegade, Sayonee Ray, Claudio Girotto, Jason Iaconis, Martin Roetteler

We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorporates multivariate dependencies beyond standard quadratic encodings. In contrast to QUBO-based approaches, the proposed model includes one-, two-, and three-body interaction terms derived from mutual-information measures, enabling the objective function to capture feature relevance, pairwise redundancy, and higher-order statistical structure within a unified energy model. To suppress trivial all-selected solutions, we further include structured linear penalties that promote sparsity while preserving informative variables. The resulting HUBO instances are optimized with digitized counterdiabatic quantum optimization on IonQ Forte and compared against noiseless quantum simulation as well as two classical dimensionality-reduction baselines: SelectKBest based on mutual information and principal component analysis (PCA). We evaluate the proposed workflow on two benchmark classification datasets, namely the Gallstone dataset and the Spambase dataset, and analyze both predictive performance and selected-subset structure. The results show good qualitative agreement between hardware executions and noiseless simulations, supporting the feasibility of implementing higher-order feature-selection Hamiltonians on current trapped-ion processors. In addition, the quantum approach yields competitive classification performance while producing compact and informative feature subsets, highlighting the potential of higher-order quantum optimization for machine-learning preprocessing tasks.

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Protein folding on a 64-qubit trapped-ion hardware via counterdiabatic quantum optimization

Alejandro Gomez Cadavid, Pavle Nikačević, Pranav Chandarana, Sebastián V. Romero, Enrique Solano, Narendra N. Hegade, Miguel Angel Lopez-Ruiz, Claudio Girotto, Hanna Linn, Hakan Doga, Evgeny Epifanovsky, Panagiotis Kl. Barkoutsos, Ananth Kaushik, Martin Roetteler

We report the largest trapped-ion hardware demonstration of lattice protein-folding optimization to date, using bias-field digitized counterdiabatic quantum optimization (BF-DCQO) on a fully connected 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. Six peptide sequences with 14-16 amino-acid residues are encoded using a coarse-grained tetrahedral lattice model, yielding higher-order spin-glass Hamiltonians with long-range interactions involving up to five-body terms and mapped to 46-61 qubits. The resulting instances are demanding for near-term quantum hardware because low-energy configurations must satisfy backbone-geometry constraints while optimizing dense residue-contact interactions. BF-DCQO uses a non-variational bias-feedback mechanism, where low-energy samples from each round define longitudinal fields that guide subsequent quantum evolutions. Across the studied instances, BF-DCQO shifts raw sampled energy distributions toward lower energies than uniform random sampling, with the strongest improvements appearing in residue-contact variables. To preserve this signal, we introduce a consensus-based post-processing pipeline that combines quantum-learned contact information with feasible backbone geometries. The resulting hybrid workflow reaches the classical reference energy in multiple instances and improves over the corresponding random-seeded pipeline. These results show that BF-DCQO can generate structured samples for dense protein-folding Hamiltonians at previously unexplored trapped-ion scales.

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The Quest for Quantum Advantage in Combinatorial Optimization: End-to-end Benchmarking of Quantum Solvers vs. Multi-core Classical Solvers

Pranav Chandarana, Alejandro Gomez Cadavid, Enrique Solano, Thorsten Koch, Stefan Woerner, Narendra N. Hegade

We perform an end-to-end benchmark of a hybrid sequential quantum computing (HSQC) solver for higher-order unconstrained binary optimization (HUBO), executed on IBM Heron r3 quantum processors to evaluate the potential of current quantum hardware for combinatorial optimization with sub-second end-to-end runtimes. All reported runtimes include the complete pipeline (from preprocessing to QPU execution and postprocessing) under strict wall-clock accounting. Across 20 benchmark instances, a single hybrid attempt produces high-quality solutions in less than one second, matching the ground-state energy in 14 cases. At the same runtime, CPU-based solvers, including simulated annealing, memetic tabu search, and EasySolve, do not reach the value obtained by HSQC, whereas an enhanced parallel tempering method and the GPU-accelerated solver ABS3 reach or surpass it. These results show that HSQC, executed on a single QPU, can achieve performance competitive with strong classical solvers running on 128 vCPUs or 8 NVIDIA A100 GPUs, while also providing a reproducible system-level benchmark for tracking progress as quantum hardware and hybrid sequential workflows improve.

