Search NASA⌕ Search

SEARCH · Search NASA

Results for “optimal state preparation”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integrated operation scenarios: Chapter 6 of the special issue: on the path to tokamak burning plasma operation

Here we report the progress of the development and optimization of operational scenarios for ITER and beyond, focusing upon baseline, hybrid, and steady-state scenarios since 2007. This includes advancements made by the integrated operation scenarios (IOS) topical group of the international tokamak physical activity as well as contributions from the broader tokamak community. The key area of research involves developing IOSs that encompass tokamak physics, operation, and technology by utilizing integrated modeling and control strategies. This requires leveraging available actuators to simultaneously control plasma position and shape, MHD activities that could lead to disruptions, transport, plasma-wall interaction and power exhaust, fuel cycle, fusion burn, and tritium breeding. The control extends from the plasma initiation phase, through the current ramp-up, flattop, start and end of the fusion burn, and current ramp-down, to the plasma termination phase. A review of the currently developed scenarios and modeling is provided in terms of (i) optimizing plasma initiation in ITER, (ii) preparing for the low activation phase to fully commission all tokamak systems and establish and validate physics and scenario conditions in preparation for deuterim-tritium (DT) operation, (iii) developing and preparing baseline and hybrid scenarios to demonstrate the feasibility of achieving these regimes within device constraints, (iv) exploring steady-state scenarios to meet ITER’s steady-state goals, (v) evaluating and preparing actuators for ITER, (vi) developing integrated control solutions using shared actuators. The most notable achievements include; (i) the development of ITER demonstration discharges by matching various dimensionless parameters, (ii) the development of scenarios in an ITER-like tungsten environment and DT operation, and (iii) the development of scenarios in superconducting tokamaks, enabling long-pulse operations with similar coil constraints to ITER. Along with these significant achievements, outstanding issues and recommendations for further research and development are provided. Importantly, this study goes beyond simply updating the ITER Physics Basis; it carries profound implications for the broader field of burning plasma research, offering valuable insights and guidance for the next generation of fusion experiments and devices.

ITER↗
NASA NTRSDate not supplied

Derivation and Testing of Consumptive Water Use Fraction for Specialty Crops

The Crop Consumptive Use Fraction (CCUF) expresses beneficial water use in the form of seasonal evapotranspiration of applied water (ETAW), relative to total irrigation volume. The metric is an indicator of the efficiency of agricultural water use and is a recommended component for preparation of agricultural water management plans in California. An FAO-56 based web application has been developed to facilitate retrospective evaluation of ETAW, and hence CCUF, at field level. The current application is optimized for prevailing climate in four of the state’s main growing regions: San Joaquin Valley, Sacramento Valley, Central Coast, and North Coast. User inputs include crop type, soil texture, irrigation method, daily irrigation volume, daily rainfall, and seasonal start/stop dates to define the analysis period. Time series of fractional green canopy cover (Fc), based on Landsat and Sentinel-2 Earth-resource satellite observations, are imported from NASA’s Satellite Irrigation Management Support (SIMS) system. Grass reference evapotranspiration (ETo) time series are accessed from Spatial CIMIS (California Irrigation Management Information System). Daily crop height (h) is estimated as a simple function of typical maximum height for the given crop type (from FAO-56) and Fc. A vegetation density coefficient (Kd) is derived from Fc and h. Stomatal control factors during mid-and late-season are applied to tree and vine crops. Typical values for minimum daily relative humidity and mean daily windspeed, derived from historical CIMIS weather data, are used to correct for regional deviations from standard climate (defined as RHmin=45%, windspeed =2m/s). The resulting daily basal crop coefficient (Kcb) represents the ET of a well-watered crop with minimal soil evaporation, relative to ETo. A soil water balance sub-model for the top 0.1 m is used to calculate daily evaporation coefficients (Ke). A second sub-model applies to the root zone to calculate crop water stress coefficients (Ks) and effective precipitation (Pe), which is the fraction of rainfall that is available for crop use. Those coefficients are combined with Kcb and ETo to calculate daily ETc. ETAW is then derived as cumulative ETc less cumulative Pe. For verification purposes, sensor installations were used to measure seasonal ETc in commercial fields for several annual and perennial specialty crops by soil water balance and energy balance methods. Model estimates of seasonal ETc show mean absolute error of <10% compared to the ground measurements.

