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DOE OSTI2023

Quantum simulations in effective model spaces: Hamiltonian-learning variational quantum eigensolver using digital quantum computers and application to the Lipkin-Meshkov-Glick model

Quantum simulations offer the potential to predict the structure and dynamics of nuclear many-body systems that are beyond the capabilities of classical computing. Generally, preparing the ground state of strongly-interacting many-body systems relevant to nuclear physics is however inefficient, even using ideal quantum computers. In addition, currently available NISQ-era quantum devices possess modest numbers of qubits, limiting the size of quantum many-body systems that can be simulated. In this context, a reformulation of the quantum many-body problems using truncated model spaces and Hamiltonians is desirable to make them more amenable to near-term quantum computers. The importance of symmetries in low-energy theories, including effective field theories (EFTs), lattice quantum chromodynamics (QCD), and effective model spaces for nuclear systems, in particular their interplay with the reduction of active Hilbert spaces, is well known. Lesser known is the fact that the non-commutivity of some symmetries and truncations of the model space can be profitably combined with variational calculations to rearrange the entanglement into localized structures and enable more efficient simulations. Here, the goal of the present study is to explore and utilize the non-commutivity of symmetries and model-space truncations of quantum many-body systems important to nuclear physics, particularly in combination with variational algorithms for quantum simulations and effective Hamiltonian learning. We introduce an iterative hybrid classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wavefunction. Quantum simulations, using classical computers and IBM's superconducting-qubit quantum computers, are performed to demonstrate the HL-VQE algorithm, in the context of the Lipkin-Meshkov-Glick (LMG) model of interacting fermions, where the Hamiltonian transformation corresponds to an orbital rotation. We use a mapping where the number of qubits scales with the $\log$ of the size of the effective model space, rather than the particle number. HL-VQE is found to provide an exponential improvement in LMG-model calculations of the ground-state energy and wavefunction, compared to naive truncations without Hamiltonian learning, throughout a significant fraction of the Hilbert space. In the context of EFT, this corresponds to counterterms scaling exponentially with the cut-off as opposed to power law. Implementations on IBM's QExperience quantum computers and simulators for 1- and 2-qubit effective model spaces are shown to provide accurate and precise results, reproducing classical predictions. For a range of parameters defining the LMG model, the HL-VQE algorithm is found to have better scaling of quantum resources requirements than previously explored algorithms. In particular, the HL-VQE scales efficiently over a large fraction of the model space, in contrast to VQE alone. This work constitutes a step in the development of entanglement-driven quantum algorithms for descriptions of nuclear many-body systems. This, in part, leverages the potential of noisy intermediate-scale quantum (NISQ) devices. The exponential scaling of counterterms observed in this study suggests the possibility of more general applicability to other non-perturbative EFTs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Quantum machine learning with differential privacy

Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Low-loss millimeter-wave resonators with an improved coupling structure

Millimeter-wave superconducting resonators are a useful tool for studying quantum device coherence in a new frequency domain. However, improving resonators is difficult without a robust and reliable method for coupling millimeter-wave signals to 2D structures. We develop and characterize a tapered transition structure coupling a rectangular waveguide to a planar slotline waveguide with better than 0.5 dB efficiency over 14 GHz, and use it to measure ground-shielded resonators in the W band (75–110 GHz). Having decoupled the resonators from radiative losses, we consistently achieve single-photon quality factors above 10 5 , with a two-level-system loss limit above 10 6 , and verify the effectiveness of oxide removal treatments to reduce loss. These values are 4–5 times higher than those previously reported in the W band, and much closer to typical planar microwave resonators. The improved losses demonstrated by these on-chip millimeter-wave devices shed new light on quantum decoherence in a different frequency regime, offer increased selectivity for high-frequency detectors, and enables new possibilities for hybrid quantum experiments integrating millimeter-wave frequencies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Quantum algorithm for polaritonic chemistry based on an exact ansatz

Abstract Cavity-modified chemistry uses strong light-matter interactions to modify the electronic properties of molecules in order to enable new physical phenomena such as novel reaction pathways. As cavity chemistry often involves critical regions where configurations become nearly degenerate, the ability to treat multireference problems is crucial to understanding polaritonic systems. In this Letter, we show through the use of a unitary ansatz derived from the anti-Hermitian contracted Schrödinger equation that cavity-modified systems with strong correlation, such as the deformation of rectangular H 4 coupled to a cavity mode, can be solved efficiently and accurately on a quantum device. In contrast, while our quantum algorithm can be made formally exact, classical-computing methods as well as other quantum-computing algorithms often yield answers that are both quantitatively and qualitatively incorrect. Additionally, we demonstrate the current feasibility of the algorithm on near intermediate-scale quantum hardware by computing the dissociation curve of H 2 strongly coupled to a bosonic bath.

