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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Dynamic Cooling on Contemporary Quantum Computers

We study the problem of dynamic cooling whereby a target qubit is cooled at the expense of heating up N − 1 further identical qubits by means of a global unitary operation. A standard back-of-the-envelope high-temperature estimate establishes that the target qubit temperature can be dynamically cooled by at most a factor of 1 / N . Here we provide the exact expression for the minimum temperature to which the target qubit can be cooled and reveal that there is a crossover from the high initial temperature regime, where the scaling is 1 / N , to a low initial temperature regime, where a much faster scaling of 1 / N occurs. This slow, 1 / N scaling, which was relevant for early high-temperature NMR quantum computers, is the reason dynamic cooling was dismissed as ineffectual around 20 years ago; the fact that current low-temperature quantum computers fall in the fast, 1 / N scaling regime, reinstates the appeal of dynamic cooling today. We further show that the associated work cost of cooling is exponentially more advantageous in the low-temperature regime. We discuss the implementation of dynamic cooling in terms of quantum circuits and examine the effects of hardware noise. We successfully demonstrate dynamic cooling in a three-qubit system on a real quantum processor. Since the circuit size grows quickly with N , scaling dynamic cooling to larger systems on noisy devices poses a challenge. We therefore propose a suboptimal cooling algorithm, whereby relinquishing a small amount of cooling capability results in a drastically reduced circuit complexity, greatly facilitating the implementation of dynamic cooling on near-future quantum computers. Published by the American Physical Society2024

Physics↗
DOE OSTI2025

Quantum Approximate Optimization Algorithm on Different Qubit Systems

Solving optimization problems is critical across many research domains, but the high dimensionality of parameter spaces often poses significant challenges. The Quantum Approximate Optimization Algorithm (QAOA) has emerged as a promising approach for accelerating optimization in the Noisy Intermediate-Scale Quantum (NISQ) era by leveraging both classical and quantum computational resources. However, its performance can vary depending on the underlying quantum hardware architecture. In this work, we evaluate the performance of QAOA on different quantum hardware platforms, specifically, superconducting transmon qubits and trapped-ion qubits, targetting real-world optimization problems formulated as fully connected Quadratic Unconstrained Binary Optimization (QUBO) instances. We evaluate both the solution quality and time-to-solution using dense QUBO matrices. Furthermore, we show that large-scale problems, such as a 100-bit QUBO instance, can be effectively tackled by integrating quantum computing with high-performance computing (HPC) resources. This study provides practical insights into the strengths and limitations of different qubit technologies and advances the application of quantum computing in solving real-world optimization problems.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗
NASA NTRS2009

Non-unitary probabilistic quantum computing circuit and method

A quantum circuit performing quantum computation in a quantum computer. A chosen transformation of an initial n-qubit state is probabilistically obtained. The circuit comprises a unitary quantum operator obtained from a non-unitary quantum operator, operating on an n-qubit state and an ancilla state. When operation on the ancilla state provides a success condition, computation is stopped. When operation on the ancilla state provides a failure condition, computation is performed again on the ancilla state and the n-qubit state obtained in the previous computation, until a success condition is obtained.

Williams, Colin P.↗
DOE OSTI2025

Preventing native oxide formation in niobium thin films through platinum encapsulation

Superconducting transmon qubits are a key technology in quantum computing, with niobium (Nb) widely used due to its favorable superconducting properties and compatibility with scalable fabrication. However, the performance of Nb-based qubits is hindered by two-level systems (TLS), which arise predominantly from native oxide layers at surfaces and interfaces. These oxides and suboxides form rapidly in air and contribute significantly to decoherence by introducing dielectric losses. While various mitigation strategies, including thermal-annealing, chemical-etching, and nitrogen plasma treatment, have been explored, many are challenging to integrate into large-scale manufacturing due to complexity and instability in ambient conditions. In this study, we investigate the structural, chemical, and superconducting properties of room-temperature sputtered Nb thin films grown on sapphire substrates. We focus on a surface protection strategy using noble metal encapsulation with platinum (Pt) to suppress oxides and reduce TLS-related losses. Structural and chemical characterization were performed using X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy. Superconducting properties were measured using a physical property measurement system. Results demonstrate that Pt encapsulation effectively suppresses surface oxidation and mitigates its detrimental impact on superconducting performance. In conclusion, this approach offers a promising path toward enhancing the coherence of Nb-based qubits while remaining compatible with scalable-fabrication processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Valley splitting correlations across a silicon quantum well containing germanium