Derivation↗
DOE OSTI2023

Super p-sulfur cathodes for quasi-solid-state lithium-sulfur-batteries

Lithium-Sulfur (Li-S) batteries have become a promising candidate to meet the current energy storage demand, with its natural abundance of materials, high theoretical capacity of 1672 mAhg-1, high energy density of 2600 Whkg-1, low cost and lower environmental impact. Sulfide based solid state electrolytes (SSEs) have received greater attention due to their higher ionic conductivity, compatible interface with sulfur-based cathodes, and lower grain boundary resistance. However, the interface between SSEs and cathodes has become a challenge in all solid-state Li-S batteries due to the rigidity of the participating surfaces. A hybrid electrolyte containing SSE coupled with a small amount of ionic liquid, was essential to improve the interface contact of the SSE with the electrodes. Coating-based cathodes were successfully fabricated using water-based carboxymethyl cellulose (CMC) solution and Styrene butadiene rubber (SBR) as the binder with low sulfur loading (0.70 mgcm-2) as well as high sulfur loading (4.0 mgcm-2). Solid-state composite powder-based cathodes pressed onto SSE (loading 4.0 mgcm-2) with enhanced electronic and ionic conductivity were fabricated with Super P: Sulfur (SP:S) and SSE. Ionic Liquids (IL) prepared using Lithium bis(trifluoromethyl sulfonyl)imide (LiTFSI) as salt, with premixed pyrrolidinium bis(trifluoromethyl sulfonyl)imide (PYR) as solvent and 1,3-dioxolane (DOL) as diluent were used to wet both SSE-electrode interfaces. The effect of IL dilution, co-solvent amount, LiTFSI concentration, C rate at which the batteries are tested and the effect of SSE inside the cathode, were systematically studied and optimized to develop a quasi-solid-state electrolyte Li-S battery (QSSLSB) with higher capacity retention and cyclability. LiTFSI (2M) dissolved in PYR:DOL(1:1) found to be optimum IL combination for low sulfur loading QSSLSBs reaching 500 mAh/g after 100 cycles while LiTFSI (3M) in PYR:DOL(1:3) was the optimum IL concentration for higher loading QSSLSBs reaching 400 mAh/g after 100 cycles. This work reports promising results of QSSLSB based on novel Li6PS5F0.5Cl0.5 Li-argyrodite solid-state electrolyte (SSE) with minute amount of IL, Super P-Sulfur (SP:S) cathode, and Li-anode. It also offers a new insight into the intimate interfacial contacts between the SSE and carbon-sulfur cathodes, which will be critical for improved electrochemical performance of quasi-solid-state lithium-sulfur batteries with high sulfur loading in the future.

25 ENERGY STORAGE↗
DOE OSTI2020

Local lattice distortions and dynamics in extremely overdoped superconducting YSr 2 Cu 2.75 Mo 0.25 O 7.54

A common characteristic of many “overdoped” cuprates prepared with high-pressure oxygen is T c values ≥ 50 K that often exceed that of optimally doped parent compounds, despite O stoichiometries that place the materials at the edge or outside of the conventional boundary between superconducting and normal Fermi liquid states. X-ray absorption fine-structure (XAFS) measurements at 52 K on samples of high-pressure oxygen (HPO) YSr 2 Cu 2.75 Mo 0.25 O 7.54 , T c = 84 K show that the Mo is in the (VI) valence in an unusually undistorted octahedral geometry with predominantly Mo neighbors that is consistent with its assigned substitution for Cu in the chain sites of the structure. Perturbations of the Cu environments are minimal, although the Cu X-ray absorption near-edge structure (XANES) differs from that in other cuprates. The primary deviation from the crystal structure is therefore nanophase separation into Mo- and Cu-enriched domains. There are, however, indications that the dynamical attributes of the structure are altered relative to YBa 2 Cu 3 O 7 , including a shift of the Cu-apical O two-site distribution from the chain to the plane Cu sites. Another effect that would influence T c is the possibility of multiple bands at the Fermi surface caused by the presence of the second phase and the lowering of the Fermi level.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Probing Quantum Telecloning on Superconducting Quantum Processors

