When linear regression generates a relationship between a (dependent) scalar response and one or multiple independent variables, various datasets providing distinct graphical trends can develop resembling relationships based on the same statistical p
roperties. Advanced statistical approaches, such as neural networks and machine learning methods, are of great necessity to process, characterize, and analyze these degenerate datasets. On the other hand, the accurate creation of purposedly degenerate datasets is essential to test new models in the research and education of applied statistics. In this light, the present study characterizes the famous Anscombe datasets and provides a general algorithm for creating multiple paired datasets of identical statistical properties.
Quantum computers promise to enhance machine learning for practical applications. Quantum machine learning for real-world data has to handle extensive amounts of high-dimensional data. However, conventional methods for measuring quantum kernels are i
mpractical for large datasets as they scale with the square of the dataset size. Here, we measure quantum kernels using randomized measurements to gain a quadratic speedup in computation time and quickly process large datasets. Further, we efficiently encode high-dimensional data into quantum computers with the number of features scaling linearly with the circuit depth. The encoding is characterized by the quantum Fisher information metric and is related to the radial basis function kernel. We demonstrate the advantages of our methods by classifying images with the IBM quantum computer. To achieve further speedups we distribute the quantum computational tasks between different quantum computers. Our approach is exceptionally robust to noise via a complementary error mitigation scheme. Using currently available quantum computers, the MNIST database can be processed within 220 hours instead of 10 years which opens up industrial applications of quantum machine learning.
Noisy intermediate scale quantum computers are useful for various tasks including quantum state preparation, quantum metrology and variational quantum algorithms. However, the non-euclidean quantum geometry of parameterized quantum circuits is detrim
ental for these applications. Here, we introduce the natural parameterized quantum circuit (NPQC) with a euclidean quantum geometry. The initial training of variational quantum algorithms is substantially sped up as the gradient is equivalent to the quantum natural gradient. NPQCs can also be used as highly accurate multi-parameter quantum sensors. For a general class of quantum circuits, the NPQC has the minimal quantum Cramer-Rao bound. We provide an efficient sensing protocol that only requires sampling in the computational basis. Finally, we show how to generate tailored superposition states without training. These applications can be realized for any number of qubits with currently available quantum processors.
Recently, a new interpretation of quantum mechanics has been developed for the wave nature of a photon, where determinacy in quantum correlations becomes an inherent property without the violation of quantum mechanics. Here, we experimentally demonst
rate a direct proof of the wave natures of quantum correlation for the so-called coherence de Broglie waves (CBWs) using sub-Poisson distributed coherent photon pairs obtained from an attenuated laser. The observed experimental data coincides with the analytic solutions and the numerical calculations. Thus, the CBWs pave a road toward deterministic and macroscopic quantum technologies for such as quantum metrology, quantum sensing, and even quantum communications, that are otherwise heavily limited due to the microscopic non-determinacy of the particle nature-based quantum mechanics.
The purpose of this document is to study the security properties of the Silver Bullet algorithm against worst-case RowHammer attacks. We mathematically demonstrate that Silver Bullet, when properly configured and implemented in a DRAM chip, can secur
ely prevent RowHammer attacks. The demonstration focuses on the most representative implementation of Silver Bullet, the patent claiming many implementation possibilities not covered in this demonstration. Our study concludes that Silver Bullet is a promising RowHammer prevention mechanism that can be configured to operate securely against RowHammer attacks at various efficiency-area tradeoff points, supporting relatively small hammer count values (e.g., 1000) and Silver Bullet table sizes (e.g., 1.06KB).
To operate efficiently across a wide range of workloads with varying power requirements, a modern processor applies different current management mechanisms, which briefly throttle instruction execution while they adjust voltage and frequency to accom
modate for power-hungry instructions (PHIs) in the instruction stream. Doing so 1) reduces the power consumption of non-PHI instructions in typical workloads and 2) optimizes system voltage regulators cost and area for the common use case while limiting current consumption when executing PHIs. However, these mechanisms may compromise a systems confidentiality guarantees. In particular, we observe that multilevel side-effects of throttling mechanisms, due to PHI-related current management mechanisms, can be detected by two different software contexts (i.e., sender and receiver) running on 1) the same hardware thread, 2) co-located Simultaneous Multi-Threading (SMT) threads, and 3) different physical cores. Based on these new observations on current management mechanisms, we develop a new set of covert channels, IChannels, and demonstrate them in real modern Intel processors (which span more than 70% of the entire client and server processor market). Our analysis shows that IChannels provides more than 24x the channel capacity of state-of-the-art power management covert channels. We propose practical and effective mitigations to each covert channel in IChannels by leveraging the insights we gain through a rigorous characterization of real systems.
