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In this contribution we consider the recent computation of the gauge coupling $\beta$-function at four loops and the Yukawa matrix $\beta$-function at three loops in the most general, renormalizable and four-dimensional quantum field theory. Furthermore, we discuss ambiguities and divergences arising in Yukawa matrix $\beta$-functions.
Lattice gauge theory continues to be a powerful theoretical and computational approach to simulating strongly interacting quantum field theories, whose applications permeate almost all disciplines of modern-day research in High-Energy Physics. Whether it is to enable precision quark- and lepton-flavor physics, to uncover signals of new physics in nucleons and nuclei, to elucidate hadron structure and spectrum, to serve as a numerical laboratory to reach beyond the Standard Model, or to invent and improve state-of-the-art computational paradigms, the lattice-gauge-theory program is in a prime position to impact the course of developments and enhance discovery potential of a vibrant experimental program in High-Energy Physics over the coming decade. This projection is based on abundant successful results that have emerged using lattice gauge theory over the years: on continued improvement in theoretical frameworks and algorithmic suits; on the forthcoming transition into the exascale era of high-performance computing; and on a skillful, dedicated, and organized community of lattice gauge theorists in the U.S. and worldwide. The prospects of this effort in pushing the frontiers of research in High-Energy Physics have recently been studied within the U.S. decadal Particle Physics Planning Exercise (Snowmass 2021), and the conclusions are summarized in this Topical Report.
Quantum noise limits the use of quantum memory in high energy physics simulations. In particular, it breaks the gauge symmetry of stored quantum states. We examine this effect for abelian and nonabelian theories and demonstrate that optimizing the digitization of gauge theories to quantum memory to account for noise channels can extend the lifetime before complete loss of gauge symmetry by $2-10\times$ over some other digitizations. These constructions also allow for quantum error correction to integrate the symmetries of quantum fields and prioritize the largest gauge violations.
The Green's function Monte Carlo (GFMC) method provides accurate solutions to the nuclear many-body problem and predicts properties of light nuclei starting from realistic two- and three-body interactions. Controlling the GFMC fermion-sign problem is crucial, as the signal-to-noise ratio decreases exponentially with Euclidean time, requiring significant computing resources. Inspired by similar scenarios in lattice quantum field theory and spin systems, in this work, we employ integration contour deformations to improve the GFMC signal-to-noise ratio. Machine learning techniques are used to select optimal contours with minimal variance from parameterized families of deformations. As a proof of principle, we consider the deuteron binding energies and Euclidean density response functions. We only observe mild signal-to-noise improvement for the binding energy case. On the other hand, we achieve an order of magnitude reduction of the variance for Euclidean density response functions, paving the way for computing electron- and neutrino-nucleus cross-sections of larger nuclei.
The Muon $g-2$ Experiment operated at Fermi National Accelerator Laboratory (FNAL, or Fermilab) between 2018 and 2023 to produce the world's most precise measurement of the muon's \textit{anomalous magnetic moment}, $a_\mu = \frac{g_\mu - 2}{2}$, which expresses the relative deviation in the muon's $g$-factor from a baseline theoretical expectation that $g_\mu = 2$. In the Standard Model of particle physics, $g_\mu > 2$ and hence $a_\mu > 0$ by a calculable amount that depends on all possible interactions between the muon and all other fundamental particles, including any potentially undiscovered interactions beyond the Standard Model. For this reason, measurements of the electron anomaly $a_e$ and later the muon anomaly $a_\mu$ have helped guide the development of the Standard Model since the inception of quantum field theory, and the measured value of $a_\mu$ provides a valuable constraint for new hypotheses that extend the Standard Model. As of 2006, the leading measurement and Standard Model prediction for $a_\mu$ exhibited tension at the level of about three standard deviations, motivating an improved measurement at Fermilab that could test the tension more precisely. The experiment functions by storing a polarized beam of $\mu^+$ in a uniform magnetic field, which simultaneously induces circular motion and spin precession. As the stored muons undergo the Michel decay $\mu^+ \to e^+ + \nu_e + \bar{\nu}_\mu$, mediated by the parity-violating weak interaction, the rest-frame $e^+$ emission direction is correlated with the parent $\mu^+$ spin orientation. Boosting into the laboratory frame encodes this correlation in the decay $e^+$ energy, which is higher when the emission (i.e. $\mu^+$ spin direction) is aligned with the $\mu^+$ momentum, and lower when opposite. Detectors then count the rate of high-energy decay $e^+$, which modulates at the difference between the $\mu^+$ revolution and spin precession frequencies. This observed frequency, called the \textit{anomalous spin precession frequency} $\omega_a$, is directly proportional to $a_\mu$. The extraction of $\omega_a$ proceeds by fitting the time spectrum of detected $e^+$, which requires precise modeling of the $\omega_a$ oscillation as well as any perturbations from beam dynamics and detector acceptance. Using the $\omega_a$ analysis presented in this work, based on Runs 4 -- 6 of the Muon $g-2$ Experiment at Fermilab, we find that $a_\mu = 0.001\,165\,920\,738(162)$ with a relative uncertainty of 139 parts per billion.
