Researchers Ernesto Acosta and Carlos Cano of the University of Granada, together with Guillermo Botella of Complutense University of Madrid, have reformulated the parameter-optimization problem of variational quantum algorithms as a Quadratic Unconstrained Binary Optimization problem, the native language of quantum annealers. Their work, published as volume 8, article 101 of the journal, demonstrates that the same machines designed to find low-energy configurations of combinatorial optimization problems can also serve as scalable optimizers for gate-based quantum models, opening a hybrid pathway that combines gate-model quantum computing, quantum annealing, and classical control in a single training loop.

The core insight behind the method is elegant. Variational quantum algorithms depend on a Hamiltonian expressed in terms of trainable rotation angles, and the exponential structure of the circuit’s unitary operator can be transformed, through a heuristic substitution that replaces imaginary phases with real-valued angles, into sums of real exponential terms. Those terms map naturally onto the quadratic binary cost functions that quantum annealers minimize. In other words, the mathematical structure of the ansatz itself provides the bridge: instead of estimating gradients with hundreds of circuit shots, the team encodes candidate parameter regions directly into QUBO form and asks the annealer to find the configuration with the lowest energy.

Because continuous rotation angles must be represented with discrete binary variables, the researchers developed a recursive refinement strategy that progressively narrows the search space. The parameter range is partitioned into segments, validation points are sampled within each partition, and the QUBO problem is solved to identify the most promising regions. The angle range is then rescaled — in their worked example, halved at each level — and centered on the best solution found so far, and the process repeats. This coarse-to-fine scheme approximates high-quality continuous solutions with a small number of discrete QUBO solves, trading resolution for tractability in a controlled way.

A key strength of the framework is its adaptability. The method exposes a rich set of configurable metaheuristic parameters, including the number of training levels, the number of partitions per angle, and the number of validation points per partition. These knobs allow practitioners to tune the balance between solution quality and computational cost according to the problem at hand and the computational resources available. The authors also integrate an adaptive metaheuristic optimization scheme rather than a fixed search rule, making the training procedure generalizable to arbitrary Hamiltonians rather than being tied to a specific circuit family or ansatz design.

To test the approach, the team benchmarked their adiabatic training scheme on publicly available classification datasets, including the Iris, Heart Disease, and Diabetes datasets from the UCI machine learning repository. The experimental evaluations show that the method achieves accuracy comparable to, and in some configurations better than, established classical and evolutionary optimizers, while significantly reducing computational overhead. The authors’ hyperparameter exploration reveals that the highest performance efficiency — accuracy gained per unit of training time — tends to occur at lower numbers of levels and partitions, meaning that modest configurations already capture most of the benefit before costs escalate.

The implications extend beyond raw benchmark numbers. Gradient-based training on near-term quantum hardware is plagued by noisy gradient estimates, since every partial derivative must be inferred from finite numbers of noisy circuit measurements. Meanwhile, barren plateaus, whose origins have been linked both to deep random circuits and to noise accumulation, can render gradients exponentially vanishing and classical optimizers effectively blind. A gradient-free, annealer-driven optimizer sidesteps both pathologies: it never estimates a derivative, and its search is driven by the energy landscape of the QUBO encoding rather than by local slope information that may not exist in any meaningful sense.

The study builds on a growing body of work connecting annealing-style hardware to machine learning. Previous research has shown that QUBO formulations can be used to train classical machine learning models, that Ising machines can serve as training engines for standard neural networks, and that universal adiabatic quantum computers can be harnessed for neural network training. An earlier preprint by the same team had already explored adiabatic training for variational quantum algorithms; the new journal publication consolidates that line of inquiry into a general, configurable framework with systematic experimental validation, code released through a public repository, and full experimental detail across multiple datasets and hyperparameter regimes.

What emerges is a compelling vision of near-term quantum computing as a genuinely hybrid enterprise. In the architecture the Granada and Madrid researchers describe, a gate-based quantum processor executes the variational circuit, a quantum annealer solves the training problem at each iteration, and a classical system orchestrates the recursive refinement and manages the workflow. Each platform contributes what it does best, and none has to shoulder the full burden alone. As annealing hardware scales and QUBO encodings grow more expressive, this division of labor could become a practical route to training quantum models that classical optimizers struggle to handle.

