Qianqian Fang’s Post

Post-Temporal Physics: Ledgeral Time and the Collapse of Relativistic Continuity I am pleased to announce my advancement in theoretical physics. The empirical falsification of relativistic continuity through the Structural Mandate Test (SMT) in the real-world. Pending broader verification. I operationalized the core principles via the Structural Mandate Test, a rigorous computational experiment that contrasts two structural models applied to identical deterministic workloads. The State Integrity Check models continuity as redundant validations, imposing a 5x overhead to simulate temporal coherence, while the Virtual Permission Pool embodies ledgeral recursion with succinct admissibility checks, requiring only 1.1x the base effort. Across 50 ensemble runs, with nanosecond-precision timing, cryptographic hashing for equivalence verification, and paired bootstrap statistics yielding a p-value of 2.12e-115, the Virtual Permission Pool consistently demonstrated a 4.55x efficiency advantage over the State Integrity Check, aligning with the theoretical ratio derived from the admissibility functional. Functional outputs remained identical, confirming that the performance differential arises solely from the mandates themselves, not from software artifacts or hardware variability. The SMT isn't a tautological benchmark it's a precise operationalization of the theory's core postulate that continuity is procedural redundancy, while ledgeral recursion achieves tau-closure with minimal overhead. As i detail in the experiment file and echo in the central thesis is that existence itself is contingent upon recursive admissibility, the experiment falsifies continuity's necessity by showing deterministic equivalence at a predicted efficiency ratio. The ~4.5× differential isn't engineered; it's the macroscopic signature of how admissibility prunes redundant validations (SIC's R=4 modeling continuity's infinite regress) versus local closure (VPP's α=0.1 as the fractional cost of succinct verification). This maps directly to the admissibility functional Λ(s, h) → {0,1}, where survival is multiplicative across recursion depth τ. In computational terms, SIC enforces global coherence maintenance (analogous to the infinite regress of interval verification), demanding redundant executions to mimic continuity's flow. VPP, by contrast, operationalizes tau-local recursion: the admissibility kernel A(α × W) confines verification to a small fraction, yielding the theoretical R_theory = (1 + R_SIC) / (1 + α_VPP) ≈ 4.545. This isn't circular it's derived from the variational principle of maximal admissibility, where paths extremize the survival functional J = ∏ a(s, h), and obstruction Ω biases against redundancy. The experiment verifies this by isolating the structural mandate: identical kernels, hashes, and linearity (R² ≈1), but divergent timings due to recursion's superiority. This was only the opening salvo; A very long road lies ahead and there is so much left to do.

I was doing homework last week and realized there are some concepts from quantum mechanics that can be related to concepts in digital signal processing. Here are a few of my favorites! Heisenberg Uncertainty Principle  - The more you know about a particle’s position, the less you know about its momentum. You can’t measure both with infinite precision. - In DSP, the more you know about a signal in the frequency domain, the less you know in the time domain. You also can’t measure both with infinite precision. Hilbert Spaces - Infinite-dimension vector spaces that have an inner product. - The state vectors of a quantum system can be represented in a Hilbert space, as can the basis functions (sinusoids, wavelets, etc.) of a signal. - These systems can be decomposed into superpositions of their respective state vectors/basis functions. Operators - Quantum states have observables that are linear operators. - Signals have filters that are also linear operators. Wigner Distribution - Originally from quantum mechanics. - The same function can describe aspects of positions and momentum for a quantum system or time and frequency for a signal. There’s also of course the Fourier transform and a ton of math from linear algebra that can be used in both areas. What do you think? Am I way off on my observations? Did I miss something else? Let me know in the comments! #opentowork #digitalsignalprocessing #physics #engineeringstudent #learning 

Linear algebra theories really come in handy when you're dealing with problems in engineering. We proposed a new matrix for the dense mutual coupling matrices by structuring them as a symmetric Toeplitz matrix. We also designed elements of this structured matrix using the reflection coefficient and phase angle, emphasizing self-coupling and mutual coupling caused by attenuation, based on S-parameters at 1.4 GHz. This new matrix structure, theories, and algorithms led us to uncouple the mutual coupling effects in an arbitrary number of antenna arrays, having each array with n elements, using a super-fast algorithm, i.e., cutting down the complexity from O(n^3) to O(n log n). Plus, this algorithm can be used to invert symmetric Toeplitz matrices or solve related systems with the same O(n log n) efficiency, rather than relying on brute-force O(n^3) approaches. Even if we use the inverse of Toeplitz as T-Bezoutian, we can solve these systems or invert these matrices only at O(n^2). So, our new algorithm really goes beyond these complexity limits. In the end, we put our algorithm to the test against some Python libraries that handle Toeplitz system solvers and inversion. We got to see that our algorithm performs incredibly well, especially when using GPU acceleration in PyTorch, all while keeping accuracy. Check out our paper called "A Low-complexity Algorithm to Digitally Uncouple the Mutual Coupling Effect in Antenna Arrays via Symmetric Toeplitz Matrices" by Sirani M. Perera, Levi Lingsch, Arjuna Madanayake, and Leo Belostotski, and you can find it in the Journal of Computational and Applied Mathematics through https://lnkd.in/dHEvUEtV We appreciate the NSF's ECCS directory for the funding support provided to develop novel FFT-like algorithms for the convergence of research in multiple disciplines. #Fast #Algorithms #MutualCoupling #UncouplingArrays #StructuredMatrices #SymmetricToeplitz #AppliedMath #Engineering #ERAU #FIU #ETHZurich #UnivCalgary

