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The objective of this work is to combine HEP simulation experience with HPC quantum systems simulations to provide an environment for executing large-scale experiments with advanced analysis capabilities for studying multi-qubit and multi-level reduced-noise, long coherence time quantum systems such as 3D Superconducting RF (SRF) cavities integrated with transmon qubits.
Methods for analysing the quality of the element base of quantum information technologies are considered. Methods for simulating quantum operations with allowance for quantum noise, based on the formalism of quantum operations (operator sum method and Choi – Jamiolkowski isomorphism), are described. Results of simulating some one- and two-qubit quantum transformations with allowance for the amplitude and phase relaxation and the presence of depolarising noise are reported. The quantum state tomography, based on the root approach and maximum likelihood method, is considered. The method of guaranteed estimation of quantum tomography accuracy, which implies quantitative estimation of measurement results, is described. Tomography of a single-qubit transformation on a superconducting quantum processor (IBM) is performed. The practical purpose of the study is to provide high quality and efficiency of quantum information technologies. The results obtained can be used to improve the quality of logic gates of quantum computers and debug the procedures of quantum state control in quantum cryptography problems. The bibliography includes 28 references. (quantum technology)
In this paper, we present a strategy for producing multiqubit gates that promise high fidelity with minimal tuning requirements. Our strategy combines gap protection from the adiabatic theorem with dynamical decoupling in a complementary manner. Energy-level transition errors are protected by adiabaticity and remaining phase errors are mitigated via dynamical decoupling. This is a powerful way to divide and conquer the various error channels. In order to accomplish this without violating a no-go theorem regarding black-box dynamically corrected gates [Phys. Rev. A 80, 032314 (2009)], we require a robust operating point (sweet spot) in control space where the qubits interact with little sensitivity to noise. There are also energy gap requirements for effective adiabaticity. We apply our strategy to an architecture in Si with P donors where we assume we can shuttle electrons between different donors. Electron spins act as mobile ancillary qubits and P nuclear spins act as long-lived data qubits. Furthermore, this system can have a very robust operating point where the electron spin is bound to a donor in the quadratic Stark shift regime. High fidelity single qubit gates may be performed using well-established global magnetic resonance pulse sequences. Single electron-spin preparation and measurement has also been demonstrated. Thus, putting this all together, we present a robust universal gate set for quantum computation.
The most striking features of novel two-dimensional semiconductors (e.g., transition metal dichalcogenide monolayers or phosphorene) is a strong Coulomb interaction between charge carriers resulting in large excitonic effects. In particular, this leads to the formation of multicarrier bound states upon photoexcitation (e.g., excitons, trions, and biexcitons), which could remain stable at near-room temperatures and contribute significantly to the optical properties of such materials. In our work we have used the path integral Monte Carlo methodology to numerically study properties of multicarrier bound states in two-dimensional semiconductors. Specifically, we have accurately investigated and tabulated the dependence of single-exciton, trion, and biexciton binding energies on the strength of dielectric screening, including the limiting cases of very strong and very weak screening. Our results of this work are potentially useful in the analysis of experimental data and benchmarking of theoretical and computational models.
Quantum annealing is a quantum algorithm for computing solutions to combinatorial optimization problems. This study proposes a method for minor embedding optimization problems onto sparse quantum annealing hardware graphs called 4-clique network minor embedding. This method is in contrast to the standard minor embedding technique of using a path of linearly connected qubits in order to represent a logical variable state. The 4-clique minor embedding is possible on Pegasus graph connectivity, which is the native hardware graph for some of the current D-Wave quantum annealers. The Pegasus hardware graph contains many cliques of size 4, making it possible to form a graph composed entirely of paths of connected 4-cliques on which a problem can be minor-embedded. The 4-clique chains come at the cost of additional qubit usage on the hardware graph, but they allow for stronger coupling within each chain, thereby increasing chain integrity, reducing chain breaks, and allow for greater usage of the available energy scale for programming logical problem coefficients on current quantum annealers. The 4-clique minor embedding technique is compared with the standard linear path minor embedding with experiments on two D-Wave quantum annealing processors with Pegasus hardware graphs. We show proof-of-concept experiments where the 4-clique minor embeddings can use weak chain strengths while successfully carrying out the computation of minimizing random all-to-all spin glass problem instances. Published by the American Physical Society 2024
The driven-dissipative many-body problem remains one of the most challenging unsolved problems in quantum mechanics. The advent of quantum computers may provide a unique platform for efficiently simulating such driven-dissipative systems. But, there are many choices for how one can engineer the reservoir. One can simply employ ancilla qubits to act as a reservoir and then digitally simulate them via algorithmic cooling. A more attractive approach, which allows one to simulate an infinite reservoir, is to integrate out the bath degrees of freedom and describe the driven-dissipative system via a master equation, that can also be simulated on a quantum computer. In this work, we consider the particular case of noninteracting electrons on a lattice driven by an electric field and coupled to a fermionic thermostat. Then, we provide two different quantum circuits: the first one reconstructs the full dynamics of the system using Trotter steps, while the second one dissipatively prepares the final nonequilibrium steady state in a single step. We run both circuits on the IBM quantum experience. For circuit (i), we achieved up to five Trotter steps. When partial resets become available on quantum computers, we expect that the maximum simulation time can be significantly increased. Lastly, the methods developed here suggest generalizations that can be applied to simulating interacting driven-dissipative systems.