Warren, Samuel (ORCID:0000000157134454)↗

DOE OSTI2025

Accuracy Guarantees and Quantum Advantage in Analog Open Quantum Simulation with and without Noise

Many-body open quantum systems, described by Lindbladian master equations, are a rich class of physical models that display complex equilibrium and out-of-equilibrium phenomena which remain to be understood. In this paper, we theoretically analyze noisy analog quantum simulation of geometrically local open quantum systems and provide evidence that this problem both is hard to simulate on classical computers and could be approximately solved on near-term quantum devices. First, given a noiseless quantum simulator, we show that the dynamics of local observables and the fixed-point expectation values of rapidly mixing local observables in geometrically local Lindbladians can be obtained to a precision of ϵ in time that is poly ( ϵ − 1 ) and uniform in system size. Furthermore, we establish that the quantum simulator would provide a superpolynomial advantage, in run-time scaling with respect to the target precision and either the evolution time (when simulating dynamics) or the Lindbladian’s decay rate (when simulating fixed points), over any classical algorithm for these problems, assuming BQP ≠ BPP . We then consider the presence of noise in the quantum simulator in the form of additional geometrically local Lindbladian terms. We show that the simulation tasks considered in this paper are stable to errors; i.e., they can be solved to a noise-limited, but system-size independent, precision. Finally, we establish that, assuming BQP ≠ BPP , there are stable geometrically local Lindbladian simulation problems such that, as the noise rate on the simulator is reduced, classical algorithms must take time superpolynomially longer in the inverse noise rate to attain the same precision as the analog quantum simulator. Published by the American Physical Society 2025

Kashyap, Vikram (ORCID:0000000208195207)↗

DOE OSTI2026

A New Route Toward Atomically Flat and Defect-Free Ge/SiGe Planar Heterostructures

Germanium-based planar heterostructures are emerging as versatile platforms for realizing quantum devices. In particular, planar Ge/SiGe quantum wells (QWs) host hole states with exceptionally high mobility and strong, electrically tunable spin–orbit interactions, enabling full electrical control of quantum information. A key requirement for these systems is the growth of microscopic, defect-free, atomically flat Ge QW heterostructures on relaxed or reverse-graded SiGe buffer layers, as well as on commercial Ge substrates. While several physical deposition techniques have demonstrated high-quality planar Ge/SiGe QWs, a major challenge remains: minimizing defect density typically requires high growth temperatures, which are incompatible with standard CMOS process flows. Here, we present a convenient low-temperature process for realizing high-quality planar SiGe/Ge heterostructures using a combination of thermal and electron-beam evaporation. We systematically map the effects of ex-situ and in-situ substrate preparation protocols, growth temperature, and post-deposition annealing conditions, and correlate these parameters with surface roughness and defect density. We find that in-situ oxide desorption conditions and post-annealing parameters have the most pronounced impact on improving surface quality. Under optimized conditions, we achieve atomically smooth surfaces with root-mean-square roughness σrms ≤ 10 Å and negligible defect density. Interestingly, thermally evaporated Ge layers exhibit oriented triangular crystallites that elongate upon post-annealing in the presence of high Ge vapor pressure. These results demonstrate that this simple, low-temperature deposition approach is a viable and effective route for achieving high-quality Ge QW heterostructures, with strong potential for scalable quantum computing and sensing applications.

Tripathi, Malvika [Fermilab] (ORCID:00000001989251↗

DOE OSTI2026

A New Route Toward Atomically Flat and Defect-Free Ge/SiGe Planar Heterostructures

Tripathi, Malvika [Fermilab] (ORCID:00000001989251↗

DOE OSTI2024

Solving the Hele–Shaw flow using the Harrow–Hassidim–Lloyd algorithm on superconducting devices: A study of efficiency and challenges

The development of quantum processors for practical fluid flow problems is a promising yet distant goal. Recent advances in quantum linear solvers have highlighted their potential for classical fluid dynamics. In this study, we evaluate the Harrow–Hassidim–Lloyd (HHL) quantum linear systems algorithm (QLSA) for solving the idealized Hele–Shaw flow. Our focus is on the accuracy and computational cost of the HHL solver, which we find to be sensitive to the condition number, scaling exponentially with problem size. This emphasizes the need for preconditioning to enhance the practical use of QLSAs in fluid flow applications. Moreover, we perform shots-based simulations on quantum simulators and test the HHL solver on superconducting quantum devices, where noise, large circuit depths, and gate errors limit performance. Error suppression and mitigation techniques improve accuracy, suggesting that such fluid flow problems can benchmark noise mitigation efforts. Finally, our findings provide a foundation for future, more complex application of QLSAs in fluid flow simulations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Leakage Reduces Device Coherence Demands for Pulse-Level Molecular Simulations

