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DOE OSTI2026

Sapphire Substrate Trenching to Advance Superconducting High-Coherence Quantum Devices

Achieving longer coherence times in superconducting qubits is essential for advancing quantum information processing and enabling scalable quantum computing. This work presents an innovative fabrication strategy focused on sapphire trenching of the substrate of superconducting high-coherence quantum devices, developed by the SQMS Nanofabrication Taskforce in collaboration with the materials characterization team. In this talk, we present a method to efficiently and consistently trench into the sapphire substrate, without compromising the surface roughness or profile of the etched sapphire. In the literature, trenching in the substrate has been shown to decrease losses, unwanted coupling and thermal stress. Sapphire’s strong covalent bonds make it harder to etch compared to silicon, yet it is more favorable for high-coherence quantum devices due to its lower dielectric losses. This innovative technique opens new possibilities for superconducting qubit fabrication: we fabricated high coherence qubit chips on sapphire substrates with trenching, to demonstrate that it will help pushing towards higher coherence times. By integrating this work with ongoing junction process optimization, design innovation, materials characterization and exploration, we outline a clear pathway toward transmon coherence times reaching millisecond scales and beyond.

Garattoni, S. [Fermilab]↗
DOE OSTI2026

TLS characterization using multilevel decay of a fixed-frequency transmon

Transmon qubits are a cornerstone of superconducting quantum computing platforms, yet their coherence times often exhibit temporal fluctuations that degrade processor performance. These variations are commonly attributed to shifts in the resonance frequencies of individual two-level systems (TLSs) near the qubit transition. In this study, we monitor the lifetimes of multiple energy levels of a fixed-frequency transmon and examine their temporal correlations. Our measurements reveal that one or more TLSs—detuned by more than 100 MHz from the qubit transition—can still significantly influence coherence. The proposed method provides a powerful tool for TLS spectroscopy without the need to tune the transmon frequency, either via a flux-tunable inductor or AC-Stark shifts.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗
DOE OSTI2026

Sapphire Substrate Trenching to Advance Superconducting High-Coherence Quantum Devices

Achieving longer coherence times in superconducting qubits is essential for advancing quantum information processing and enabling scalable quantum computing. This work presents an innovative fabrication strategy focused on sapphire trenching of the substrate of superconducting high-coherence quantum devices, developed by the SQMS Nanofabrication Taskforce in collaboration with the materials characterization team. In this talk, we present a method to efficiently and consistently trench into the sapphire substrate, without compromising the surface roughness or profile of the etched sapphire. In the literature, trenching in the substrate has been shown to decrease losses, unwanted coupling and thermal stress. Sapphire’s strong covalent bonds make it harder to etch compared to silicon, yet it is more favorable for high-coherence quantum devices due to its lower dielectric losses. This innovative technique opens new possibilities for superconducting qubit fabrication: we fabricated high coherence qubit chips on sapphire substrates with trenching, to demonstrate that it will help pushing towards higher coherence times. By integrating this work with ongoing junction process optimization, design innovation, materials characterization and exploration, we outline a clear pathway toward transmon coherence times reaching millisecond scales and beyond.

Garattoni, S. [Fermilab]↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Error mitigation with Clifford quantum-circuit data

Achieving near-term quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data { X i noisy , X i exact } via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where X i noisy and X i exact are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. Here, we obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer

There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivity, and coherence times, a quantum circuit optimization is essential to make the best use of near-term quantum devices. We introduce a new circuit optimizer called AQCEL, which aims to remove redundant controlled operations from controlled gates, depending on initial states of the circuit. Especially, the AQCEL can remove unnecessary qubit controls from multi-controlled gates in polynomial computational resources, even when all the relevant qubits are entangled, by identifying zero-amplitude computational basis states using a quantum computer. As a benchmark, the AQCEL is deployed on a quantum algorithm designed to model final state radiation in high energy physics. For this benchmark, we have demonstrated that the AQCEL-optimized circuit can produce equivalent final states with much smaller number of gates. Moreover, when deploying AQCEL with a noisy intermediate scale quantum computer, it efficiently produces a quantum circuit that approximates the original circuit with high fidelity by truncating low-amplitude computational basis states below certain thresholds. Our technique is useful for a wide variety of quantum algorithms, opening up new possibilities to further simplify quantum circuits to be more effective for real devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Model predictive control for robust quantum state preparation

