The foundations of quantum mechanics
Theory of quantum mechanics, measuring apparatus, observation, and operators
QUANTUM MECHANICS↗
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Third International Workshop on Squeezed States and Uncertainty Relations
The purpose of these workshops is to bring together an international selection of scientists to discuss the latest developments in Squeezed States in various branches of physics, and in the understanding of the foundations of quantum mechanics. At the third workshop, special attention was given to the influence that quantum optics is having on our understanding of quantum measurement theory. The fourth meeting in this series will be held in the People's Republic of China.
Han, D.↗
Fourth International Conference on Squeezed States and Uncertainty Relations
The fourth International Conference on Squeezed States and Uncertainty Relations was held at Shanxi University, Taiyuan, Shanxi, China, on June 5 - 9, 1995. This conference was jointly organized by Shanxi University, the University of Maryland (U.S.A.), and the Lebedev Physical Institute (Russia). The first meeting of this series was called the Workshop on Squeezed States and Uncertainty Relations, and was held in 1991 at College Park, Maryland. The second and third meetings in this series were hosted in 1992 by the Lebedev Institute in Moscow, and in 1993 by the University of Maryland Baltimore County, respectively. The scientific purpose of this series was initially to discuss squeezed states of light, but in recent years, the scope is becoming broad enough to include studies of uncertainty relations and squeeze transformations in all branches of physics, including, of course, quantum optics and foundations of quantum mechanics. Quantum optics will continue playing the pivotal role in the future, but the future meetings will include all branches of physics where squeeze transformations are basic transformation. This transition took place at the fourth meeting of this series held at Shanxi University in 1995. The fifth meeting in this series will be held in Budapest (Hungary) in 1997, and the principal organizer will be Jozsef Janszky of the Laboratory of Crystal Physics, P.O. Box 132, H-1052. Budapest, Hungary.
Han, D.↗
Fifth International Conference on Squeezed States and Uncertainty Relations
The Fifth International Conference on Squeezed States and Uncertainty Relations was held at Balatonfured, Hungary, on 27-31 May 1997. This series was initiated in 1991 at the College Park Campus of the University of Maryland as the Workshop on Squeezed States and Uncertainty Relations. The scientific purpose of this series was to discuss squeezed states of light, but in recent years the scope is becoming broad enough to include studies of uncertainty relations and squeeze transformations in all branches of physics including quantum optics and foundations of quantum mechanics. Quantum optics will continue playing the pivotal role in the future, but the future meetings will include all branches of physics where squeeze transformations are basic. As the meeting attracted more participants and started covering more diversified subjects, the fourth meeting was called an international conference. The Fourth International Conference on Squeezed States and Uncertainty Relations was held in 1995 was hosted by Shanxi University in Taiyuan, China. The fifth meeting of this series, which was held at Balatonfured, Hungary, was also supported by the IUPAP. In 1999, the Sixth International Conference will be hosted by the University of Naples in 1999. The meeting will take place in Ravello near Naples.
Han, D.↗
Deriving Laws from Ordering Relations
The effect of Richard T. Cox's contribution to probability theory was to generalize Boolean implication among logical statements to degrees of implication, which are manipulated using rules derived from consistency with Boolean algebra. These rules are known as the sum rule, the product rule and Bayes Theorem, and the measure resulting from this generalization is probability. In this paper, I will describe how Cox s technique can be further generalized to include other algebras and hence other problems in science and mathematics. The result is a methodology that can be used to generalize an algebra to a calculus by relying on consistency with order theory to derive the laws of the calculus. My goals are to clear up the mysteries as to why the same basic structure found in probability theory appears in other contexts, to better understand the foundations of probability theory, and to extend these ideas to other areas by developing new mathematics and new physics. The relevance of this methodology will be demonstrated using examples from probability theory, number theory, geometry, information theory, and quantum mechanics.
Knuth, Kevin H.↗
Relativistic astrophysics - The view from Texas in Baltimore /Review/
Recent observational and theoretical work presented at the Tenth Texas Symposium on Relativistic Astrophysics held in Baltimore, Maryland from December 15-19, 1980, is outlined. Areas covered include the theoretical foundations of relativistic astrophysics in general relativity, quantum gravitational theory and the association of grand unification with astronomical and cosmological issues, the cosmic microwave, X-ray, gamma-ray, UV, cosmic ray and gravitational wave backgrounds, the current expansion rate and average mass-energy density of the universe, and mechanisms of galaxy formation. Also discussed are the characteristics of active galaxies and clusters emitting in the gamma-ray and X-ray regions, and compact objects formed from supernova explosions, including pulsars, X-ray-emitting neutron stars, Sco X-1 and SS 433, gamma-ray sources, and X-ray and gamma-ray bursters.
Trimble, V. L.↗
Two-photon interferometry for high-resolution imaging
We discuss advantages of using non-classical states of light for two aspects of optical imaging: creating of miniature images on photosensitive substrates, which constitutes the foundation for optical lithography, and conversely, imaging of micro objects. In both cases, the classical resolution limit given by the Rayleigh criterion is approximately a half of the optical wavelength. It has been shown, however, that by using multi-photon quantum states of the light field, and multi-photon sensitive material or detector, this limit can be surpassed. In the present work, we give a rigorous quantum mechanical treatment of this problem, address some particularly widespread misconceptions and discuss the requirements arising on the way of turning the research on quantum imaging into a practical technology.
quantum technology↗
Exploring Divisibility and Summability of 'Photon' Wave Packets in Nonlinear Optical Phenomena
Formulations for second and higher harmonic frequency up and down conversions, as well as multi photon processes directly assume summability and divisibility of photons. Quantum mechanical (QM) interpretations are completely congruent with these assumptions. However, for linear optical phenomena (interference, diffraction, refraction, material dispersion, spectral dispersion, etc.), we have a profound dichotomy. Most optical engineers innovate and analyze all optical instruments by propagating pure classical electromagnetic (EM) fields using Maxwell s equations and gives only lip-service to the concept "indivisible light quanta". Further, irrespective of linearity or nonlinearity of the phenomena, the final results are always registered through some photo-electric or photo-chemical effects. This is mathematically well modeled by a quadratic action (energy absorption) relation. Since QM does not preclude divisibility or summability of photons in nonlinear & multi-photon effects, it cannot have any foundational reason against these same possibilities in linear optical phenomena. It implies that we must carefully revisit the fundamental roots behind all light-matter interaction processes and understand the common origin of "graininess" and "discreteness" of light energy.
Prasad, Narasimha↗
Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders
Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.
quantum machine learning↗