Research
QLab supports interdisciplinary research that brings quantum computing hardware together with domain expertise. Across science and engineering, researchers use QLab systems to develop and test algorithms, investigate quantum matter, build networking and control technologies, and measure the capabilities of emerging quantum processors.
- Quantum Machine Learning
Quantum machine learning combines quantum circuits with classical optimization to explore new approaches to classification, materials discovery, and quantum-state reconstruction. QLab researchers test these methods on real processors, where noise, limited data, and hardware constraints reveal both the promise and the practical limits of near-term quantum learning.
Selected recent papers
Jobilal et al., Quantum graph neural networks for jet tagging on quantum hardware, arXiv:2609.04367 (2026).
Adams et al., Quantum kernel machine learning for autonomous materials science, arXiv:2601.11775 (2026).
Lakhdar-Hamina et al., Benchmarking a tunable quantum neural network on trapped-Ion and superconducting hardware, arXiv:2507.21222 (2025). - Quantum Simulation of Matter and Fields
Quantum computers can emulate strongly correlated quantum many-body systems whose state spaces overwhelm classical computation. For the investigation of condensed matter, particle, and nuclear physics, QLab research combines compact encodings, tensor-network ideas, and tailored measurement protocols to study phase transitions, entanglement, gauge dynamics, and even toy models of emergent spacetime.
Selected recent papers
Biswas et al., Observation of gravity-like signatures in holographic codes on a quantum computer, arXiv:2607.12047 (2026).
Miao et al., Probing entanglement scaling across a quantum phase transition on a quantum computer, Nature Communications 17, 9110 (2026).
Than et al., The phase diagram of quantum chromodynamics in one dimension on a quantum computer, Nature Communications 16, 10288 (2025).
Than et al., Observation of quantum-field-theory dynamics on a spin-phonon quantum computer, arXiv:2509.11477 (2025).
Davoudi et al., Quantum computation of hadron scattering in a lattice gauge theory, arXiv:2505.20408 (2025). - Quantum Algorithms and Error Correction
Quantum algorithms seek computational advantages by matching a problem's structure to operations that quantum hardware can perform more efficiently than any classical computer. In cases like Shor's algorithm for prime factorization, this can lead to gains that grow exponentially in the problem size. This generally requires error correction which protects the information needed for long computations. QLab research develops algorithms, reduces resource demands, and studies thresholds and nonclassical resources that determine when quantum computations are useful.
Selected recent papers
Yang et al., Towards end-to-end quantum estimation of non-Hermitian pseudospectra, arXiv:2603.16214 (2026).
Li et al., Resource-efficient quantum simulation of transport phenomena via Hamiltonian embedding, arXiv:2602.03099 (2026).
Niroula et al., Phase transition in magic with random quantum circuits, Nature Physics 20, 1786 (2024). - Quantum Networking and Communications
Quantum networks distribute entanglement between remote quantum memories, processors, and sensors using photons. Our research focuses on robust photonic interfaces and compatibility with deployed telecommunications fiber, opening paths toward modular quantum computers and quantum-enhanced network security.
Selected recent papers
Wu et al., Trapped ion quantum networking and telecommunications coexisting on one fiber, arXiv:2609.06387 (2026).
Kao et al., Quantum-enhanced physical-layer threat detection in metropolitan-scale fiber networks, arXiv:2607.10799 (2026).
Ferrari et al., Robust ion-photon entanglement via polarization-to-time-bin conversion, arXiv:2607.07805 (2026). - Quantum Hardware Control and Enabling Technologies
Useful quantum computation depends on more than qubit count: gates must be fast and calibratable, control systems must be safe, and quantum information must move reliably between hardware components. QLab researchers co-design experimental methods and software interfaces that improve performance today and support more scalable architectures.
Selected recent papers
Wang et al., A hardware-safety-gated system for LLM-written native ARTIQ control code on a trapped-ion platform, arXiv:2606.27231 (2026).
Diaz et al., Arbitrary parallel entangling gates with independent calibration on a trapped ion quantum computer, arXiv:2604.25993 (2026). - Device Characterization and Benchmarking
QLab provides access to quantum computers based on different technologies including trapped ions, superconducting qubits, and netural atoms in optical tweezer arrays. To use them efficiently and guide hardware developments, QLab research analyzes the application-dependent performance of these devices and implements efficient error mitigation schemes.
Selected recent papers
Than et al., Nonlocal games as cross-platform quantum benchmarks: Exceeding unconditional classical bounds on trapped-ion processors, arXiv:2603.18323 (2026).
Doucet et al., From compatibility of measurements to exploring Quantum Darwinism on NISQ, arXiv:2601.05350 (2026).
Proctor et al., Featuremetric benchmarking: Quantum computer benchmarks based on circuit features, arXiv:2504.12575 (2025).