A critical engineering challenge in quantum technology is the accurate control of quantum dynamics. Model-based methods for optimal control have been shown to be highly effective when theory and experiment closely match. Consequently, realizing high-fidelity quantum processes with model-based control requires careful device characterization. In quantum processors based on cold atoms, the Hamiltonian can be well-characterized. For superconducting qubits operating at milli-Kelvin temperatures, the Hamiltonian is not as well-characterized. Unaccounted for physics (i.e., mode discrepancy), coherent disturbances, and increased noise compromise traditional model-based control. This work introduces model predictive control (MPC) for quantum control applications. MPC is a closed-loop optimization framework that (i) inherits a natural degree of disturbance rejection by incorporating measurement feedback, (ii) utilizes finite-horizon model-based optimizations to control complex multi-input, multi-output dynamical systems under state and input constraints, and (iii) is flexible enough to develop synergistically alongside other modern control strategies. We show how MPC can be used to generate practical optimized control sequences in representative examples of quantum state preparation. Specifically, we demonstrate for a qubit, a weakly-anharmonic qubit, and a system undergoing crosstalk, that MPC can realize successful model-based control even when the model is inadequate. These examples showcase why MPC is an important addition to the quantum engineering control suite.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Parallelization techniques for quantum simulation of fermionic systems

Mapping fermionic operators to qubit operators is an essential step for simulating fermionic systems on a quantum computer. We investigate how the choice of such a mapping interacts with the underlying qubit connectivity of the quantum processor to enable (or impede) parallelization of the resulting Hamiltonian-simulation algorithm. It is shown that this problem can be mapped to a path coloring problem on a graph constructed from the particular choice of encoding fermions onto qubits and the fermionic interactions onto paths. The basic version of this problem is called the weak coloring problem. Taking into account the fine-grained details of the mapping yields what is called the strong coloring problem, which leads to improved parallelization performance. A variety of illustrative analytical and numerical examples are presented to demonstrate the amount of improvement for both weak and strong coloring-based parallelizations. Our results are particularly important for implementation on near-term quantum processors where minimizing circuit depth is necessary for algorithmic feasibility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Single-shot Quantum Signal Processing Interferometry

Quantum systems of infinite dimension, such as bosonic oscillators, provide vast resources for quantum sensing. Yet, a general theory on how to manipulate such bosonic modes for sensing beyond parameter estimation is unknown. We present a general algorithmic framework, quantum signal processing interferometry (QSPI), for quantum sensing at the fundamental limits of quantum mechanics by generalizing Ramsey-type interferometry. Our QSPI sensing protocol relies on performing nonlinear polynomial transformations on the oscillator's quadrature operators by generalizing quantum signal processing (QSP) from qubits to hybrid qubit-oscillator systems. We use our QSPI sensing framework to make efficient binary decisions on a displacement channel in the single-shot limit. Theoretical analysis suggests the sensing accuracy, given a single-shot qubit measurement, scales inversely with the sensing time or circuit depth of the algorithm. We further concatenate a series of such binary decisions to perform parameter estimation in a bit-by-bit fashion. Numerical simulations are performed to support these statements. Our QSPI protocol offers a unified framework for quantum sensing using continuous-variable bosonic systems beyond parameter estimation and establishes a promising avenue toward efficient and scalable quantum control and quantum sensing schemes beyond the NISQ era.

Physics↗
DOE OSTI2024

HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware

In order to characterize and benchmark computational hardware, software, and algorithms, it is essential to have many problem instances on-hand. This is no less true for quantum computation, where a large collection of real-world problem instances would allow for benchmarking studies that in turn help to improve both algorithms and hardware designs. To this end, here we present a large dataset of qubit-based quantum Hamiltonians. The dataset, called HamLib (for Hamiltonian Library), is freely available online and contains problem sizes ranging from 2 to 1000 qubits. HamLib includes problem instances of the Heisenberg model, Fermi-Hubbard model, Bose-Hubbard model, molecular electronic structure, molecular vibrational structure, MaxCut, Max- k -SAT, Max- k -Cut, QMaxCut, and the traveling salesperson problem. The goals of this effort are (a) to save researchers time by eliminating the need to prepare problem instances and map them to qubit representations, (b) to allow for more thorough tests of new algorithms and hardware, and (c) to allow for reproducibility and standardization across research studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Tunable Geometries in Sparse Clifford Circuits

