Houston Haynes' Framework for Preserving Code Semantic Meaning

I am pleased to announce the publication of my first arXiv preprint: "Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking." Efficient data encoding has long been a bottleneck in hybrid quantum-classical algorithms, with traditional simulators spending significant time converting classical features into quantum rotations. To address this, our team developed the Hybriqu Encoder, a Rust-based, SIMD-aware kernel that focuses exclusively on angle encoding and integrates seamlessly with Python via CFFI. By processing multiple double-precision rotations simultaneously using AVX-class vector lanes, utilizing pre-calculated trigonometric factors, and optimizing for cache performance, we achieved a 5.4% speedup at 64 qubits on Apple Silicon. This performance scales effectively as data exceeds the L1 cache size, demonstrating that vectorization provides consistent improvements for compute-bound encoding tasks. Our benchmarking also identified that kernels applying rotations to the entire state vector remain heavily limited by memory bandwidth rather than compute power. This highlights a clear path for future optimization regarding state updates and processing methods in larger hybrid quantum-classical systems. A huge thank you to my co-authors Irwan Alnarus Kautsar, Gunawan Witjaksono, and Haza Nuzly Bin Abdull Hamed for their collaboration and support on this work. You can read the full preprint here: https://lnkd.in/gKMNt57B #QuantumComputing #MachineLearning #RustLang #HighPerformanceComputing #SIMD #QuantumSimulation #ArXiv

Orange is the new black? Maybe. But this year, compilers are the new cool. 😎 And where better to celebrate all things architecture, compilation, systems, and parallelism than #PACT2026? PACT brings together the community across hardware and software, with a special emphasis on parallelism, and this year’s abstract deadline is April 17, 2026, with full papers due April 24, 2026. So what’s next on your agenda? Submit to PACT --> https://pact26.hotcrp.com Get your title and abstract in, and give your work the stage it deserves. Do not miss the chance to join us. Michela Taufer Jungwon Kim Jie R. Yongwei Zhao Tanu Malik Danu Popovici Bogdan Alexandru Lawrence Rauchwerger Jaejin Lee Bernhard Egger #PACT2026 #HPE #Compilers #ComputerArchitecture #ParallelComputing #HPC #SystemsResearch

Quantum computers don’t run code. They run circuits. That’s the first mental shift you need to make. In classical computing, we write programs step by step instructions executed sequentially in languages like Python or C++. Quantum computing works differently. Instead of code, you design a quantum circuit a sequence of quantum gates applied to qubits. Each gate doesn’t “compute” in the classical sense. It transforms the state of a qubit shaping probabilities, not flipping bits. You’re not telling the computer what to do step by step. You’re constructing a system that evolves according to quantum mechanics. And at the end of the circuit? You measure. That’s when probabilities collapse into an actual result. This is why quantum programming feels less like coding… and more like designing an experiment. From instructions → to transformations From logic → to physics This is Day 2 of understanding how quantum computers actually compute. Next: What exactly is a quantum gate? #QuantumComputing #QuantumAlgorithms #QuantumCircuits #DeepTech #FutureOfComputing #EmergingTech #day2 #computation

Quantum Error Correction Gains a Powerful New Mathematical Framework A quantity of Θ(N) independent cup products, sustained on almost good quantum codes, suggests a pathway towards scaling logical operations beyond current limitations. This mathematical relationship between code length and parallel gate operations offers a new lens for designing more efficient quantum computations. The work rigorously connects seemingly disparate areas of mathematics, sheaf codes, covering spaces, and Artin’s conjecture, to the practical demands of error correction. #quantum #quantumcomputing #technology https://lnkd.in/euZMVDnT

When Brute Force Discovers "Alien" Math I was testing a symbolic regression engine. The goal: find the most efficient way to compute the Fibonacci sequence using only raw CPU primitives. As a human, I expected the system to converge on standard addition: Fn = Fn-1 + Fn-2. The machine had a different idea. Instead of traditional arithmetic, the top-performing "logic" the system discovered was a ROR 10 (Circular Bit Rotation). Why is this fascinating? We are taught that Fibonacci is about addition and the Golden Ratio 1.618. But in a 64-bit register, the machine found a "bit-hack" where rotating the binary structure of a number by 10 positions produced a closer approximation to the next Fibonacci step than a simple constant addition. The Takeaway: We often force AI to mimic human thought patterns. But when you give a system a "compass" and let it brute-force the optimal math, it finds shortcuts that are: - Hardware-native: Rotation is a single-clock cycle operation. - Counter-intuitive: No textbook would ever suggest bit-rotation for Fibonacci. - Alien: It’s a purely binary logic that only "makes sense" to a processor. This is the beauty of low-level engineering. Sometimes the most "optimal" math isn't the one we were taught in school - it’s the one hidden in the architecture of the chip. Feel free to share you opinion in comments #LowLevel #SoftwareArchitecture #RustLang #Algorithms #Computing #BitHacking