Quantum information cannot be perfectly cloned, but approximate copies of quantum information can be generated. Quantum telecloning combines approximate quantum cloning, more typically referred to as quantum cloning, and quantum teleportation. Quantum telecloning allows approximate copies of quantum information to be constructed by separate parties, using the classical results of a Bell measurement made on a prepared quantum telecloning state. Quantum telecloning can be implemented as a circuit on quantum computers using a classical coprocessor to compute classical feedforward instructions using if statements based on the results of a midcircuit Bell measurement in real time. We present universal symmetric optimal 1 → M telecloning circuits and experimentally demonstrate these quantum telecloning circuits for M = 2 up to M = 10, natively executed with real-time classical control systems on IBM Quantum superconducting processors, known as dynamic circuits. We perform the cloning procedure on many different message states across the Bloch sphere, on seven IBM Quantum processors, optionally using the error suppression technique X–X sequence digital dynamical decoupling. Two circuit optimizations are utilized: one that removes ancilla qubits for M = 2,3 , and one that reduces the total number of gates in the circuit but still uses ancilla qubits. Parallel single-qubit tomography with maximum likelihood estimation density matrix reconstruction is used in order to compute the mixed-state density matrices of the clone qubits, and clone quality is measured using quantum fidelity. These results present one of the largest and most comprehensive noisy intermediate-scale quantum computer experimental analyses on (single qubit) quantum telecloning to date. The clone fidelity sharply decreases to 0.5 for M > 5 , but for M = 2 , we are able to achieve a mean clone fidelity of up to 0.79 using dynamical decoupling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Arbitrary State Preparation via Quantum Walks Software

SF-24-077 Our software generates a circuit that prepares a specified target quantum state. It generates circuits with CNOT counts of O(mn), where m is the number of nonzero amplitudes of the state and n is the number of qubits. The method does not require ancillas. It is designed for sparse states, but works for any quantum state. Our method is based on the new perspective of dynamic continuous time quantum walks for quantum state preparation. This is an intuitive perspective where a graph is first constructed to traverse the basis states. The method works by 1.) construct a graph of the basis states, 2.) transform the graph into quantum walks, and 3.) construct the final gate based circuit optimized with our control reduction method.

Liu, Ji↗
NASA NTRS1985

Raman structural studies of the nickel electrode

Raman spectroscopy is sensitive to empirically controlled nickel electrode structural variations, and has unique potential for structural characterization of these materials. How the structure relates to electrochemical properties is examined so that the latter can be more completely understood, controlled, and optimized. Electrodes were impregnated and cycled, and cyclic voltammetry is being used for electrochemical characterization. Structural variation was observed which has escaped detection using other methods. Structural changes are induced by: (1) cobalt doping, (2) the state of change or discharge, (3) the preparation conditions and type of buffer used, and (4) the formation process. Charged active mass has an NiOOH-type structure, agreeing with X-ray diffraction results. Discharged active mass, however, is not isostructural with beta-Ni(OH)2. Chemically prepared alpha phases are not isostructural either. A disordered structural model, containing point defects, is proposed for the cycled materials. This model explains K(+) incorporation. Band assignments were made and spectra interpreted for beta-Ni(OH)2, electrochemical NiOOH and chemically precipitated NiOOH.

Cornilsen, B. C.↗
NASA NTRS2013

Optimal Propellant Maneuver Flight Demonstrations on ISS

In this paper, first ever flight demonstrations of Optimal Propellant Maneuver (OPM), a method of propulsive rotational state transition for spacecraft controlled using thrusters, is presented for the International Space Station (ISS). On August 1, 2012, two ISS reorientations of about 180deg each were performed using OPMs. These maneuvers were in preparation for the same-day launch and rendezvous of a Progress vehicle, also a first for ISS visiting vehicles. The first maneuver used 9.7 kg of propellant, whereas the second used 10.2 kg. Identical maneuvers performed without using OPMs would have used approximately 151.1kg and 150.9kg respectively. The OPM method is to use a pre-planned attitude command trajectory to accomplish a rotational state transition. The trajectory is designed to take advantage of the complete nonlinear system dynamics. The trajectory choice directly influences the cost of the maneuver, in this case, propellant. For example, while an eigenaxis maneuver is kinematically the shortest path between two orientations, following that path requires overcoming the nonlinear system dynamics, thereby increasing the cost of the maneuver. The eigenaxis path is used for ISS maneuvers using thrusters. By considering a longer angular path, the path dependence of the system dynamics can be exploited to reduce the cost. The benefits of OPM for the ISS include not only reduced lifetime propellant use, but also reduced loads, erosion, and contamination from thrusters due to fewer firings. Another advantage of the OPM is that it does not require ISS flight software modifications since it is a set of commands tailored to the specific attitude control architecture. The OPM takes advantage of the existing ISS control system architecture for propulsive rotation called USTO control mode1. USTO was originally developed to provide ISS Orbiter stack attitude control capability for a contingency tile-repair scenario, where the Orbiter is maneuvered using its robotic manipulator relative to the ISS. Since 2005 USTO has been used for nominal ISS operations.

