Search NASA⌕ Search

SEARCH · Search NASA

Results for “Quantum Computing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
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 OSTI2025

S-QGPU: Shared quantum gate processing unit for distributed quantum computing

We propose a distributed quantum computing (DQC) architecture in which individual small-sized quantum computers are connected to a shared quantum gate processing unit (S-QGPU). The S-QGPU comprises a collection of hybrid two-qubit gate modules for remote gate operations. In contrast to conventional DQC systems, where each quantum computer is equipped with dedicated communication qubits, S-QGPU effectively pools the resources (e.g., the communication qubits) together for remote gate operations, and, thus, significantly reduces the cost of not only the local quantum computers but also the overall distributed system. Our preliminary analysis and simulation show that S-QGPU's shared resources for remote gate operations enable efficient resource utilization. When not all computing qubits (also called data qubits) in the system require simultaneous remote gate operations, S-QGPU-based DQC architecture demands fewer communication qubits, further decreasing the overall cost. Alternatively, with the same number of communication qubits, it can support a larger number of simultaneous remote gate operations more efficiently, especially when these operations occur in a burst mode.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Quantum tensor network algorithms for evaluation of spectral functions on quantum computers

We investigate quantum algorithms derived from tensor networks to simulate the static and dynamic properties of quantum many-body systems. Using a sequentially prepared quantum circuit representation of a matrix product state (MPS) that we call a quantum tensor network (QTN), we demonstrate algorithms to prepare ground and excited states on a quantum computer and apply them to molecular nanomagnets (MNMs) as a paradigmatic example. In this setting, we develop two approaches for extracting the spectral correlation functions measured in neutron-scattering experiments: (a) a generalization of the SWAP test for computing wave function overlaps and, (b) a generalization of the notion of matrix product operators to the QTN setting which generates a linear combination of unitaries. The latter method is discussed in detail for translationally invariant spin-half systems, where it is shown to reduce the qubit resource requirements compared with the SWAP method and may be generalized to other systems. We demonstrate the versatility of our approaches by simulating spin-1/2 and spin-3/2 MNMs, with the latter being an experimentally relevant model of a Cr$^{3+}_{8}$ ring. Here, our approach has qubit requirements that are independent of the number of constituents of the many-body system and scale only logarithmically with the bond dimension of the MPS representation, making them appealing for implementation on near-term quantum hardware with mid-circuit measurement and reset.

Neutron scattering↗
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 OSTI2025

Lie-algebraic classical simulations for quantum computing

The classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity and in the development of quantum technologies. Efficient techniques such as those based on the Gottesman-Knill theorem for Clifford circuits, tensor networks for low entanglement-generating circuits, or Wick's theorem for fermionic Gaussian states have become central tools in quantum computing. In this work, we contribute to this body of knowledge by presenting a framework for classical simulations, dubbed “𝔤-sim”, which is based on the underlying Lie algebraic structure of the dynamical process. When the dimension of the algebra grows at most polynomially in the system size, there exist observables for which the simulation is efficient. Indeed, we show that 𝔤-sim enables new regimes for classical simulations, is able to deal with certain forms of noise in the evolution, as well as can be used to tackle several paradigmatic variational and nonvariational quantum computing tasks. For the former, we perform Lie-algebraic simulations to train and optimize parametrized quantum circuits (thus effectively showing that some variational models can be dequantized), design enhanced parameter initialization strategies, solve tasks of quantum circuit synthesis, and train a quantum-phase classifier. For the latter, we report large-scale noiseless and noisy simulations on benchmark problems. By comparing the limitations of 𝔤-sim and certain Wick's theorem-based simulations, we find that the two methods become inefficient for different types of states or observables, hinting at the existence of distinct, nonequivalent resources for classical simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Benchmarking quantum computers

The rapid pace of development in quantum computing technology has sparked a proliferation of benchmarks to assess the performance of quantum computing hardware and software. However, not all benchmarks are of equal merit. Good ones empower scientists, engineers, programmers and users to understand the power of a computing system, whereas bad ones can misdirect research and inhibit progress. In this Perspective, we survey the science of quantum computer benchmarking. Here, we discuss the role of benchmarks and benchmarking and how good benchmarks can drive and measure progress towards the long-term goal of useful quantum computations, known as quantum utility. We explain how different kinds of benchmark quantify the performance of different parts of a quantum computer, discuss existing benchmarks, examine recent trends in benchmarking, and highlight important open research questions in this field.

Proctor, Timothy James [Sandia National Laboratori↗
DOE OSTI2026

Energy and physical resource impacts of quantum computing merit greater attention

Quantum computing research and development is growing worldwide; yet the energy and physical resource demands of future quantum-accelerated data centres are unknown. Planning for quantum computing requires strong collaboration between research communities across engineering, physics, environmental sciences, economics, policy, and energy systems and scenario modelling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Quantum Computing Market Report

The quantum computing market is entering a rapid growth phase. Growth was originally driven by private investment interest in high value applications such as drug discovery, materials science, and finance. Currently the main market drivers include technical advances, continued private investment, and rising public funding. The quantum computing market is poised for explosive growth — with its market size estimated at $1.42 billion USD in 2024, and an expected compound annual growth rate of 20.5% from 2025 to 2030 [4]. Superconducting qubits are the largest qubit type by revenue. However, alternative approaches such as trapped ion, neutral atom, spin based, and photonic qubits are also attracting investment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

How Do You Hear a Quantum Computer Whisper?