Recent Projects
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A collaboration between QLab and the Duke Quantum Center has used a trapped-ion quantum computer to study a continuous quantum phase transition in an effectively infinite system. Combining multiscale entanglement renormalization with a new holographic tomography method, the team demonstrated for the first time on a digital quantum computer the predicted logarithmic scaling of subsystem entanglement entropy at criticality. Published in Nature Communications, the results establish a promising approach for investigating strongly correlated quantum matter with compact, noise-resilient quantum circuits. -
A research team led by QLab Director Norbert Linke has demonstrated a novel technique to protect fragile photon-ion entangled states against photon decoherence in optical fiber networks. By passing ion-emitted photons through an asymmetric Mach-Zehnder interferometer, the team converted polarization-encoded qubits into robust time-bin qubits in flight, preserving high-fidelity entanglement with the emitting Strontium ion even under severe depolarizing noise. This breakthrough provides a vital building block for transmitting quantum information across scalable, long-distance quantum networks. -
Researchers from QLab and partner institutions have successfully used a trapped-ion quantum computer to simulate toy models of quantum gravity in anti-de Sitter space. By implementing the HaPPY holographic error-correcting code and injecting it with non-stabilizer "magic," the team observed emergent, gravity-like spacetime signatures. The study demonstrates the connection of boundary entanglement and bulk properties in accordance with the holographic principle and the Faulkner-Lewkowycz-Maldacena formula. -
A multi-institutional research team including QLab Fellow Saikat Guha has demonstrated a quantum-enhanced physical-layer threat detection system over an operational fiber network connecting QLab's Campus-Drive site to UMD's main campus. By embedding bright squeezed light directly into classical optical data streams, the system sensitively detects unauthorized physical tapping without requiring complex modifications to upper classical network layers. The real-world field deployment validated continuous, stable threat monitoring over a 5-kilometer fiber loop without interrupting standard internet data throughput. -
A collaboration between QLab and the Duke Quantum Center has introduced a hardware-safety-gated control system that allows large language models (LLMs) to safely write and execute native experimental code on trapped-ion quantum platforms. It utilizes a model context protocol (MCP) server and a safety filter that blocks any command from reaching the hardware without an authorization token, which must be validated either by a human operator or an isolated simulation. By successfully testing the system on live quantum hardware, the team demonstrated that LLM agents can develop calibration experiments while protecting sensitive laboratory apparatuses from unchecked code. -
The 2026 Meeting of the National Quantum Laboratory (QLab) highlighted the collaborative drive of the QLab community in pushing the boundaries of near-term quantum technologies. The event featured 13 dynamic talks by early-career scientists on projects at the frontier of practical quantum computation and simulation, while QLab Director Norbert Linke led discussions on expanded hardware access and spreading knowledge about quantum information technology through outreach activities. -
Researchers at QLab and collaborating institutions have developed a breakthrough method for executing entangling gates on trapped-ion quantum computers in parallel. The new framework utilizes a core set of independently calibratable pulses to create any possible gate pattern, eliminating the need for computationally expensive, bespoke pulse synthesis. This innovation dramatically reduces execution times while maintaining high fidelities, and removes a major bottleneck in classical control. -
A new study demonstrates a "quantum cheat code", using entangled states on trapped-ion processors to solve classically impossible graph coloring puzzles. By exceeding the win rates allowed by classical probability, the team lead by Carlos Ortiz Marrero and QLab's Norbert Linke effectively colored a 14-vertex graph using fewer colors than is classically required. Such nonlocal games can serve as holistic benchmarks for comparing and verifying the performance of different quantum computing architectures. -
QLab Fellow Xiaodi Wu and collaborators have introduced a new end-to-end quantum protocol for estimating the pseudospectra of non-Hermitian many-body systems, a task proven to be QMA-complete. The research features two major algorithmic innovations - Quantum Singular-value Gaussian-filtered Search and Algorithmic Lindbladian Protocols. The approach is demonstrated experimentally on IonQ’s Forte trapped-ion hardware. -
UMD Professors Katrina Groth and Mohammad Modarres have been awarded EPRI-QLab seed grants. Leveraging a generous gift by the Electric Power Research Institute (EPRI) and QLab resources, these grants will fund research exploring the application of quantum computing technologies to probabilistic risk assessment. Ultimately, this work seeks to improve the speed and accuracy of safety and reliability assessments for critical energy technologies and nuclear infrastructure. -
Quantum chromodynamics (QCD) is the fundamental theory for how quarks and gluons interact through the strong nuclear force. In their recent paper "The phase diagram of quantum chromodynamics in one dimension on a quantum computer", QLab researchers Alaina Green and Norbert Linke, their team, and collaborators from the University of Waterloo and York University show a fascinating path for the simulation of QCD at finite temperatures on quantum computers. The study has been published in Nature Communications.