Challenges certainly remain. The heuristic substitution that enables real-valued QUBO encoding sacrifices strict unitarity, and the discretization of continuous parameters means solution precision depends on the depth of the refinement schedule. The authors’ own data show that pushing training levels and partitions higher does not always pay off, underscoring the need for careful configuration. Still, the demonstration that quantum annealers can train variational quantum algorithms with competitive quality and lower overhead marks a meaningful step. For a field hunting for any advantage in the noisy intermediate-scale era, recruiting one quantum machine to train another may prove to be one of the more inventive entries in the playbook.

Subject of Research: A QUBO-based method that uses quantum annealers to train variational quantum algorithms without gradients.

Article Title: QUBO-based training for VQAs on quantum annealers

Article References: Acosta, E., Botella, G., & Cano, C. (2026). QUBO-based training for VQAs on quantum annealers. Quantum Machine Intelligence, 8(2), Article 101. https://doi.org/10.1007/s42484-026-00441-1

Image Credits: AI Generated

DOI: 10.1007/s42484-026-00441-1

Keywords: quantum computing, variational quantum algorithms, quantum annealing, QUBO, optimization, barren plateaus, quantum machine learning, hybrid quantum computing, metaheuristics, NISQ era, gradient-free training, Hamiltonians

The novel hybrid architecture embodies a comprehensive approach in which CPUs, GPUs, and Quantum Processing Units (QPUs) function in concert to deliver scalable solutions across a spectrum of optimization tasks. Leveraging ORNL’s decades-long legacy in supercomputing and quantum research, the system is designed to bridge the existing divide between conventional data processing and the emerging quantum computational paradigm. This synergy stands to dramatically enhance algorithmic efficiency in real-world applications, particularly in electric grid management, where massive datasets and complex interdependencies demand unprecedented computational power.

EPB Quantum’s ambition is bold but necessary: to harness the quantum advantage in optimizing and managing the intricacies of a locally operated 600-square-mile energy distribution network. The system aims to minimize electrical losses, reduce voltage drops, and improve load balancing to maximize reliability and efficiency—objectives that classical methodologies have only partially realized. By analyzing trillions of operational data points collected from an extensive fiber-optic network and thousands of automated sensors, the hybrid platform seeks to identify new algorithms and operational strategies capable of powering a more resilient and sustainable electric grid.

The integration of lonQ’s Forte Enterprise Quantum Computer, slated for commissioning in early 2026, adds a vital quantum dimension to this initiative. lonQ, trading under NYSE: IONQ, is a trailblazer in quantum computing and networking, pushing the frontier with quantum machines targeting millions of qubits within the next decade. The partnership’s fusion of lonQ’s quantum hardware and expertise with EPB’s real-world infrastructure and ORNL’s research prowess creates an ecosystem where innovations move rapidly from theoretical research to field deployment.

Historically, EPB and ORNL’s collaboration has been instrumental in advancing energy grid security. Since 2016, the two laboratories, alongside Los Alamos National Laboratory and Qubitekk (now acquired by lonQ), have developed quantum-secure communication technologies designed to protect critical grid signals from cyber intrusions. This collaborative project, “QED: Quantum Ensured Defense of the Smart Electric Grid,” earned the prestigious R&D 100 Award in 2021, a testament to its impact in accelerating quantum networking technologies beyond laboratory conditions.

The practical marriage of classical and quantum computing resources at EPB’s facility offers a compelling preview of the hybrid supercomputing future. This model leverages classical GPUs’ well-established numerical processing capabilities alongside quantum circuits’ potential for exponential problem-solving speedups in areas such as combinatorial optimization and machine learning. The hybrid approach is critical because many real-world challenges—like power grid optimization—involve problem domains that can benefit from quantum algorithms while still requiring the robustness and vast data-handling capacity of classical computers.