Quantum dots in SiGe/Si/SiGe heterostructures host coherent electron spin qubits, which are promising for future quantum computers. The silicon quantum well hosts near-degenerate electron valley states, creating a low-lying excited state that is known to reduce spin qubit readout and control fidelity. The valley energy splitting is dominated by the microscopic disorder in the SiGe alloy and at the Si/SiGe interfaces, and while Si devices are compatible with large-scale semiconductor manufacturing, achieving a uniformly large valley splitting energy across a many-qubit device spanning mesoscopic distances is an outstanding challenge. In this work we study valley splitting variations in a 1D quantum dot array, formed in a Si 0.972 Ge 0.028 quantum well, manufactured by Intel. We observe correlations in valley splitting, at both sub-100 nm (single gate) and > 1 μm (device) lengthscales, that are consistent with alloy disorder-dominated theory and simulation. Our results develop the mesoscopic understanding of Si/SiGe heterostructures necessary for scalable device design.

Marcks, Jonathan C. [Argonne National Laboratory (↗
DOE OSTI2024

Fast Z Z free entangling gates for superconducting qubits assisted by a driven resonator

Engineering high-fidelity two-qubit gates is an indispensable step toward practical quantum computing. For superconducting quantum platforms, one important setback is the stray interaction between qubits, which causes significant coherent errors. For transmon qubits, protocols for mitigating such errors usually involve fine-tuning the hardware parameters or introducing usually noisy flux-tunable couplers. In this work, we propose a simple scheme to cancel these stray interactions. The coupler used for such cancelation is a driven high-coherence resonator, where the amplitude and frequency of the drive serve as control knobs. Through the resonator-induced-phase interaction, the static Z Z coupling can be entirely neutralized. We numerically show that such a scheme can enable short and high-fidelity entangling gates, including cross-resonance controlled-not (cnot) gates within 40 ns and adiabatic controlled- Z gates within 140 ns. Our architecture is not only Z Z -free, but also contains no extra noisy components, such that it preserves the coherence times of fixed-frequency transmon qubits. With the state-of-the-art coherence times, the error of our cross-resonance cnot gate can be reduced to below 10 − 4 .

Huang, Ziwen↗
NASA NTRSDate not supplied

Exploring Quantum State Preparation Using Tensor Networks and Sparse Wavefunction Simulations

The variational quantum eigenvalue solver is a powerful hybrid quantum-classical approach that has been suggested as a candidate method to run on near-term quantum hardware for computing ground state electronic energies of molecular systems. However, even for small molecules, the number of variational parameters and qubits required to minimize the electronic energy is beyond the reach of current quantum computers except for small basis sets. We explore a new paradigm for state preparation where we test how much of the optimization can be approximately prepared with classical computers to reduce the number of optimization steps performed using a quantum device. By adapting a recent algorithm for the factorized form of the UCC ansatz, we can study molecular electronic structure problems with up to 64 qubits. In addition, we also test a related approach of using tensor networks to optimize quantum circuits in order to benchmark various lattice models. We present results using these approaches and discuss strategies for incorporating these ideas into variational algorithms involving near-term quantum computers. Our results help demonstrate the strength of the UCC ansatz and address pressing questions about optimal initial parameterizations and circuit construction.

