In the rapidly evolving domain of energy trading, integrating cutting-edge meteorological forecas... more In the rapidly evolving domain of energy trading, integrating cutting-edge meteorological forecasting presents a transformative opportunity to refine commodity trading strategies. This paper details our collaboration between Dynex and Recycgo Go in developing an enhanced quantum weather prediction system that employs a diffusion model, achieving unprecedented forecast accuracy of up to 98% over a 14-day period. We provide a comprehensive background on the intersection of extreme weather events and WTI crude oil market dynamics, highlighting how highly accurate forecasts can inform risk mitigation, optimize decision-making, and capture opportunities driven by weather-induced price volatility. Furthermore, we discuss the design and implementation of a machine learning trading bot, capable of adapting its strategies in real time based on forecast data, thereby pushing the boundaries of algorithmic commodity trading.
Large language models (LLMs) 1 have traditionally relied on autoregressive generation methods 2 ,... more Large language models (LLMs) 1 have traditionally relied on autoregressive generation methods 2 , which produce text sequentially and often struggle with long-range dependencies and creative variability. Recently, diffusion-based models 3 have emerged as a promising alternative by iteratively denoising a fully masked sequence. In this paper, we introduce Quantum Diffusion Large Language Models (QDLLMs), a novel framework that integrates quantum annealing 4 into the reverse diffusion process to guide token selection. By formulating token selection as a Quadratic Unconstrained Binary Optimization (QUBO) 5 problem, our approach overcomes the limitations of classical remasking strategies-such as the unpredictability of random remasking and the rigidity of low-confidence remasking-thus enabling more context-aware, creative, and human-like text generation. Preliminary evaluations on benchmarks like HellaSwag 6 and TruthfulQA 7 demonstrate notable improvements over conventional diffusion models, and our method successfully resolves a challenging logic puzzle that has stumped other state-of-the-art systems. These early results underscore the potential of quantum-inspired optimization to bridge the gap between deterministic token generation and the dynamic, non-linear processes inherent in human writing, paving the way for further advancements in generative language technologies.
Random Circuit Sampling (RCS) has emerged as a key benchmark for demonstrating quantum computatio... more Random Circuit Sampling (RCS) has emerged as a key benchmark for demonstrating quantum computational advantage. In this work, we implement RCS protocols on the Dynex platform, achieving results that approach Google’s beyond-classical benchmarks on their Willow chip. We demonstrate successful RCS execution on both 4x4 and 10x10 qubit grids, with circuit depths ranging from 5 to 200 cycles. Using patch-based cross-entropy benchmarking (XEB), we verify non-trivial fidelities across these configurations. Our complexity analysis shows that classically simulating these circuits would require billions of years on current supercomputers, while our quantum implementation completes in minutes. Through careful validation and complexity estimation, we provide strong evidence that the Dynex platform has achieved quantum supremacy. The accessibility of our platform and public availability of our complete codebase enables independent verification of these results by the broader scientific community. Our work demonstrates that beyond-classical quantum computation is not limited to specialized hardware but can be achieved on more accessible platforms, marking an important step toward practical quantum computing applications.
This paper presents an approach to optimizing gear shift strategies in racing vehicles by integra... more This paper presents an approach to optimizing gear shift strategies in racing vehicles by integrating LifeRacing Systems with the Dynex n.quantum platform. We propose a quantum-classical hybrid algorithm that leverages high-frequency telemetry data and quantum annealing techniques to discover optimal gear shift patterns. The integration aims to enhance racing performance through more efficient and adaptive gear shifting strategies.
Quantum Volume (QV) is a holistic benchmark that measures the performance of quantum computers, a... more Quantum Volume (QV) is a holistic benchmark that measures the performance of quantum computers, accounting for both gate fidelity and circuit complexity. Achieving a high QV is essential for demonstrating quantum advantage over classical systems. In this paper, we present a detailed account of computing a Quantum Volume of 2^119 using the Dynex neuromorphic quantum computing platform. We describe the methodologies employed, the modifications made to standard QV testing protocols to accommodate the Dynex architecture, and the results obtained. Our work showcases the scalability and computational capabilities of the Dynex platform in handling large-scale quantum computations.