We investigate the emergence of different effective geometries in stochastic Clifford circuits with sparse coupling. By changing the probability distribution for choosing two-site gates as a function of distance, we generate sparse interactions that either decay or grow with distance as a function of a single tunable parameter. Tuning this parameter reveals three distinct regimes of geometry for the spreading of correlations and growth of entanglement in the system. We observe linear geometry for short-range interactions, treelike geometry on a sparse coupling graph for long-range interactions, and an intermediate fast scrambling regime at the crossover point between the linear and treelike geometries. This transition in geometry is revealed in calculations of the subsystem entanglement entropy and tripartite mutual information. We also study emergent lightcones that govern these effective geometries by teleporting a single qubit of information from an input qubit to an output qubit. These tools help to analyze distinct geometries arising in dynamics and correlation spreading in quantum many-body systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware

We implement a quantum generalization of a neural network on trapped-ion and IBM superconducting quantum computers to classify MNIST images, a common benchmark in computer vision. The network feedforward involves qubit rotations whose angles depend on the results of measurements in the previous layer. The network is trained via simulation, but inference is performed experimentally on quantum hardware. The classical-to-quantum correspondence is controlled by an interpolation parameter, $a$, which is zero in the classical limit. Increasing $a$ introduces quantum uncertainty into the measurements, which is shown to improve network performance at moderate values of the interpolation parameter. We then focus on particular images that fail to be classified by a classical neural network but are detected correctly in the quantum network. For such borderline cases, we observe strong deviations from the simulated behavior. We attribute this to physical noise, which causes the output to fluctuate between nearby minima of the classification energy landscape. Such strong sensitivity to physical noise is absent for clear images. We further benchmark physical noise by inserting additional single-qubit and two-qubit gate pairs into the neural network circuits. Our work provides a springboard toward more complex quantum neural networks on current devices: while the approach is rooted in standard classical machine learning, scaling up such networks may prove classically non-simulable and could offer a route to near-term quantum advantage.

FOS: Physical sciences↗
DOE OSTI2021

Excitation dynamics in inductively coupled fluxonium circuits

We propose a near-term quantum simulator based on the fluxonium qubits inductively coupled to form a chain. This system provides long coherence time, large anharmonicity, and strong coupling, making it suitable to study Ising spin models. At the half-flux quantum sweet spot, the system is described by the transverse field Ising model (TFIM). We evaluate the propagation of qubit excitations through the system. As disorder increases, the excitations become localized. A single qubit measurement using the circuit QED methods is sufficient to identify localization transition without introducing tunable couplers. We argue that inductively coupled fluxoniums provide opportunities to study localization and many-body effects in highly coherent quantum systems.

Barış Özgüler, A.↗
DOE OSTI2021

Designing Energy-Efficient Quantum Computers Through Prediction and Reduction of Cooling Requirements for Cryogenic Electronics

Quantum computing has been identified as a “wild card” by the International Energy Agency in predicting future global data center energy usage. This is primarily because both uncertainty in the extent to which quantum computing will be adopted, and uncertainty in the power consumption of individual quantum data centers. Unlike the classical counterparts, quantum computers need to be maintained at near absolute zero, requiring energy-intensive cryogenic cooling systems. Therefore, as quantum computers scale up from existing 50 qubit technology demonstrations to the 10,000 to 100,000 qubit systems that will be able to solve complex problems, the energy consumption of both the electronics and the required cooling systems will also increase. To predict this scaling, this work analyzes the energy requirements for both computation and cooling of quantum hardware. We show that the energy requirements for cooling of quantum computers is determined by several computing system parameters, including the number and type of physical qubits, the operating temperature, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. The energy requirements can then be found based on thermal system parameters such as cooling efficiency and cryostat heat transfer. Analysis of these parameters shows that the energy required for cooling is significantly larger than that required for computation, a reversal from energy usage patterns seen in conventional computing. The results and discussions provide a road-map for creating energy efficient quantum computers through the selection of computer architectures and cryogenic system configurations that minimize cooling requirements.

energy efficiency↗
DOE OSTI2021

Simulating Large PEPs Tensor Networks on Small Quantum Devices

We systematically map low bond dimension PEPs tensor networks to quantum circuits. By measuring and reusing qubits, we are in principle able to simulate an N×M square lattice PEPs network (for arbitrary M) of bond dimension 2 using N+ 2 qubits. We then seek to determine if this mapping can yield useful values for long-range loop observables in the presence of quantum device noise in the topological Wen plaquette model by mapping a 3×3 PEPs tensor network to a 5-qubit quantum circuit and executing it on the Honeywell System Model H1-1 trapped-ion device. We find that, for this system size, the noisy observable values are indeed sufficient for diagnosing topological vs. trivial order as the Wen model is perturbed by a magnetic field term in the Hamiltonian. We provide an overview of the experimental procedure and its results. We then explain in greater detail our method for mapping 2D tensor networks to quantum circuits and its scaling properties. This serves as a proof-of-concept of the utilty of the measure-and-reuse approach for simulating large 2D quantum systems on small quantum devices.