I am thrilled to announce that our (Yannis Psaromiligkos) paper, "𝐌𝐨𝐝𝐞𝐥-𝐛𝐚𝐬𝐞𝐝 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐉𝐨𝐢𝐧𝐭 𝐑𝐈𝐒 𝐏𝐡𝐚𝐬𝐞 𝐒𝐡𝐢𝐟𝐭 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐖𝐌𝐌𝐒𝐄 𝐁𝐞𝐚𝐦𝐟𝐨𝐫𝐦𝐢𝐧𝐠" has been accepted for publication in the journal: 𝐈𝐄𝐄𝐄 𝐖𝐢𝐫𝐞𝐥𝐞𝐬𝐬 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐋𝐞𝐭𝐭𝐞𝐫𝐬 𝐈𝐄𝐄𝐄 𝐗𝐩𝐥𝐨𝐫𝐞: https://lnkd.in/ewPErAZw 𝐚𝐫𝐗𝐢𝐯: https://lnkd.in/eahUiJyh 𝐃𝐎𝐈: 10.1109/LWC.2026.3683016 𝐂𝐨𝐝𝐞: https://lnkd.in/eZa_5M5s 𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭:  A model-based deep learning (DL) architecture is proposed for reconfigurable intelligent surface (RIS)-assisted multi-user communications to reduce the number of bits required for transmitting phase shift information from the access point (AP) to the RIS controller. The AP computes the phase shifts and compresses them into a binary control message that is sent to the RIS controller for element configuration. To help reduce beamformer mismatches caused by phase shift compression errors, the beamformer is updated with the actual (decompressed) RIS phase shifts. By unrolling the iterative weighted minimum mean square error (WMMSE) algorithm within the wireless communication-informed DL architecture, joint phase shift compression and WMMSE beamforming can be trained end-to-end. Simulation results demonstrate that incorporating compression-aware beamforming significantly improves sum-rate performance, even when the number of control bits is lower than the number of RIS elements. #DeepLearning #WirelessCommunications #RIS #IEEE #MachineLearning #SignalProcessing

For 38 years, computer scientists believed Dijkstra's algorithm was optimal for sparse graphs. The logic seemed airtight: Dijkstra sorts vertices by distance. Sorting has a lower bound of O(n log n). Therefore shortest paths can't be faster. 5 researchers proved the assumption wrong. The trick: combine Dijkstra's priority queue with Bellman-Ford's dynamic programming. Divide and conquer on vertex sets. Shrink the frontier. Result: O(m log^(2/3) n) First improvement for directed graphs since Fibonacci heap in 1987. Tsinghua. Stanford. Max Planck. 17 pages. Read the paper: https://lnkd.in/dTMNiSt5

The bottleneck was maintaining a global ordering of the frontier, they get around it by shrinking and partitioning it, so only small subsets ever need to be ordered

Machine Learning Audio Data using Julius #machinelearning #datascience #audiodata #julius Nice DSP sweets: resampling, FFT Convolutions. All with PyTorch, differentiable and with CUDA support. Julius contains different Digital Signal Processing algorithms implemented with PyTorch, so that they are differentiable and available on CUDA. Note that all the modules implemented here can be used with TorchScript : julius.resample: fast sinc resampling. julius.fftconv: FFT based convolutions. julius.lowpass: FIR low pass filter banks. julius.filters: FIR high pass and band pass filters. julius.bands: Decomposition of a waveform signal over mel-scale frequency bands. Along that, you might found useful utilities in : julius.core: DSP related functions. julius.utils: Generic utilities. This package is named in this honor of Julius O. Smith, whose books and website were a gold mine of information for me to learn about DSP. https://lnkd.in/g4tNEP5P