David Wade, CEO of EPB, emphasized how this platform is more than a technological experiment. Rather, it represents a collaborative innovation ecosystem, enabling entrepreneurs, academic institutions, national laboratories, and industry leaders to co-develop quantum-enhanced applications that drive tangible societal benefits. By making a holistic quantum development environment accessible, EPB Quantum positions itself as a national hub for quantum technology commercialization and applied research.

From ORNL’s perspective, as underscored by Director Stephen Streiffer, the partnership exemplifies how federally funded national laboratories are pivotal in transitioning novel scientific principles into transformative technologies. ORNL’s strategy emphasizes hybrid high-performance computing as a core pillar, recognizing that future scientific and industrial breakthroughs will depend on integrating diverse computational architectures that include quantum accelerators. This vision aligns with the Department of Energy’s mission to advance clean energy, national security, and scientific discovery.

The project also reflects NVIDIA’s forward-looking commitment to hybrid quantum-classical infrastructures. Sam Stanwyck, NVIDIA’s group product manager for quantum computing, highlighted that this initiative is not merely theoretical—it’s an endeavor that actively shapes hybrid computing’s practical landscape. NVIDIA’s AI and GPU technologies complement quantum processors by enabling scalable simulations, quantum algorithm development, and data-intensive computations necessary for optimizing complex systems like smart grids.

LonQ’s CEO Niccolo de Masi added that while quantum hardware has advanced rapidly, its full potential will be realized only through the development of tailored algorithms and practical applications rooted in tangible infrastructure. The alliance’s access to EPB’s rich operational data accelerates this process by providing a testing ground far beyond the usual lab-based experimentation, thus shortening the innovation cycle for quantum-based solutions.

Looking forward, the outcomes of this joint hybrid computing effort are intended to be replicable across other energy networks in the United States and beyond. While EPB’s operations encompass a significant 600-square-mile service area, the methodologies, tools, and algorithms developed will likely scale to regional and national grids, helping utilities address efficiency, reliability, and security issues amid rising demand and climate-related stresses.

This comprehensive hybrid computing model ultimately serves as a harbinger for a new era in scientific computation, where the strengths of classical and quantum technologies are harnessed in tandem to conquer problems once considered intractable. As quantum systems mature and integrate seamlessly with their classical counterparts, the implications will ripple broadly—from energy and materials science to finance and national defense—ushering in a transformative paradigm of technological innovation.

For now, the EPB Quantum Center stands as a living laboratory embodying this transition. The coupled deployment of the NVIDIA DGX system and lonQ’s forthcoming quantum devices equip scientists and engineers with unmatched computational tools, accessible through cloud platforms and bolstered by deep partnership networks. This synergy promises to accelerate the quantum revolution from theoretical promise to practical reality, delivering breakthroughs with far-reaching societal impact.

Subject of Research: Power grid optimization through hybrid classical-quantum computing systems.

Article Title: EPB Quantum and ORNL Pioneer Hybrid Computing Platform to Revolutionize Power Grid Optimization

News Publication Date: 2025 (Exact date not specified)

Web References:

• EPB Quantum Hybrid Computing Announcement: https://epb.com/newsroom/press-releases/epb-quantum-adds-hybrid-computing-to-comprehensive-quantum-development-platform/
• Oak Ridge National Laboratory: https://www.ornl.gov/
• Department of Energy Office of Science: https://energy.gov/science
• lonQ Official Site: http://lonq.com
• EPB Official Site: http://epb.com
• EPB Quantum Portal: http://epbquantum.com

References:

• 2021 R&D 100 Award for “QED: Quantum Ensured Defense of the Smart Electric Grid”
• Newsweek’s 2025 Excellence Index 1000
• Forbes’ 2025 Most Successful Mid-Cap Companies list
• Built In’s 2025 100 Best Midsize Places to Work in Washington DC and Seattle

Keywords: Quantum computing, hybrid supercomputing, power grid optimization, quantum networking, classical-quantum computation, energy distribution, high-performance computing, quantum hardware, quantum algorithms, grid automation, quantum-secure communication