The field of quantum metrology seeks to apply quantum techniques and/or resources to classical sensing approaches with the goal of enhancing the precision in the estimation of a parameter beyond what can be achieved with classical resources. Theoretically, the fundamental minimum uncertainty in the estimation of a parameter for a given probing state is bounded by the quantum Cramér-Rao bound. From a practical perspective, it is necessary to find physical measurements that can saturate this fundamental limit and to show experimentally that it is possible to perform measurements with the required precision to do so. Here we perform experiments that saturate the quantum Cramér-Rao bound for transmission estimation over a wide range of transmissions when probing the system under study with a continuous wave bright two-mode squeezed state. To properly take into account the imperfections in the generation of the quantum state, we extend our previous theoretical results to incorporate the measured properties of the generated quantum state. For our largest transmission level of 84%, we show a 62% reduction over the optimal classical protocol in the variance in transmission estimation when probing with a bright two-mode squeezed state with -8 dB of intensity-difference squeezing. Given that transmission estimation is an integral part of many sensing protocols, such as plasmonic sensing, spectroscopy, calibration of the quantum efficiency of detectors, etc., the results presented promise to have a significant impact on a number of applications in various fields of research.
Recent experimental advances in ultrafast phenomena have triggered renewed interest in the dynamics of correlated quantum systems away from equilibrium. We review non-equilibrium dynamical mean-field theory studies of both the transient and the steady states of a DC field-driven correlated quantum system. In particular, we focus on the non-equilibrium behavior and how it relates to the fluctuation-dissipation theorem. The fluctuation-dissipation theorem emerges as an indicator for how the system thermalizes and for how it reaches a steady state. When the system thermalizes in an infinite temperature steady state it can pass through a succession of quasi-thermal states that approximately obey the fluctuation-dissipation theorem. We also discuss the Wigner distribution and what its evolution tells us about the non-equilibrium many-body problem.
Field-effect transistors (FETs) made from colloidal quantum dot (QD) solids commonly suffer from current–voltage hysteresis caused by the bias-stress effect (BSE), which complicates fundamental studies of charge transport in QD solids and the use of QD FETs in electronics. Here, we show that the BSE can be eliminated in n-channel PbSe QD FETs by first removing the QD ligands with a dose of H 2 S gas and then infilling the QD films with alumina by atomic layer deposition (ALD). The H 2 S-treated, alumina-infilled FETs have stable, hysteresis-free device characteristics (total short-term stability), indefinite air stability (total long-term stability), and a high electron mobility of up to 14 cm 2 V −1 s −1 , making them attractive for QD circuitry and optoelectronic devices. The BSE-free devices are utilized to conclusively establish the dependence of the electron mobility on temperature and QD diameter. We demonstrate that the BSE in these devices is caused by both electron trapping at the QD surface and proton drift within the film. The H 2 S/alumina chemistry produces ligand-free PbSe/PbS/Al 2 O 3 interfaces that lack the traps that cause the electronic part of the BSE, while full alumina infilling stops the proton motion responsible for the ionic part of the BSE. In conclusion, our matrix engineering approach should aid efforts to eliminate the BSE, boost carrier mobilities, and improve charge transport in other types of nanocrystal solids.
Molecular qubits with the potential for optical read-out require careful ligand design to control zero-field splitting, D , for quantum manipulation. We find Ni 2+ in a sulfur ligand field in a near-ideal octahedral environment enables quantum control.
Drift field, field gradient and diffused impurity effects on minority carriers and quantum efficiency in silicon photocells
We propose an algorithm for computing real-time observables using a quantum processor while avoiding the need to prepare the full quantum state. This reduction in quantum resources is achieved by classically sampling configurations in imaginary-time using standard lattice field theory. These configurations can then be passed to quantum processor for time-evolution. This method encounters a signal-to-noise problem which we characterize, and we demonstrate the application of standard lattice QCD methods to mitigate it.