Variational quantum algorithms (VQAs) promise efficient use of near-term quantum computers. However, training VQAs often requires an extensive amount of time and suffers from the barren plateau problem where the magnitude of the gradients vanishes wi
th increasing number of qubits. Here, we show how to optimally train VQAs for learning quantum states. Parameterized quantum circuits can form Gaussian kernels, which we use to derive adaptive learning rates for gradient ascent. We introduce the generalized quantum natural gradient that features stability and optimized movement in parameter space. Both methods together outperform other optimization routines in training VQAs. Our methods also excel at numerically optimizing driving protocols for quantum control problems. The gradients of the VQA do not vanish when the fidelity between the initial state and the state to be learned is bounded from below. We identify a VQA for quantum simulation with such a constraint that thus can be trained free of barren plateaus. Finally, we propose the application of Gaussian kernels for quantum machine learning.
We propose magnetically arrested disks (MADs) in quiescent black-hole (BH) binaries as the origin of the multiwavelength emission, and argue that this class of sources can dominate the cosmic-ray spectrum around the knee. X-ray luminosities of Galact
ic BH binaries in the quiescent state are far below the Eddington luminosity, and thus, radiatively inefficient accretion flows (RIAFs) are formed in the inner region. Strong thermal and turbulent pressures in RIAFs produce outflows, which can create large-scale poloidal magnetic fields. These fields are carried to the vicinity of the BH by the rapid inflow motion, forming a MAD. Inside the MAD, non-thermal protons and electrons are naturally accelerated by magnetic reconnections or stochastic acceleration by turbulence. Both thermal and non-thermal electrons emit broadband photons via synchrotron emission, which are broadly consistent with the optical and X-ray data of the quiescent BH X-ray binaries. Moreover, protons are accelerated up to PeV energies and diffusively escape from these MADs, which can account for the cosmic-ray intensity around the knee energy.
We propose a novel scenario for possible electromagnetic (EM) emission by compact binary mergers in the accretion disks of active galactic nuclei (AGNs). Nuclear star clusters in AGNs are a plausible formation site of compact-stellar binaries (CSBs)
whose coalescences can be detected through gravitational waves (GWs). We investigate the accretion onto and outflows from CSBs embedded in AGN disks. We show that these outflows are likely to create outflow cavities in the AGN disks before the binaries merge, which makes EM or neutrino counterparts much less common than would otherwise be expected. We discuss the necessary conditions for detectable EM counterparts to mergers inside the outflow cavities. If the merger remnant black hole experiences a high recoil velocity and can enter the AGN disk, it can accrete gas with a super-Eddington rate, newly forming a cavity-like structure. This bubble can break out of the disk within a day to a week after the merger. Such breakout emission can be bright enough to be detectable by current soft X-ray instruments, such as Swift-XRT and Chandra.
Aggressive memory density scaling causes modern DRAM devices to suffer from RowHammer, a phenomenon where rapidly activating a DRAM row can cause bit-flips in physically-nearby rows. Recent studies demonstrate that modern DRAM chips, including chips
previously marketed as RowHammer-safe, are even more vulnerable to RowHammer than older chips. Many works show that attackers can exploit RowHammer bit-flips to reliably mount system-level attacks to escalate privilege and leak private data. Therefore, it is critical to ensure RowHammer-safe operation on all DRAM-based systems. Unfortunately, state-of-the-art RowHammer mitigation mechanisms face two major challenges. First, they incur increasingly higher performance and/or area overheads when applied to more vulnerable DRAM chips. Second, they require either proprietary information about or modifications to the DRAM chip design. In this paper, we show that it is possible to efficiently and scalably prevent RowHammer bit-flips without knowledge of or modification to DRAM internals. We introduce BlockHammer, a low-cost, effective, and easy-to-adopt RowHammer mitigation mechanism that overcomes the two key challenges by selectively throttling memory accesses that could otherwise cause RowHammer bit-flips. The key idea of BlockHammer is to (1) track row activation rates using area-efficient Bloom filters and (2) use the tracking data to ensure that no row is ever activated rapidly enough to induce RowHammer bit-flips. By doing so, BlockHammer (1) makes it impossible for a RowHammer bit-flip to occur and (2) greatly reduces a RowHammer attacks impact on the performance of co-running benign applications. Compared to state-of-the-art RowHammer mitigation mechanisms, BlockHammer provides competitive performance and energy when the system is not under a RowHammer attack and significantly better performance and energy when the system is under attack.
