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We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.
We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024
Correlations between electrons have brought about some of the most celebrated discoveries in quantum materials research, including all forms of superconductivity, the fractional quantum hall effect, and giant magnetoresistance. A more recent development has been the discovery that the topological aspects of electrons are important, producing new states of matter like topological insulators, and Dirac and Weyl fermions. This conference will bring together experts from both the "correlated" and "topological" communities to explore future directions that merge both fields, including emergent physics in the flat bands of twisted bilayer systems, and new routes to quantum computation using topological superconductors and spin liquids. We hope to come away with new research questions, and new material synthesis directions, that will strongly overlap with the Basic Energy Sciences mission to support the fundamental experimental and theoretical quantum materials research.
This final report summarizes the team's efforts to develop a multiscale modeling approach that ranges from different levels of ab-initio quantum chemistry simulations to effective models and time-dependent external control, and to use this approach to systematically design quantum information processors with TbPc 2 single-molecule magnets. The impact of the work is two-fold: (i) New quantum chemistry simulation techniques capable of treating complex, multiscale problems such as the TbPc 2 molecule were developed, and (ii) the prospects for building quantum processors based on single-molecule magnets coupled by superconducting transmission line resonators were analyzed. The outcomes of this project revealed that current technology is at the cusp of being able to realize the main components of such a processor, and they highlighted the need to achieve stronger molecule-resonator interactions to enhance the viability of this approach. The multiscale modeling techniques developed during this project are general and transferable to other molecules and will thus have a broad impact on the field of quantum chemistry. Methods for controlling and simulating many coupled qubits developed here will also impact other quantum information technologies.
This research presents an innovative approach to improving theSignalSignal-to -Noise Ratio (SNR) in superconducting quantumcomputing readout signals, utilizing adaptive filtering on thecontrol board. Our solution FPGA FPGA-based aims to bolsterquantum computations' reliability by providing a more accurateand precise way to obtain our readout traces and achieve ahigher discernability between excitation states in real real-time.
A recently completed LDRD project has provided a new facility at Sandia for testing effects of energetic neutrons on electronic components. 14 MeV neutrons are produced with a deuterium ion beam onto a thin-film tritide target. The goal of the project was to increase the neutron fluence to levels needed for radiation effects testing and qualification. This goal was achieved through two technical advances. First, a new multi-layer target concept was developed to reduce the rate of tritium loss from the target by isotope exchange, thereby reducing tritium usage and increasing target lifetime. The second advance was the construction of a new test chamber designed to maximize neutron flux at test locations. Together, these increased the available neutron fluence by several orders of magnitude. This new capability is being used in tests for Sandia nuclear weapon programs, evaluation of commercial parts such as highly-scaled CMOS SRAM lCs, and tests of new devices under development at Sandia such as lll-V HBTs, gallium nitride high-voltage diodes, and for fundamental studies of physical mechanisms of device failure.
Abstract not provided.
Ionizing radiation in the form of $α$, $β$, $γ$, and additional high-energy particles can induce decoherence via phonon and quasiparticle poisoning in superconducting qubits. Recent studies have explored this effect using cosmic rays or controlled radioactive sources held in the proximity of a qubit package, and have concluded that reductions in such ``external'' environmental radiation may benefit stable operation of qubit devices. However, the effect of long-lived, unstable daughters of $^{222}$Rn that ``plate out'' directly on device and packaging surfaces has not been as extensively explored. This plate-out process, well-known to the dark matter direct detection field, occurs throughout the fabrication and testing lifecycle of a device and (separately) its packaging, and produces a local source of $α$-decays which can remain active for decades. As this scales with chip area, understanding and managing this source of ionizing radiation is relevant for successfully scaling quantum computing architectures to larger numbers of qubits in a radiation-robust way. We present a setup capable of accelerating and enhancing radon daughter plateout by a factor of $7\times10^4$ over ambient, in order to study, \textit{in situ}, the impact of these events on superconducting qubits. We also provide outlook on the potential impact of this source of ionizing radiation on current and future qubit arrays.
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.
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.
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.
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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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.