Quantum transduction is the process of upconverting microwave quantum signals into optical signals to develop quantum networks through optical fibers outside the dilution refrigerator, enabling connections between quantum technologies on the quantum internet. In this project, upconversion is achieved by directing an optical laser and the microwave quantum signal into an electro-optic bulk crystal. The crystal is housed within a Superconducting Radio Frequency (SRF) cavity designed to maximize the overlap between the microwave field and the crystal volume. In the presence of microwaves, the refractive index of the crystal changes through the Pockels effect. This change in refractive index modifies the propagation of the optical field within the crystal, allowing the quantum information carried by the microwave field to be transferred to the optical field. This study focuses on coupling laser light from suspended waveguide chips into the whispering-gallery modes (WGMs) of the crystal to enable transduction. The coupling efficiency between the optical field in the suspended waveguide and the WGM depends on the position of the laser spot on the crystal. To address this challenge, a feedback-based algorithm is being developed to fine-tune the waveguide position so that the optical signal is coupled efficiently into the crystal. After passing through the crystal, the optical signal is detected by a photodiode connected to an oscilloscope. The algorithm evaluates the coupling quality and iteratively adjusts the waveguide position to maximize coupling efficiency. The expected outcome is an automated waveguide alignment method that improves optical coupling and enables more efficient microwave-to-optical quantum transduction.

Karanastasis, Mihael [Fermilab]↗
DOE OSTI2022

Quantum Circuits for the Preparation of Spin Eigenfunctions on Quantum Computers

The application of quantum algorithms to the study of many-particle quantum systems requires the ability to prepare wave functions that are relevant in the behavior of the system under study. Hamiltonian symmetries are important instruments used to classify relevant many-particle wave functions and to improve the efficiency of numerical simulations. In this work, quantum circuits for the exact and approximate preparation of total spin eigenfunctions on quantum computers are presented. Two different strategies are discussed and compared: exact recursive construction of total spin eigenfunctions based on the addition theorem of angular momentum, and heuristic approximation of total spin eigenfunctions based on the variational optimization of a suitable cost function. The construction of these quantum circuits is illustrated in detail, and the preparation of total spin eigenfunctions is demonstrated on IBM quantum devices, focusing on three- and five-spin systems on graphs with triangle connectivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Mathematical Foundation for Quantum Computing of Electromagnetic Wave Propagation in Dielectric Media

Can quantum computers effectively simulate the propagation and scattering of electromagnetic waves in a classical plasma? This chapter introduces some of the basic concepts in mathematics and physics essential to answering that question. The numerical simulations of Maxwell equations for wave propagation in dielectrics are constrained by technological limitations of the present-day computers. In contrast, there has been ample fanfare around quantum computers and their potential to far exceed the performance of traditional computers. Whether the enhanced capabilities of a quantum computer can be put to use for simulating topics in classical physics is a source of intrigue and curiosity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Learning quantum computers' errors using interpretable neural networks

Learning and reducing the errors and noise in quantum computing systems is necessary for achieving quantum computation’s promise. However, rapid advances in experimental quantum computing are making this task increasingly difficult, because state-of-the-art systems now contain hundreds of qubits and many characterization techniques are hard to apply at this scale. Furthermore, complex kinds of errors in these systems, such as crosstalk and non-Markovian effects, must be understood and decreased, but these errors are challenging to study with most existing methods. In this project, we explored using neural networks for scalable characterization of complex errors in quantum computers. We proposed and demonstrated characterizing a quantum computer’s errors with neural networks that have interpretable parameters corresponding to the rates of different kinds of errors, within a sparse Lindbladian parameterization for errors. To enable scaling to many qubit systems, these networks then predict how these errors combine within quantum circuits and impact their outcomes using an efficient approximations. We demonstrated these networks ability to learn coherent crosstalk errors and context-dependent errors in a simulated 4-qubit system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Playing Nonlocal Games across a Topological Phase Transition on a Quantum Computer

Many-body quantum games provide a natural perspective on phases of matter in quantum hardware, crisply relating the quantum correlations inherent in phases of matter to the securing of quantum advantage at a device-oriented task. In this Letter, we introduce a family of multiplayer quantum games for which topologically ordered phases of matter are a resource yielding quantum advantage. Unlike previous examples, quantum advantage persists away from the exactly solvable point and is robust to arbitrary local perturbations, irrespective of system size. We demonstrate this robustness experimentally on Quantinuum’s H1-1 quantum computer by playing the game with a continuous family of randomly deformed toric code states that can be created with constant-depth circuits leveraging midcircuit measurements and unitary feedback. We are thus able to tune through a topological phase transition—witnessed by the loss of robust quantum advantage—on currently available quantum hardware. This behavior is contrasted with an analogous family of deformed Greenberger-Horne-Zeilinger states, for which arbitrarily weak local perturbations destroy quantum advantage in the thermodynamic limit. Lastly, we discuss a topological interpretation of the game, which leads to a natural generalization involving an arbitrary number of players.

97 MATHEMATICS AND COMPUTING↗