quantum computing↗
DOE OSTI2025

Radiative processes on a quantum computer

Radiative processes, where a photon/neutrino is emitted as a result of a collision or decay of a particle, play a central role in atomic, nuclear, and particle physics. Their rate is determined by certain off-diagonal matrix elements between different initial and final states. We propose a method to compute them using quantum computers. It relies on a single extra qubit that, in a certain sense, represents the photon/neutrino. The generic formula relating this matrix element to the amplitude and frequency of oscillations of the extra qubit is derived for the near-resonance case. Furthermore, we demonstrate the feasibility of the method by using it in actual quantum computations and simulations of simple systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Two-Tower Quantum Matrix Chain Multiplication: Trading Qubits for Depth

Matrix chain multiplication -- computing $\mathcal{W} = M^{(0)}\cdots M^{(K-1)}$ where $M^{(k)} \in \mathbb{R}^{P_k \times P_{k+1}}$-- arises in scientific computing, machine learning, and graph analysis. Despite the importance of this problem, for chains of distinct matrices, the classical number of operations grows linearly with the chain length $K$ and polynomially in the matrix dimensions. We present \emph{Two-Tower Matrix Multiplication}, a quantum subroutine that encodes the product $\mathcal{W}$ of the $K$ matrices into a quantum state in circuit depth $\mathcal{O}(\max_{k} \mathrm{polylog} (P_k P_{k+1}))$, which is independent of~$K$ within the QRAM-based state-preparation model, whereas the qubit count is $\mathcal{O}\bigl(\sum_{k} \log P_k \bigr)$; the total gate count remains linear in $K$, so the gain is in the circuit depth. The construction interleaves state-preparation operators across two layers; within each layer, all operators act on disjoint registers and execute in parallel. This subroutine can be specialized for the chain-vector case, which computes the product of $K-1$ matrices applied to a vector. We prove the correctness of the subroutine for all $K$ and provide two implementations using the Qiskit and QCLAB frameworks. The subroutine is applicable to any downstream quantum algorithm that operates on a matrix encoded in the statevector, including norm estimation, graph-matrix powers, linear system solving, and quantum machine learning kernels.

Antonioli, Giacomo [Pisa U.] (ORCID:00090000668703↗
DOE OSTI2025

Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem

Simon’s problem is to find a hidden period (a bitstring) encoded into an unknown 2-to-1 function. It is one of the earliest problems for which an exponential quantum speedup was proven for ideal, noiseless quantum computers, albeit in the oracle model. Here, using two different 127-qubit IBM Quantum superconducting processors, we demonstrate an algorithmic quantum speedup for a variant of Simon’s problem where the hidden period has a restricted Hamming weight 𝑤. For sufficiently small values of 𝑤 and for circuits involving up to 58 qubits, we demonstrate an exponential speedup, albeit of a lower quality than the speedup predicted for the noiseless algorithm. The speedup exponent and the range of 𝑤 values for which an exponential speedup exists are significantly enhanced when the computation is protected by dynamical decoupling. Further enhancement is achieved with measurement error mitigation. This case constitutes a demonstration of a bona fide quantum advantage for an Abelian hidden subgroup problem.

computation↗
DOE OSTI2025

Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. Here, in this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach.

Li, Jinyang [George Mason Univ., Fairfax, VA (Unit↗
DOE OSTI2026

Point defects in semiconductors: Friends and foes for quantum technologies

Point defects in semiconductors are both a curse and a blessing in microelectronics: they enable the control of electrical conductivity through doping, yet can also act as trapping and recombination centers that degrade device performance. In quantum information science, defects play a similarly dual role. They can be harnessed as spin–photon interfaces enabling the coupling of electronic and nuclear spins to light and the creation of distributed entanglement for quantum networks or used as atomistic scale sensors for quantum sensing. At the same time, defects are a major source of decoherence for superconducting qubits, one of the leading quantum computing platforms. This article discusses how a deeper materials-level understanding of defects can guide the design of improved quantum devices for communication, sensing, and computation.