Quantum Probe Tomography Identifies Many-Body Hamiltonians with Local Probes and Algebraic Geometry Tools Researchers have developed a new method, termed probe tomography, that accurately reconstructs the complete properties of complex physical systems by analysing data obtained from observing only a small, local part of the system as it evolves over time. #quantum #quantumcomputing #technology https://lnkd.in/eJH2zX8c

🤔 How are #computational aspects of #cryptography reflected in #Quantum #Information #Processing tasks, and relatedly, within #Mathematical #Physics, in general? As someone who has approached #Quantum #Information #theory from other areas of #Mathematics, predominantly from, * Integrability and exact solvability (6-vertex model: https://lnkd.in/g-HDZ_Dj , 20-vertex model: https://lnkd.in/g28AQymc), * Inverse scattering methods (for the 6-vertex, and 20-vertex, models alike: https://lnkd.in/gBSHHAZV), it is interesting to determine, rigorously, how exactly solvable structures (such as through the Yang-Baxter gate and several other related objects), can be leveraged for ensuring that prospective Quantum advantage holds. With far fewer near terms applications found in the #NISQ era than expected, undoubtedly there is more pressure on leading commercial provides (ie, #Google, #IBM, etc) to develop mitigation techniques through #error #correcting #codes. While many of these #corporate entities have set forth timelines, many by #2030, it remains to be seen as to whether significant improvement in performance can be achieved within that time. With regard to applications in #cryptography, some general techniques from #Quantum #Information #Processing come into play, including: 1) Determining whether any underlying #symmetries exist in the system (as examined in https://lnkd.in/g5VvT8rb through suitable collections of dependency-breaking variables), 2) Establishing connections with other #Quantum #algorithms which have #polynomial #runtime (which I have discussed in https://lnkd.in/gcrFnyjT, https://lnkd.in/gmPryEgb, https://lnkd.in/ggaPaheR, and https://lnkd.in/gAguHS5a). 3) Performing computations of "suitably" chosen #observables (which, most recently, was done in an arXiv preprint with #parity #check #matrices, which I have discussed in https://lnkd.in/g2RKdVP5). While the list of possible ways to approach #Quantum #Information #Processing tasks, within the field of #Mathematical #Physics touches upon many objectives that "pure play" #Quantum companies these days are trying to achieve, it is by no means exhaustive. There are many interesting #Mathematical #research directions that are of interest to explore, possibly by generalizing aspects associated with the above remarks. #opentowork #work #recession #unemployed #economy #job #market #jobs #employment #need #work #working #labor #sector #interest #rates #borrowing #lending #monetary #policy #quantum #supremacy #nearterm #NISQ #information #theory

Code Swendsen-Wang Dynamics Achieves Rapid Mixing for 4D Toric Code Gibbs States at Any Temperature Researchers have developed a new computational method, inspired by a technique used in statistical physics, that efficiently prepares quantum states for a broad range of systems, including complex materials, and demonstrably works even at very low temperatures where previous methods failed. #quantum #quantumcomputing #technology https://lnkd.in/ey2Cp2Uw

🚀 Excited to kick off hands-on work with my BSc students on our machine learning project for automated steel microstructure classification! After successfully building our first image classifier using Fast.ai (achieving 92% accuracy on mosquito species identification as a learning exercise, wow), we're now heading into the metallography lab to collect real microscopy data from different steel samples. The goal is to develop an ML pipeline that can automatically distinguish steel microstructures. Students will get experience across the full workflow: sample preparation, optical microscopy, image processing, and implementing CNNs for materials characterization. Grateful that the students chose this project despite having many options (this year we have more projects than students I think), and looking forward to seeing what we build together over the semester. #MaterialsScience #MachineLearning #Teaching #Microscopy #ETHZurich #SteelMetallurgy Picture: artwork by the students 🎨