Quantum simulation on noisy intermediate-scale quantum devices is severely limited by short qubit coherence times. A variational pulse-shaping algorithm known as ctrl-VQE was recently proposed to address this issue by eliminating the need for parameterized quantum circuits, which lead to long state preparation times. Here, in this study, we find the fastest possible pulses that prepare target molecular wave functions for a given device Hamiltonian describing coupled transmon qubits using simulations. We find that the resulting time-optimal pulses develop a bang-bang form consistent with Pontryagin’s maximum principle. We further investigate how the minimal state preparation time is impacted by the number of energy levels active in the transmon simulations. We find that leakage outside the computational subspace (something that is usually considered problematic) speeds up the state preparation, further reducing device coherence-time demands. Our analysis reveals that this speedup is due to both an enlarged solution space of target wave functions and the appearance of additional channels connecting initial and target states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Defect‐Free Nanowelding of Bilayer SnSe Nanoplates

Nanowelding is a bottom‐up technique to create custom‐designed nanostructures and devices beyond the precision of lithographic methods. Here, a new technique is reported based on anisotropic lubricity at the van der Waals interface between monolayer and bilayer SnSe nanoplates and a graphene substrate to achieve precise control of the crystal orientation and the interface during the welding process. As‐grown SnSe monolayer and bilayer nanoplates are commensurate with graphene's armchair direction but lack commensuration along graphene's zigzag direction, resulting in a reduced friction along that direction and a rail‐like, 1D movement that permits joining nanoplates with high precision. This way, molecular beam epitaxially grown SnSe nanoplates of lateral sizes 30–100 nm are manipulated by the tip of a scanning tunneling microscope at room temperature. In situ annealing is applied afterward to weld contacting nanoplates without atomic defects at the interface. This technique can be generalized to any van der Waals interfaces with anisotropic lubricity and is highly promising for the construction of complex quantum devices, such as field effect transistors, quantum interference devices, lateral tunneling junctions, and solid‐state qubits.

2D ferroelectrics↗

DOE OSTI2025

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Hybrid Quantum-Classical Neural Networks

Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become increasingly pressing as conventional computers are soon expected to approach the physical limitations that will slow their performance improvements in the near future. For these reasons, scientists have begun to explore alternative computing platforms, like quantum computers, for training neural networks. In recent years, variational quantum circuits have emerged as one of the most successful approaches to quantum deep learning on noisy intermediate scale quantum devices. We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit. We empirically analyze the performance of this hybrid neural network on a series of binary classification data sets using a simulated IBM universal quantum computer and a state-of-the-art IBM universal quantum computer. On the simulated hardware, we observe that the hybrid neural network achieves around 10% higher classification accuracy and 20% better minimization of the cost function than an individual variational quantum circuit. On the quantum hardware, we observe that each model only performs well when the qubit and gate count is sufficiently small.

Arthur, Davis↗

DOE OSTI2024

Convergence QL: NSF/DOE Quantum Science Summer School: QS3 Final Report

Convergence QL: NSF/DOE Quantum Science Summer School (QS3) was a 2-week summer program held in 2017, 2018, 2019, and 2022. The program was aimed at graduate students and postdoctoral researchers working on a wide range of topics related to quantum science and quantum engineering. Expert leaders in this field served as lectures for the school. The school program included lectures, active learning, project activities, poster presentations, and industry panel discussions. The school covered four topics: Fundamentals and Applications of Quantum Computing (2017), Fundamentals and Applications of Quantum Materials (2018), Fundamentals and Applications of Quantum Devices (2019), and Modern Synthesis of Quantum Materials (2022). In terms of intellectual merit, the school brought together students from different areas of research related to quantum science to educate them on a broad range of related topics, providing a unique crucible for the development of educational content and learning opportunities in a cross-disciplinary fashion in the context of quantum science and quantum engineering. In terms of broader impacts, this may serve to create a workforce which would accelerate research in quantum science, quantum engineering, and the development of new quantum technologies beneficial to society. Additionally, this may add to the training of a cadre of scientists and engineers to serve as future leaders in this research field.

42 ENGINEERING↗

NASA NTRSDate not supplied

Dynamical Decoupling for Measuring and Suppressing Crosstalk

Dynamical decoupling (DD) is a noise-mitigating strategy in which sequences of pulses are applied to single qubits to average out their interaction with the environment. DD has been extensively studied and demonstrated for suppressing single-qubit decoherence and can be tailored for different noise spectrum. We report another important adaptation of DD where crosstalk between qubits are suppressed. We demonstrate the efficiency of this procedure on quantum circuits on superconducting transmon-based quantum devices. We designed a family of syncopated DD sequences that effectively suppress ZZ coupling between qubit pairs, which is the dominating crosstalk form on the device. We insert DD to a quantum circuit whenever single qubits are idle (often during two-qubits gates on other qubits). While standard periodic DD suppress crosstalk between these qubits and their neighbors, the syncopated DD further decouples crosstalk between these qubits. We further designed short sequences that maximize the application of DD without adding time to the quantum circuit execution. Such DD sequences yield significant improvement of the performance of the algorithm on the hardware. The performance is further boosted by combining DD with another mitigation strategy, randomized compilation. Our work demonstrated that syncopated DD is effective and practical way to suppress crosstalk in quantum circuits and serves as a great probe to characterize the crosstalk and inform hardware design.