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Digitized counterdiabatic quantum critical dynamics

Anne-Maria Visuri, Alejandro Gomez Cadavid, Balaganchi A. Bhargava, Sebastián V. Romero, András Grabarits, Pranav Chandarana, Enrique Solano, Adolfo del Campo, Narendra N. Hegade

We experimentally demonstrate that a digitized counterdiabatic quantum protocol reduces the number of topological defects created during a fast quench across a quantum phase transition. To show this, we perform quantum simulations of one- and two-dimensional transverse-field Ising models driven from the paramagnetic to the ferromagnetic phase. We utilize superconducting cloud-based quantum processors with up to 156 qubits. Our data reveal that the digitized counterdiabatic protocol reduces defect formation by up to 48% in the fast-quench regime -- an improvement hard to achieve through digitized quantum annealing under current noise levels. The experimental results closely match theoretical and numerical predictions at short evolution times, before deviating at longer times due to hardware noise. In one dimension, we derive an analytic solution for the defect number distribution in the fast-quench limit. For two-dimensional geometries, where analytical solutions are unknown and numerical simulations are challenging, we use advanced matrix-product-state methods. Our findings indicate a practical way to control the topological defect formation during fast quenches and highlight the utility of counterdiabatic protocols for quantum optimization and quantum simulation in material design on current quantum processors.

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Analog Counterdiabatic Quantum Computing

Qi Zhang, Narendra N. Hegade, Alejandro Gomez Cadavid, Lucas Lassabli`ere, Jan Trautmann, S´ebastien Perseguers, Enrique Solano, Lo¨ıc Henriet, Eric Michon

We propose analog counterdiabatic quantum computing (ACQC) to tackle combinatorial optimization problems on neutral-atom quantum processors. While these devices allow for the use of hundreds of qubits, adiabatic quan- tum computing struggles with non-adiabatic errors, which are inevitable due to the hardware’s restricted coherence time. We design counterdiabatic proto- cols to circumvent those limitations via ACQC on analog quantum devices with ground-Rydberg qubits. To demonstrate the effectiveness of our paradigm, we experimentally apply it to the maximum independent set (MIS) problem with up to 100 qubits and show an enhancement in the approximation ratio with a short evolution time. We believe ACQC establishes a path toward quantum advantage for a variety of industry use cases.

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Large-scale portfolio optimization on a trapped-ion quantum computer

Alejandro Gomez Cadavid, Ananth Kaushik, Pranav Chandarana, Miguel Angel Lopez-Ruiz, Gaurav Dev, Willie Aboumrad, Qi Zhang, Claudio Girotto, Sebastián V. Romero, Martin Roetteler, Enrique Solano, Marco Pistoia, Narendra N. Hegade

We present an end-to-end pipeline for large-scale portfolio selection with cardinality constraints and experimentally demonstrate it on trapped-ion quantum processors using hardware-aware decomposition. Building on RMT-based correlation-matrix denoising and community detection, we identify correlated asset groups and introduce a correlation-guided greedy splitting scheme that caps each cluster by the executable qubit budget. Each cluster defines a hardware-embeddable QUBO subproblem that we solve using bias-field digitized counterdiabatic quantum optimization (BF-DCQO), a non-variational method that avoids classical parameter-training loops. We recombine low-energy candidates into global portfolios and enforce feasibility with a two-stage post-processing routine: fast repair followed by a cardinality-preserving swap local search. We benchmark the workflow on a 250-asset universe taken from the S&P 500 and execute subproblems on a 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. We observe that larger executable subproblem sizes reduce decomposition error and systematically improve final objective values and risk-return trade-offs relative to randomized baselines under identical post-processing. Overall, the results establish a hardware-tested route for scaling financial optimization problems, defined by a trade space in which executable problem size and circuit cost are balanced against the resulting solution quality.

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Quantum-enhanced satellite image classification

Qi Zhang, Anton Simen, Carlos Flores-Garrigós, Gabriel Alvarado Barrios, Paolo A. Erdman, Enrique Solano, Aaron C. Kemp, Vincent Beltrani, Vedangi Pathak, Hamed Mohammadbagherpoor

We demonstrate the application of a quantum feature extraction method to enhance multi-class image classification for space applications. By harnessing the dynamics of many-body spin Hamiltonians, the method generates expressive quantum features that, when combined with classical processing, lead to quantum-enhanced classification accuracy. Using a strong and well-established ResNet50 baseline, we achieved a maximum classical accuracy of 83%, which can be improved to 84% with a transfer learning approach. In contrast, applying our quantum-classical method the performance is increased to 87% accuracy, demonstrating a clear and reproducible improvement over robust classical approaches. Implemented on several of IBM’s quantum processors, our hybrid quantum-classical approach delivers consistent gains of 2-3% in absolute accuracy. These results highlight the practical potential of current and near-term quantum processors in high-stakes, data-driven domains such as satellite imaging and remote sensing, while suggesting broader applicability in real-world machine learning tasks.

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