The U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) is a collaborative national center for fusion energy science, basic sciences, and advanced technology. The Laboratory has three major missions: (1) to develop the scientific knowledge and advanced engineering to enable fusion to power the U.S. and the world; (2) to advance the science of nanoscale fabrication for technologies of tomorrow; and (3) to further the development of the scientific understanding of the plasma universe from laboratory to astrophysical scales. PPPL’s Laboratory Directed Research and Development (LDRD) program supports and encourages creativity and innovation and contributes to its long-term viability. New scientific and technical research areas emerge and are nurtured through the program. Furthermore, new capabilities are developed to enable the Laboratory to meet its and DOE’s missions. The program is used to systematically diversify the Laboratory’s programs and mission. In the last few years, the program has started projects in nanomaterial synthesis, microelectronics, advanced x-ray spectroscopy, high-energy-density physics, superconducting magnet technology, machine learning and artificial intelligence, 3D magnetic fields to optimize fusion plasmas, integration of permanent magnets with simple high-field magnets to reduce the cost of producing complex 3D magnetic fields, advanced computational methods for predictive understanding and control of fusion plasma, development of quantum computing algorithms for plasma physics, liquid metal plasma-facing components for fusion reactors, virtual engineering, and plasma-based space propulsion. The program is also the vehicle to recruit and train talented scientists and engineers with the new skills needed to perform the Laboratory’s mission. Many of the new hires through the program go on to become world-class scientists and engineers in their fields. This report provides descriptions and accomplishments of those LDRD projects that were completed during fiscal years 2018 through 2020.
A density functional theory parameterized lattice gas cluster expansion of oxygen on an Fe(100) surface is developed in the presence and absence of an applied positive and negative external electric field, characterizing the heterogeneity in oxygen's surface distribution and the effect of an external electric field on the lateral interactions between the adsorbates. We show that the presence of a negative electric field tends to weaken both attractive and repulsive interactions while the positive electric field tends to strengthen these interactions, altering surface distributions and ground state configurations. Since lateral interactions have been shown to play a critical role in defining catalytic behavior, the application of an applied electric field has the potential to be a useful tool in adjusting critical chemical properties of Fe-based hydrodeoxygenation catalysts, by decreasing the repulsive interactions between the adspecies in the presence of a negative field and thereby mitigating the formation of an oxide.
A formalism is presented for the construction of multicomponent lattice gas models parameterized with ab initio (typically density functional theory) data. The leave-multiple-out and the leave-one-out cross validation score are showcased, and practical algorithms are developed and implemented in a new code called the Ab initio Mean-field Augmented Lattice Gas Modeling (AMALGM) code. Here, the functionality of these algorithms is demonstrated with a fully worked out example using the O/Fe(100) system. AMALGM and the formalism on which it is based are envisioned as a surface-oriented lattice gas alternative to other cluster expansion codes that are typically geared toward bulk systems whose lateral interactions between components are parameterized using an Ising model. While the formalism is created within the context of surfaces, it is equally applicable to bulk materials.
Superconducting topological systems formed by a strong 3D topological insulator (TI) in proximity to a conventional s-wave superconductor (SC) have been intensely studied, as they may host Majorana zero modes. However, there are limited experimental realizations of TI-SC systems in which robust superconducting pairing is induced on the surface states of the TI and a topological superconducting state is established. Here, we fabricate a TI-SC system by depositing, via a focused ion beam, tungsten (W) nanoscale clusters on the surface of TI Bi 0.91 Sb 0.09 . We find that the resulting heterostructure supports phase-slip lines (PSLs) that act as effective Josephson junctions (JJs). Here, we probe the response of the system to microwave radiation. We find that for some ac frequencies, and powers, the resulting Shapiro steps’ structure of the voltage–current characteristic exhibits a missing first step and an unexpectedly wide second Shapiro step. The theoretical analysis of the measurements shows that the unusual Shapiro response arises from the interplay between a static JJ and a dynamic one and allows us to identify the conditions under which the missing first step can be attributed to the topological nature of the JJs formed by the PSLs. Our results suggest an approach to induce superconductivity in a TI, a route to realizing highly transparent topological JJs, and show how the response of superconducting systems to microwave radiation can be used to infer the dynamics of PSLs. Highly transparent topological junctions are promising candidates to realize vector field sensors with very high sensitivity. In addition, due to the nontrivial Berry phase of the TI’s surface states such junctions can be in a topological state which is ideal to create topologically protected qubits.
