When is better ground state preparation worthwhile for energy estimation?

Many quantum simulation tasks require preparing a state with overlap $\gamma$ relative to the ground state of a Hamiltonian of interest, such that the probability of computing the associated energy eigenvalue is upper bounded by $\gamma^2$. Amplitude amplification can increase $\gamma$, but the conditions under which this is more efficient than simply repeating the computation remain unclear. Analyzing Lin and Tong's near-optimal state preparation algorithm we show that it can reduce a proxy for the runtime of ground state energy estimation near quadratically. Resource estimates are provided for a variety of problems, suggesting that the added cost of amplitude amplification is worthwhile for realistic materials science problems under certain assumptions

Near-Minimal Gate Set Tomography Experiment Designs

Gate set tomography (GST) provides precise, self-consistent estimates of the noise channels for all of a quantum processor's logic gates. But GST experiments are large, involving many distinct quantum circuits. This has prevented their use on systems larger than two qubits. Here, we show how to streamline GST experiment designs by removing almost all redundancy, creating smaller and more scalable experiments without losing precision. We do this by analyzing the "germ" subroutines at the heart of GST circuits, identifying exactly what gate set parameters they are sensitive to, and leveraging this information to remove circuits that duplicate other circuits' sensitivities. We apply this technique to two-qubit GST experiments, generating streamlined experiment designs that contain only slightly more circuits than the theoretical minimum bounds, but still achieve Heisenberg-like scaling in precision (as demonstrated via simulation and a theoretical analysis using Fisher information). In practical use, the new experiment designs can match the precision of previous GST experiments with significantly fewer circuits. We discuss the prospects and feasibility of extending GST to three-qubit systems using our techniques.Comment: 11 pages, 6 figures, to be published in proceedings of 2023 IEEE International Conference on Quantum Computing and Engineering (QCE

Benchmarking quantum logic operations relative to thresholds for fault tolerance

Contemporary methods for benchmarking noisy quantum processors typically measure average error rates or process infidelities. However, thresholds for fault-tolerant quantum error correction are given in terms of worst-case error rates -- defined via the diamond norm -- which can differ from average error rates by orders of magnitude. One method for resolving this discrepancy is to randomize the physical implementation of quantum gates, using techniques like randomized compiling (RC). In this work, we use gate set tomography to perform precision characterization of a set of two-qubit logic gates to study RC on a superconducting quantum processor. We find that, under RC, gate errors are accurately described by a stochastic Pauli noise model without coherent errors, and that spatially-correlated coherent errors and non-Markovian errors are strongly suppressed. We further show that the average and worst-case error rates are equal for randomly compiled gates, and measure a maximum worst-case error of 0.0197(3) for our gate set. Our results show that randomized benchmarks are a viable route to both verifying that a quantum processor's error rates are below a fault-tolerance threshold, and to bounding the failure rates of near-term algorithms, if -- and only if -- gates are implemented via randomization methods which tailor noise

Two-Qubit Gate Set Tomography with Fewer Circuits

Gate set tomography (GST) is a self-consistent and highly accurate method for the tomographic reconstruction of a quantum information processor's quantum logic operations, including gates, state preparations, and measurements. However, GST's experimental cost grows exponentially with qubit number. For characterizing even just two qubits, a standard GST experiment may have tens of thousands of circuits, making it prohibitively expensive for platforms. We show that, because GST experiments are massively overcomplete, many circuits can be discarded. This dramatically reduces GST's experimental cost while still maintaining GST's Heisenberg-like scaling in accuracy. We show how to exploit the structure of GST circuits to determine which ones are superfluous. We confirm the efficacy of the resulting experiment designs both through numerical simulations and via the Fisher information for said designs. We also explore the impact of these techniques on the prospects of three-qubit GST.Comment: 46 pages, 13 figures. V2: Minor edits to acknowledgment

Consistency of high-fidelity two-qubit operations in silicon

The consistency of entangling operations between qubits is essential for the performance of multi-qubit systems, and is a crucial factor in achieving fault-tolerant quantum processors. Solid-state platforms are particularly exposed to inconsistency due to the materials-induced variability of performance between qubits and the instability of gate fidelities over time. Here we quantify this consistency for spin qubits, tying it to its physical origins, while demonstrating sustained and repeatable operation of two-qubit gates with fidelities above 99% in the technologically important silicon metal-oxide-semiconductor (SiMOS) quantum dot platform. We undertake a detailed study of the stability of these operations by analysing errors and fidelities in multiple devices through numerous trials and extended periods of operation. Adopting three different characterisation methods, we measure entangling gate fidelities ranging from 96.8% to 99.8%. Our analysis tools also identify physical causes of qubit degradation and offer ways to maintain performance within tolerance. Furthermore, we investigate the impact of qubit design, feedback systems, and robust gates on implementing scalable, high-fidelity control strategies. These results highlight both the capabilities and challenges for the scaling up of spin-based qubits into full-scale quantum processors

ChemVox: Voice-Controlled Quantum Chemistry

Over the last decade, artificial intelligence has been propelled forward by advances in machine learning algorithms and computational hardware, opening up myriad new avenues for scientific research. Nevertheless, virtual assistants and voice control have yet to be widely utilized in the natural sciences. Here, we present ChemVox, an interactive Amazon Alexa skill that uses speech recognition to perform quantum chemistry calculations. This new application interfaces Alexa with cloud computing and returns the results through a capable device. ChemVox paves the way to making computational chemistry routinely accessible to the wider communit

Voice-controlled quantum chemistry

Over the past decade, artificial intelligence has been propelled forward by advances in machine learning algorithms and computational hardware, opening up myriads of new avenues for scientific research. Nevertheless, virtual assistants and voice control have yet to be widely used in the natural sciences. Here, we present ChemVox, an interactive Amazon Alexa skill that uses speech recognition to perform quantum chemistry calculations. This new application interfaces Alexa with cloud computing and returns the results through a capable device. ChemVox paves the way to making computational chemistry routinely accessible to the wider community

InteraChem: Virtual Reality Visualizer for Reactive Interactive Molecular Dynamics

Interactive molecular dynamics in virtual reality (IMD-VR) simulations provide a digital molecular playground for students as an alternative or complement to traditional molecular modelling kits or 2D illustrations. Previous IMD-VR studies have used molecular mechanics to enable simulations of macromolecules such as proteins and nanostructures for theclassroom setting with considerable success. Here, we present the INTERACHEM molecular visualizer, intended for reactive IMD-VR simulation using semiempirical and ab initio methods.INTERACHEM visualizes not only the molecular geometry, but also 1) isosurfaces such as molecular orbitals and electrostatic potentials, and 2) two-dimensional graphs of time-varyingsimulation quantities such as kinetic/potential energy, internal coordinates, and user-applied force. Additionally, INTERACHEM employs speech recognition to facilitate user interaction and introduces a novel “atom happiness” visualization using emojis to indicate the energeticfeasibility of a particular bonding arrangement. We include a set of accompanying exercises that we have used to teach chemical reactivity in small molecular systems