Quantum Computing↗

DOE OSTI2020

On-Chip Single-Layer Integration of Diamond Spins with Microwave and Plasmonic Channels

On-chip scalable integration represents a major challenge for practical quantum devices. One particular challenge is to implement on-chip optical readout of spins in diamond. This readout requires simultaneous application of optical and microwave fields along with the efficient collection of fluorescence. These requirements are typically met using bulk optical components and dedicated microwave lines. Here, we experimentally demonstrate an on-chip integrated interface for diamond nitrogen vacancy (NV) spin-state preparation and readout, implemented in a planar single layer with only one patterning step. A nanodiamond with NV centers is positioned inside a v-groove waveguide milled in a thick gold film. The gold film carries the microwave control signal, while the v-groove acts as a fluorescence collector and, potentially, as an integrated pump filter. Hence, the device dimensions and fabrication complexity are substantially reduced. Our approach fosters further development of ultracompact nanoscale quantum sensors and quantum information processing devices on a monolithic platform.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Simulation of open quantum systems via low-depth convex unitary evolutions

Simulating physical systems on quantum devices is one of the most promising applications of quantum technology. Current quantum approaches to simulating open quantum systems are still practically challenging on NISQ-era devices, because they typically require ancilla qubits and extensive controlled sequences. In this work, we propose a hybrid quantum-classical approach for simulating a class of open system dynamics called random-unitary channels. These channels naturally decompose into a series of convex unitary evolutions, which can then be efficiently sampled and run as independent circuits. The method does not require deep ancilla frameworks and thus can be implemented with lower noise costs. We implement simulations of open quantum systems up to dozens of qubits and with large channel ranks. Published by the American Physical Society 2024

Peetz, Joseph (ORCID:0000000274458028)↗

DOE OSTI2021

Nearly Quantum-Limited Josephson-Junction Frequency-Comb Synthesizer

While coherently driven Kerr microcavities have rapidly matured as a platform for frequency-comb formation, such microresonators generally possess weak Kerr coefficients; consequently, triggering comb generation requires millions of photons to be circulating inside the cavity. This suppresses the role of quantum fluctuations in the dynamics of the comb. In this paper, we realize a minimal version of coherently driven Kerr-mediated microwave-frequency combs in the circuit quantum electrodynamics (cQED) architecture, where the fluctuations of the quantum vacuum are the primary limitation on comb coherence. We achieve a comb phase coherence of up to 35 μs, approaching the theoretical device quantum limit of 55 μs and vastly longer than the inherent lifetimes of the modes, of 13 ns. The ability within cQED to engineer stronger nonlinearities than optical microresonators, together with operation at cryogenic temperatures, and the excellent agreement of comb dynamics with quantum theory indicates a promising platform for the study of complex dynamics of quantum nonlinear systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Simulating large one-dimensional neutral-atom quantum systems

While abstract models of quantum computation assume a closed system of two-level states, practical quantum devices inevitably couple to the environment in some way, creating sources of noise. Understanding the tolerance to noise of specific quantum algorithms run on specific devices is important for determining the feasibility of quantum computing in the current noisy intermediate-scale quantum era. Of particular interest is understanding the noise sensitivity of these devices as more qubits are added to the system. Classical simulations are a useful tool to understand the effects of this noise, but direct classical simulations of open quantum systems are burdened by an exponentially growing cost in the number of qubits and a large local Hilbert space dimension. For onedimensional, shallow circuits, using tensor networks can replace this exponential cost with a linear one and simulate far wider systems than what would normally be available. In this paper, we describe a tensor network simulation of a neutral atom quantum system under the presence of noise, while introducing a purity-preserving truncation technique that compromises between the simplicity of the matrix product state and the positivity of the matrix product density operator. We apply this simulation to a near-optimized iteration of the quantum approximate optimization algorithm on a transverse field Ising model in order to investigate the influence of large system sizes on the performance of the algorithm. We find that while circuits with a large number of qubits fail more often under noise that depletes the qubit population, their outputs on a successful measurement are just as robust under Rydberg atom dissipation or qubit dephasing as smaller systems. However, such circuits might not perform as well under coherent multiqubit errors such as Rydberg atom crosstalk. We also find that the optimized parameters are especially robust to noise, suggesting that a noisier quantum system can be used to find the optimal parameters before switching to a cleaner system for measurements of observables.

Allen, James↗