A mechanistic understanding of the roles of water is essential for developing highly active and selective catalysts for hydrodeoxygenation (HDO) reactions since water is ubiquitous in such reaction systems. Here, we present a study for phenol HDO on Fe catalysts using density functional theory which examines the effect of water on three elementary pathways for phenol HDO using an explicit solvation model. The presence of water is found to significantly decrease activation barriers required by hydrogenation reactions via two pathways. First, the proton transfer in the hydrogen bonding network of the liquid water phase is nearly barrierless, which significantly promotes the direct through space tautomerization of phenol. Second, due to the high degree of oxophilicity on Fe, liquid water molecules are found to be easily dissociated into surface hydroxyl groups that can act as Brønsted acid sites. These sites dramatically promote hydrogenation reactions on the Fe surface. As a result, the hydrogen assisted dehydroxylation becomes the dominant phenol HDO pathway. This work provides new fundamental insights into aqueous phase HDO of biomass-derived oxygenates over Fe-based catalysts; e.g., the activity of Fe-based catalysts can be optimized by tuning the surface coverage of Brønsted acid sites via surface doping.
Entanglement and its propagation are central to understanding many physical properties of quantum systems. Notably, within closed quantum many-body systems, entanglement is believed to yield emergent thermodynamic behaviour. However, a universal understanding remains challenging owing to the non-integrability and computational intractability of most large-scale quantum systems. Quantum hardware platforms provide a means to study the formation and scaling of entanglement in interacting many-body systems. Here we use a controllable 4 × 4 array of superconducting qubits to emulate a 2D hard-core Bose–Hubbard (HCBH) lattice. We generate superposition states by simultaneously driving all lattice sites and extract correlation lengths and entanglement entropy across its many-body energy spectrum. We observe volume-law entanglement scaling for states at the centre of the spectrum and a crossover to the onset of area-law scaling near its edges.
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To investigate the unsteady heat dissipation in accelerator superconducting coils insulated with porous ceramic insulation, two experimental mock-ups reproducing the thermal and the mechanical conditions of a superconducting coils were produced. The mock-ups with compressive load of 10 MPa and 20 MPa were tested at normal (T = 4.23 K and p = 1 bar) and supercritical helium conditions (T = 4.23 K and p = 2.0 to 3.75 bar) during unsteady heat dissipation. The paper presents the experimental results of temperature rise in both superconducting coils as a function of time for a wide range of a localized heat load varying from 0.1 kJ/m{sup 3} up to 12.8 MJ m{sup −3} per pulse. A numerical model of the transient process in these coils has been developed and the computations are compared with the experimental results.
Understanding noise in entangled systems is a prerequisite for developing scalable quantum computers. Here, we propose and apply multiqubit dynamical decoupling sequences that characterize noise during two-qubit gates. Furthermore, this noise is qualitatively different from the well-studied noise that leads to single-qubit dephasing; it simultaneously affects the two qubits, inducing fluctuations in their entangling parameter. In our superconducting system, the experimentally observed noise comes from coupler flux fluctuations and is observed to be non-Gaussian, leading to the stepwise decay of signals.
Abstract The trade-off between robustness and tunability is a central challenge in the pursuit of quantum simulation and fault-tolerant quantum computation. In particular, quantum architectures are often designed to achieve high coherence at the expense of tunability. Many current qubit designs have fixed energy levels and consequently limited types of controllable interactions. Here by adiabatically transforming fixed-frequency superconducting circuits into modifiable Floquet qubits, we demonstrate an XXZ Heisenberg interaction with fully adjustable anisotropy. This interaction model can act as the primitive for an expressive set of quantum operations, but is also the basis for quantum simulations of spin systems. To illustrate the robustness and versatility of our Floquet protocol, we tailor the Heisenberg Hamiltonian and implement two-qubit iSWAP, CZ and SWAP gates with good estimated fidelities. In addition, we implement a Heisenberg interaction between higher energy levels and employ it to construct a three-qubit CCZ gate, also with a competitive fidelity. Our protocol applies to multiple fixed-frequency high-coherence platforms, providing a collection of interactions for high-performance quantum information processing. It also establishes the potential of the Floquet framework as a tool for exploring quantum electrodynamics and optimal control.
Decoherence in superconducting qubits has long been attributed to two-level systems arising from the surfaces and interfaces present in real devices. A recent significant step in reducing decoherence was the replacement of superconducting niobium by superconducting tantalum, resulting in a tripling of transmon qubit lifetimes (T1). The identity, thickness, and quality of the native surface oxide, is thought to play a major role, as tantalum only has one oxide whereas niobium has several. Here we report the development of a thermodynamic metric to rank materials based on their potential to form a well-defined, thin, surface oxide. We first computed this metric for known binary and ternary metal alloys using data available from the Materials Project and experimentally validated the strengths and limits of this metric through the preparation and controlled oxidation of eight known metal alloys. Then we trained a convolutional neural network to predict the value of this metric from atomic composition and atomic properties. This allowed us to compute the metric for materials that are not present in the Materials Project, including a large selection of known superconductors, and, when combined with Tc, allowed us to identify new candidate superconductors for quantum information science and engineering (QISE) applications. We tested the oxidation resistance of a pair of these predictions experimentally. Our results are expected to lay the foundation for the tailored and rapid selection of improved superconductors for QISE.
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