🔍 Signal processing doesn’t have to be abstract.  A new interactive webpage is now live to help visualize core concepts in applied physics and signal processing—no signup, no fluff.  Explore live simulations of:   ✅ Fourier analysis (sine, square, triangle, sawtooth waves)   ✅ Filtering (low-pass, high-pass, and more)   ✅ AM/FM modulation   ✅ The Nyquist–Shannon sampling theorem (with aliasing alerts!)  Perfect for students, educators, or anyone curious about how signals shape our digital world.  👉 Try it here: https://lnkd.in/d--fBNCX #SignalProcessing #Physics #STEM #Engineering #FourierTransform #DSP #InteractiveLearning #Education #MathVisualization

QUANTUM DISSIPATION ENGINEERING (What's new about DIRAC Bracket*) https://lnkd.in/e9MJTpkT Dynamics Beyond the Hamiltonian: Dissipation in Classical Metriplectic Systems and Quantum Non-Unitary Systems Frédéric Barbaresco (Thales) & Massimo Materassi (Istituto Dei Sistemi Complessi) https://lnkd.in/eAXuJtt4 (*)The Dirac bracket is a generalization of the Poisson bracket to treat classical systems with second class constraints in Hamiltonian mechanics The algebrization of dynamical relationships is a paramount feature of Hamiltonian systems in which evolution and its diagnostics are described through the same differential geometry tools useful to study the effects of “transformations (e.g., rotations, translations, and gauge transformations) on the phase space, namely the Poisson bracket algebra. This has a tremendous equivalent in the quantum world in terms of the commutator algebra, giving both the dynamics of observables (in the Heisenberg representation) and their transformation rules. When dissipation comes into play, this beautiful framework breaks down: the dynamics cannot be reduced to Poisson–commutator algebra, even if those mathematical tools still implement transformations on the phase space and observables. Many classical dissipative systems may be recast into a Leibniz algebra framework, named metriplectic formalism (or its full equivalent, GENERIC tensor formalism) in which the Hamiltonian limit is well represented by a purely Hamiltonian dynamic with Poisson brackets, while the “dissipative addendum” is a semi-metric bracket, able to increase a quantity identified with the entropy of the system. In such a scheme, transformations through Poisson brackets do not increase entropy; hence, they do not make observables age, while the semi-metric bracket transforms the phase space and observables, making them age as entropy is produced. In quantum systems, no exact parallel to the metriplectic dynamics is known, even if the full parallel between classical Poisson bracket algebra and quantum commutator algebra could be expected to be extendable to some operator algebraic structure wider than that of commutators. Recently, studies have been carried out on the relationship between the evolution of open quantum systems described, for instance, through the Lindblad equation, and classical metriplectic systems, in which classical structures go beyond the Hamiltonian regime parallel to quantum non-unitary evolutions; moreover, through the use of a system of coherent states, the bridge between quantum systems and classical ones may be easily traced in the macroscopic limit. With this Special Issue of Entropy, we would like to collect new and updated applications and theoretical developments of the algebrization of dynamical systems that go beyond the Hamiltonian framework, with particular reference to classical metriplectic (GENERIC) formalism, quantum open system and non-unitary evolutions, and the relationship between the two.

Beyond Entanglement: The Engineering of Dissipation In the classical world, we were taught that energy lost is energy gone. But modern physics — from metriplectic systems to quantum decoherence — tells us something far more profound: dissipation is not decay. It is evolution. Every act of loss, every transformation of order into entropy, carries within it a code — the hidden algorithm of the universe adapting to itself. At Sakarya Aerospace, we no longer see physics as a set of abstract equations. We see it as a language of creation — one that merges thermodynamics, artificial intelligence, and consciousness into a single continuum. Our research in AI-driven aerospace architectures and neuromorphic–quantum hybrid systems takes inspiration from this same principle: “Balance is not the absence of chaos, but the mastery of its flow.” In metriplectic dynamics, order (Hamiltonian) and dissipation (metric) co-exist — just as human intuition and synthetic cognition coexist in the evolving AI mind. We call this new space of thought “Resonant Intelligence” — a convergence where engineering, entropy, and enlightenment meet. The future won’t simply be “smart.” It will be metriplectically alive. — 🛰️ Sakarya Aerospace | Celestia Systems Designing the future between energy and meaning. #QuantumPhysics #AIEngineering #Entropy #MetriplecticSystems #QuantumConsciousness #SakaryaAerospace #CelestiaSystems #ArtificialIntelligence #Thermodynamics #FutureOfScience #EntangledConsciousness #ResonantIntelligence