The HST/Wide Field Camera (WFC) 3 UV/visible channel CCD detectors have exhibited an unanticipated quantum efficiency hysteresis (QEH) behavior. At the nominal operating temperature of -83C, the QEH feature contrast was typically 0.1-0.2% or less. The behavior was replicated using flight spare detectors. A visible light flat-field (540nm) with a several times full-well signal level can pin the detectors at both optical (600nm) and near-UV (230nm) wavelengths, suppressing the QEH behavior. We are characterizing the timescale for the detectors to become unpinned and developing a protocol for flashing the WFC3 CCDs with the instrument's internal calibration system in flight. The HST/Wide Field Camera 3 UV/visible channel CCD detectors have exhibited an unanticipated quantum efficiency hysteresis (QEH) behavior. The first observed manifestation of QEH was the presence in a small percentage of flat-field images of a bowtie-shaped contrast that spanned the width of each chip. At the nominal operating temperature of -83C, the contrast observed for this feature was typically 0.1-0.2% or less, though at warmer temperatures contrasts up to 5% (at -50C) have been observed. The bowtie morphology was replicated using flight spare detectors in tests at the GSFC Detector Characterization Laboratory by power cycling the detector while cold. Continued investigation revealed that a clearly-related global QE suppression at the approximately 5% level can be produced by cooling the detector in the dark; subsequent flat-field exposures at a constant illumination show asymptotically increasing response. This QE "pinning" can be achieved with a single high signal flat-field or a series of lower signal flats; a visible light (500-580nm) flat-field with a signal level of several hundred thousand electrons per pixel is sufficient for QE pinning at both optical (600nm) and near-UV (230nm) wavelengths. We are characterizing the timescale for the detectors to become unpinned and developing a protocol for flashing the WFC3 CCDs with the instrument's internal calibration system in flight. A preliminary estimate of the decay timescale for one detector is that a drop of 0.1-0.2% occurs over a ten day period, indicating that relatively infrequent cal lamp exposures can mitigate the behavior to extremely low levels.
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Quantum algorithms for simulating electronic ground states are slower than popular classical mean-field algorithms such as Hartree–Fock and density functional theory but offer higher accuracy. Accordingly, quantum computers have been predominantly regarded as competitors to only the most accurate and costly classical methods for treating electron correlation. However, here we tighten bounds showing that certain first-quantized quantum algorithms enable exact time evolution of electronic systems with exponentially less space and polynomially fewer operations in basis set size than conventional real-time time-dependent Hartree–Fock and density functional theory. Although the need to sample observables in the quantum algorithm reduces the speedup, we show that one can estimate all elements of the k-particle reduced density matrix with a number of samples scaling only polylogarithmically in basis set size. We also introduce a more efficient quantum algorithm for first-quantized mean-field state preparation that is likely cheaper than the cost of time evolution. We conclude that quantum speedup is most pronounced for finite-temperature simulations and suggest several practically important electron dynamics problems with potential quantum advantage.
Perhaps the most basic question we can ask about cosmological correlations is how their strength changes as we smoothly vary kinematic parameters. The answer is encoded in differential equations that govern this evolution in kinematic space. In this Letter, we introduce a new perspective on these differential equations. We show that, in the simplified setting of conformally coupled scalars in power-law Friedmann-Robertson-Walker spacetimes, the equations for arbitrary tree-level processes can be obtained from a small number of simple combinatorial rules. While this “kinematic flow” is defined purely in terms of boundary data, it reflects the physics of bulk time evolution. The unexpected regularity of the equations suggests the existence of an autonomously defined mathematical structure from which cosmological correlations and the time evolution of the associated spacetime emerge.
In this report CsYbSe 2 , a recently identified quantum spin liquid (QSL) candidate, exhibits strong crystal electric field (CEF) excitations. Here, we identify phonon and CEF modes with Raman spectroscopy and observe strong CEF-phonon coupling resulting in a vibronic bound state. Complex, mesoscale interplay between phonon modes and CEF modes is observed in real space. Additionally, an unexpected resonant Raman excitation condition is satisfied, yielding up to third-order combination modes, with a total of 17 modes identified in the spectra. This study paves the way to coherent control of possible QSL ground states with optically accessible CEF-phonon manifolds and mesoscale engineering of CEF-phonon interactions.
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, its success usually hinges on efficiently training the parameters in quantum neural networks, and the field of QML is still lacking theoretical scaling results for their trainability. Some trainability results have been proven for a closely related field called variational quantum algorithms (VQAs). While both fields involve training a parametrized quantum circuit, there are crucial differences that make the results for one setting not readily applicable to the other. In this work, we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models. Our results indicate that features deemed detrimental for VQA trainability can also lead to issues such as barren plateaus in QML. Consequently, our work has implications for several QML proposals in the literature. In addition, we provide theoretical and numerical evidence that QML models exhibit further trainability issues not present in VQAs, arising from the use of a training dataset. We refer to these as dataset-induced barren plateaus. These results are most relevant when dealing with classical data, as here the choice of embedding scheme (i.e., the map between classical data and quantum states) can greatly affect the gradient scaling.
A local-molecular-field description for the ground-state properties of the quantum solids is presented. The dynamical behavior of atoms contributing to the local field, which acts on an arbitrary pair of test particles, is incorporated by decoupling the pair correlations between these field atoms. The energy, pressure, compressibility, single-particle-distribution function, and the rms atomic deviations about the equilibrium lattice sites are calculated for H2, He-3, and He-4 over the volume range from 5 to 24.5 cu cm/mole. The results are in close agreement with existing Monte Carlo calculations wherever comparisons are possible. At very high pressure, the results agree with simplified descriptions which depend on negligible overlap of the system wave function between neighboring lattice sites.