Bhatt, Sagar↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Advanced Method Optimization with Categorical and Constrained Continuous Parameters

Traditional approaches to analytical method optimization (e.g., univariate and “guess-and-check”) can be time-consuming, costly, and often fail to identify true optima within the parameter space. Previous work defined and implemented a generalized technique for method optimization for continuous method parameters, but a knowledge gap remains for the incorporation of categorical variables into these advanced method optimization schemes. This work presents and validates a generalized optimization approach that incorporates both continuous and categorical variables while also utilizing a multivariate, multiobjective optimization scheme with Karush–Kuhn–Tucker conditions to bound the optimization space to solutions within the physical limitations of the parameter space. Method optimization from a case study using GC–MS for the analysis of 11 analytical standards with objectives to minimize peak width and maximize peak height resulted in a 3 orders of magnitude improvement in the average peak height and a 2 orders of magnitude improvement in the average peak width compared to the least optimal (but reasonable) instrumental parameters utilized in this study. This approach to optimization allows for a customizable method optimization in which users can include both continuous and categorical variables to achieve objectives specific to their analytical goals. This approach significantly reduces the labor and cost associated with traditional method development approaches and can be applied in a variety of scientific fields across a range of laboratory techniques (e.g., instrument method development, sample preparation, and extraction techniques).

Amorphous materials↗
DOE OSTI2023

Learning Many-Body Hamiltonians with Heisenberg-Limited Scaling

Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. Here, in this Letter, we propose the first algorithm to achieve the Heisenberg limit for learning an interacting N-qubit local Hamiltonian. After a total evolution time of $\mathscr{O}$⁡(ε –1 ), the proposed algorithm can efficiently estimate any parameter in the N-qubit Hamiltonian to ε error with high probability. Our algorithm uses ideas from quantum simulation to decouple the unknown N-qubit Hamiltonian H into noninteracting patches and learns H using a quantum-enhanced divide-and-conquer approach. The proposed algorithm is robust against state preparation and measurement error, does not require eigenstates or thermal states, and only uses polylog⁡(ε –1 ) experiments. In contrast, the best existing algorithms require $\mathscr{O}$⁡⁡(ε –2 ) experiments and total evolution time. We prove a matching lower bound to establish the asymptotic optimality of our algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Sn doping on partially dealuminated Beta zeolite by solid state ion exchange for 5‐hydroxymethylfurfural ( 5‐HMF ) production from glucose

Abstract BACKGROUND The conversion of glucose into 5‐hydroxymethylfurfural (5‐HMF) involves the isomerization of glucose to fructose catalyzed by Lewis acid and subsequent dehydration of fructose catalyzed by Brönsted acid. In this work, in order to obtain a high yield of 5‐HMF, a series of Sn‐Al‐Beta catalysts containing both Lewis and Brönsted acid sites were prepared by a convenient solid state ion exchange (SSIE) method using partially dealuminated Beta zeolite and tin(II) acetate. RESULTS The obtained bifunctional catalysts were investigated for one‐pot production of 5‐HMF from glucose in the ionic liquid (IL) [C 4 mim]Cl. Among various catalysts, Sn‐Al‐Beta‐ 4 ‐ 8 exhibited the best catalytic performance, giving a 5‐HMF yield of 54% under optimal reaction conditions (393 K, 2 h), along with a glucose conversion of 81%. The Sn‐Al‐Beta‐ 4‐8 catalyst and IL were regenerated by the extraction of 5‐HMF, and the reactivity was reserved after five runs of recycling. In addition to glucose, the Sn‐Al‐Beta‐ 4‐8 /[C 4 mim]Cl system was also proven to have the potential to effectively convert other carbohydrates into 5‐HMF. CONCLUSION The results suggested that the catalyst is convenient to prepare and has preferable efficiency and stability in the conversion of carbohydrates into 5‐HMF. © 2022 Society of Chemical Industry (SCI).