Zhu, Yizhi [Rice University, Houston, TX (United S↗
NASA NTRS2005

NASA Tech Briefs, July 2005

Thin-Film Resistance Heat-Flux Sensors Circuit Indicates that Voice-Recording Disks are Nearly Full Optical Sensing of Combustion Instabilities in Gas Turbines Topics include: Crane-Load Contact Sensor; Hexagonal and Pentagonal Fractal Multiband Antennas; Multifunctional Logic Gate Controlled by Temperature; Multifunctional Logic Gate Controlled by Supply Voltage; Power Divider for Waveforms Rich in Harmonics; SCB Quantum Computers Using iSWAP and 1-Qubit Rotations; CSAM Metrology Software Tool; Update on Rover Sequencing and Visualization Program; Selecting Data from a Star Catalog; Rotating Desk for Collaboration by Two Computer Programmers; Variable-Pressure Washer; Magnetically Attached Multifunction Maintenance Rover; Improvements in Fabrication of Sand/Binder Cores for Casting; Solid Freeform Fabrication of Composite-Material Objects; Efficient Computational Model of Hysteresis; Gauges for Highly Precise Metrology of a Compound Mirror; Improved Electrolytic Hydrogen Peroxide Generator; High-Power Fiber Lasers Using Photonic Band Gap Materials; Ontology-Driven Information Integration; Quantifying Traversability of Terrain for a Mobile Robot; More About Arc-Welding Process for Making Carbon Nanotubes; Controlling Laser Spot Size in Outer Space; or Software-Reconfigurable Processors for Spacecraft.

Source record↗
DOE OSTI2025

Clifford Circuit-Based Heuristic Optimization of Fermion-To-Qubit Mappings

Simulation of interacting Fermionic Hamiltonians is one of the most promising applications of quantum computers. However, the feasibility of analyzing Fermionic systems with a quantum computer hinges on the efficiency of Fermion-to-qubit mappings that encode nonlocal Fermionic degrees of freedom in local qubit degrees of freedom. While recent studies have highlighted the importance of designing Fermion-to-qubit mappings that are tailored to specific problem Hamiltonians, the methods proposed so far either are restricted to a narrow class of mappings or they use computationally expensive and unscalable brute-force search algorithms. Here, in this work, we address this challenge by designing a heuristic numerical optimization framework for Fermion-to-qubit mappings. To this end, we first translate the Fermion-to-qubit mapping problem to a Clifford circuit optimization problem and then use simulated annealing to optimize the average Pauli weight of the problem Hamiltonian. For all Fermionic Hamiltonians we have considered, the numerically optimized mappings outperform their conventional counterparts, including ternary-tree-based mappings that are known to be optimal for single creation and annihilation operators. We find that our optimized mappings yield between 15% and 40% improvements on the average Pauli weight when the simulation Hamiltonian has an intermediate level of complexity. Most remarkably, the optimized mappings improve the average Pauli weight for 6 × 6 nearest-neighbor hopping and Hubbard models by more than 40% and 20%, respectively. Surprisingly, we also find specific interaction Hamiltonians for which the optimized mapping outperforms any ternary-tree-based mapping. Our results establish heuristic numerical optimization as an effective method for obtaining mappings tailored for specific Fermionic Hamiltonian.

Hamiltonians↗
DOE OSTI2025

Machine learning for arbitrary single-qubit rotations on an embedded device

Here, in this study, we present a technique for using machine learning (ML) for single-qubit gate synthesis on field-programmable logic for a superconducting transmon-based quantum computer based on simulated studies. Our approach is multi-stage. We first “bootstrap” a model based on simulation with access to the full state vector for measuring gate fidelity. We next present an algorithm, named adapted randomized benchmarking (ARB), for fine-tuning the gate on hardware based on measurements of the devices. We also present techniques for deploying the model on programmable devices with care to reduce the required resources. While the techniques here are applied to a transmon-based computer, many of them are portable to other architectures.

97 MATHEMATICS AND COMPUTING↗
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

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)↗