MacCormack, Ian↗
DOE OSTI2023

Trapped-ion entangling gates with bichromatic pair of microwave fields and magnetic field gradient

A trapped-ion quantum logic gate and a method of operating the trapped-ion quantum logic gate are provided. The trapped-ion quantum logic gate includes at least one ion having two internal states and forming a qubit having a qubit transition frequency ω 0 0, a magnetic field gradient, and two microwave fields. Each of the two microwave fields has a respective frequency that is detuned from the qubit transition frequency ω 0 by frequency difference δ. The at least one ion has a Rabi frequency Ω μ due to the two microwave fields and a Rabi frequency Ω g due to the magnetic field gradient. The method includes applying the magnetic field gradient and the two microwave fields to the at least one ion such that a quantity Ω g /δ is in a range between zero and 5×10 −2 .

Sutherland, Robert Tyler↗
DOE OSTI2023

Accelerated pattern matching method on a quantum computing system

A method of determining a pattern in a sequence of bits using a quantum computing system includes setting a first register of a quantum processor in a superposition of a plurality of string index states, encoding a bit string in a second register of the quantum processor, encoding a bit pattern in a third register of the quantum processor, circularly shifting qubits of the second register conditioned on the first register, amplifying an amplitude of a state combined with the first register in which the circularly shifted qubits of the second register matches qubits of the third register, measuring an amplitude of the first register and determining a string index state of the plurality of string index states associated with the amplified state, and outputting, by use of a classical computer, a string index associated with the first register in the measured state.

NIROULA, Pradeep↗
DOE OSTI2024

Overview of Quantum Sensing Materials and Techniques for Energy Sector Applications

The energy sector has become increasingly dependent upon highly sensitive sensing devices for a wide range of applications. Variables such as temperature, pH, electromagnetic fields, ions, and pressure must be measured with high precision, often in harsh conditions (e.g. highly corrosive environments due to high temperature, pressure and humidity). These sensors are deployed in infrastructure such as transformers, pipelines, mines, nuclear power plants, and other areas to ensure safe operating conditions and uninterrupted, optimized service. Moreover, new opportunities for sensors have emerged due to the expansion of smart grids/meters, driverless vehicles, and the discovery of new oil, gas, and critical mineral deposits. The continued maturation of quantum sensing technologies offers exciting opportunities for quantum-enhanced measurements within the energy sector that may provide significant improvements in sensitivity beyond the classical limit. Here, an overview of established and emerging quantum materials for sensing applications will be provided, from trapped ions to color centers in diamond and silicon carbide. Associated quantum sensing methods for these materials will be discussed, with an emphasis on platforms that are nearing commercial viability. Specific application opportunities within the energy sector for quantum sensors will then be analyzed, including oil/gas discovery, greenhouse gas emission monitoring, pH and ion sensing, current measurement in electric vehicle batteries, and quantum-enhanced spectroscopy. Remaining barriers, such as quantum sensor platform miniaturization and ruggedization, will also be analyzed. A specific project at the National Energy Technology Laboratory involving the functionalization of qubits using metal-organic frameworks for enhanced quantum sensing will also be highlighted. Here, nitrogen vacancy centers (NV) in nanodiamonds, a commercially available qubit with long coherence times and utilizable quantum properties at room temperature, are encapsulated in a controlled way using the metal-organic framework ZIF-8. The ZIF-8 provides a well-defined, porous scaffold that can be controlled to promote the selective uptake of specific target analytes, such as gas molecules or ions. The quantum sensing performance of this composite material is either preserved or enhanced following ZIF-8 encapsulation; the optically detected magnetic resonance response of the NV nanodiamonds with and without the ZIF-8 coating are identical, while the ZIF-8 coating increases the longitudinal spin relaxation lifetime (T1) of the NV centers, an important parameter for spin relaxometry-based quantum sensing experiments. Taken together, these results demonstrate the importance of qubit functionalization as a crucial step for rationally designing high performance quantum sensors.

Crawford, Scott↗