Zhang, Wei↗
DOE OSTI2021

Argon broad ion beam sectioning and high resolution scanning electron microscopy imaging of hydrated alite

Highlights: • The native fibrous outer and dense inner C-S-H structure are preserved by using low energy argon broad ion beam sectioning. • Possible artefacts of the preparation and imaging of hydrated alite are shown and discussed. • An approach for a semi automated pore analysis of the obtained high resolution images is demonstrated. Scanning electron microscopy (SEM) imaging is able to visualize micro- to nano-structures of cement and concrete. A prerequisite is that the sample preparation preserves the native structure of the specimen. In this study, argon broad ion beam (BIB) sectioning is compared to state-of-the-art sample preparation (resin embedding, polishing) for hydrated alite. Additionally, it is investigated if during BIB, sample cooling is beneficial to avoid deterioration of cement hydrates. The aim is to quantitatively measure pore size distributions in hardened alite pastes. Therefore, not only optimized sample preparation but also optimized imaging conditions are investigated. Finally, it is demonstrated that by image analysis pores down to a diameter of 5 nm in hydrated alite pastes can be quantitatively analysed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Noise resilience of variational quantum compiling

Variational hybrid quantum-classical algorithms (VHQCAs) are near-term algorithms that leverage classical optimization to minimize a cost function, which is efficiently evaluated on a quantum computer. Recently VHQCAs have been proposed for quantum compiling, where a target unitary U is compiled into a short-depth gate sequence V. In this work, we report on a surprising form of noise resilience for these algorithms. Namely, we find one often learns the correct gate sequence V (i.e. the correct variational parameters) despite various sources of incoherent noise acting during the cost-evaluation circuit. Our main results are rigorous theorems stating that the optimal variational parameters are unaffected by a broad class of noise models, such as measurement noise, gate noise, and Pauli channel noise. Furthermore, our numerical implementations on IBM's noisy simulator demonstrate resilience when compiling the quantum Fourier transform, Toffoli gate, and W-state preparation. Hence, variational quantum compiling, due to its robustness, could be practically useful for noisy intermediate-scale quantum devices. Finally, we speculate that this noise resilience may be a general phenomenon that applies to other VHQCAs such as the variational quantum eigensolver.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Single-ancilla ground state preparation via Lindbladians

We design a quantum algorithm for ground state preparation in the early fault tolerant regime. As a Monte Carlo style quantum algorithm, our method features a Lindbladian where the target state is stationary. The construction of this Lindbladian is algorithmic and should not be seen as a specific approximation to some weakly coupled system-bath dynamics in nature. Our algorithm can be implemented using just one ancilla qubit and efficiently simulated on a quantum computer. It can prepare the ground state even when the initial state has zero overlap with the ground state, bypassing the most significant limitation of methods like quantum phase estimation. As a variant, we also propose a discrete-time algorithm, demonstrating even better efficiency and providing a near-optimal simulation cost depending on the desired evolution time and precision. Numerical simulations using Ising and Hubbard models demonstrate the efficacy and applicability of our method. Published by the American Physical Society 2024

Ding, Zhiyan (ORCID:000000018863403X)↗
DOE OSTI2025

I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey

Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&D.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Design of quantum optical experiments with logic artificial intelligence

Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and solved by checking their satisfiability (SAT). In contrast to machine learning approaches where the results can be approximations or local minima, Logic AI delivers formal and mathematically exact solutions to those problems. In this work, we propose the use of logic AI for the design of optical quantum experiments. We show how to map into a SAT problem the experimental preparation of an arbitrary quantum state and propose a logic-based algorithm, called Klaus, to find an interpretable representation of the photonic setup that generates it. We compare the performance of Klaus with the state-of-the-art algorithm for this purpose based on continuous optimization. We also combine both logic and numeric strategies to find that the use of logic AI significantly improves the resolution of this problem, paving the path to developing more formal-based approaches in the context of quantum physics experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Error rate reduction of single-qubit gates via noise-aware decomposition into native gates

In the current era of Noisy Intermediate-Scale Quantum (NISQ) technology, the practical use of quantum computers remains inhibited by our inability to aptly decouple qubits from their environment to mitigate computational errors. In this paper, we introduce an approach by which knowledge of a qubit’s initial quantum state and the standard parameters describing its decoherence can be leveraged to mitigate the noise present during the execution of a single-qubit gate. We benchmark our protocol using cloud-based access to IBM quantum processors. On ibmq_rome, we demonstrate a reduction of the single-qubit error rate by 38%, from $1.6 \times 10 ^{-3}$ to $1.0 \times 10 ^{-3}$ , provided the initial state of the input qubit is known. On ibmq_bogota, we prove that our protocol will never decrease gate fidelity, provided the system’s T 1 and T 2 times have not drifted above 100 times their assumed values. The protocol can be used to reduce quantum state preparation errors, as well as to improve the fidelity of quantum circuits for which some knowledge of the qubits’ intermediate states can be inferred. This paper presents a pathway to using information about noise levels and quantum state distributions to significantly reduce error rates associated with quantum gates via optimized decomposition into native hardware gates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