In this paper, we unveil an innovative methodology that marries quantum computing with the traini... more In this paper, we unveil an innovative methodology that marries quantum computing with the training of Deep Restricted Boltzmann Machines(Deep RBMs) 1 , marking a significant leap forward in deep learning technologies. Our approach, Quantum-Enhanced Mode-Assisted Training, builds upon the foundation laid by our previous work in single-layer RBMs, scaling this technique to the intricate architectures of Deep RBMs with unprecedented efficiency 2. By harnessing quantum states, our algorithm navigates the training landscape of Deep RBMs with a finesse that outstrips traditional methods, capitalizing on the inherent parallelism and high-dimensional state representation of quantum computations. This quantum intervention not only preserves but notably enhances the benefits of mode-assisted training-yielding improved convergence rates, heightened stability, and an exponential reduction in the model's parameter count without diminishing its representational capabilities. Remarkably, this quantum-enhanced approach to Deep RBMs drastically curtails the parameter space required, eclipsing the efficiency of both classical Deep RBMs and their quantumtrained counterparts using conventional techniques. This significant reduction in parameter dependency, achieved without sacrificing model fidelity, represents a groundbreaking advancement in modeling complex, high-dimensional data sets with Deep RBMs. Our findings promise to redefine the paradigms of unsupervised learning, especially within quantum computing frameworks, highlighting the superior capability of our method to not only advance RBM training but to revolutionize Deep RBM applications even further. This research is powered by Dynex 3 , a cutting-edge quantum supercomputing platform, which enables us to leverage the full potential of quantum computing to its utmost advantage.
International Journal of Bioinformatics and Intelligent Computing, 2024
This document details the methodology and steps taken to convert Higher Order Unconstrained Binar... more This document details the methodology and steps taken to convert Higher Order Unconstrained Binary Optimization (HUBO) models into Quadratic Unconstrained Binary Optimization (QUBO) models. The focus is primarily on prime factorization problems; a critical and computationally intensive task relevant in various domains including cryptography, optimization, and number theory.
International Journal of Bioinformatics and Intelligent Computing, 2024
The integration of neuromorphic computing into the Dynex platform signifies a transformative step... more The integration of neuromorphic computing into the Dynex platform signifies a transformative step in computational technology, particularly in the realms of machine learning and optimization. This advanced platform leverages the unique attributes of neuromorphic dynamics, utilizing neuromorphic annealing-a technique divergent from conventional computing methods-to adeptly address intricate problems in discrete optimization, sampling, and machine learning. Our research concentrates on enhancing the training process of Restricted Boltzmann Machines (RBMs), a category of generative models traditionally challenged by the intricacy of computing their gradient. Our proposed methodology, termed "quantum mode training", blends standard gradient updates with an off-gradient direction derived from RBM ground state samples. This approach significantly improves the training efficacy of RBMs, outperforming traditional gradient methods in terms of speed, stability, and minimized converged relative entropy (KL divergence). This study not only highlights the capabilities of the Dynex platform in progressing unsupervised learning techniques but also contributes substantially to the broader comprehension and utilization of neuromorphic computing in complex computational tasks.
This document details the methodology and steps taken to convert Higher Order Unconstrained Binar... more This document details the methodology and steps taken to convert Higher Order Unconstrained Binary Optimization (HUBO) models into Quadratic Unconstrained Binary Optimization (QUBO) models. The focus is primarily on prime factorization problems; a critical and computationally intensive task relevant in various domains including cryptography, optimization, and number theory. The conversion from Higher-Order Binary Optimization (HUBO) to Quadratic Unconstrained Binary Optimization (QUBO) models is crucial for harnessing the capabilities of advanced computing methodologies, particularly quantum computing and DYNEX neuromorphic computing. Quantum computing offers potential exponential speedups for specific problems through its intrinsic parallelism capabilities. Conversely, DYNEX neuromorphic computing enhances efficiency and accelerates the resolution of intricate, pattern-oriented tasks by simulating memristors in GPUs, employing a highly decentralized approach, via Blockchain technology. This transformation enables the exploitation of these cutting-edge computing paradigms to address complex optimization challenges effectively. Through detailed explanations, mathematical formulations, and algorithmic strategies, this document aims to provide a comprehensive guide to understanding and implementing the conversion process from HUBO to QUBO. It underscores the importance of such transformations in making prime factorization computationally feasible on both existing classical computers and emerging computing technologies.
This document provides my steps and methodology on converting the Harrow-Hassidim-Lloyd (HHL) alg... more This document provides my steps and methodology on converting the Harrow-Hassidim-Lloyd (HHL) algorithm, typically used for solving linear systems on quantum computers, into a Quadratic Unconstrained Binary Optimization (QUBO) model termed as ”QCFD” to be computed on DYNEX Neuromorphic Network. This adaptation allows the use of classical and quantum-inspired solvers (a.k.a Simulated Annealing Sampler) and DYNEX Network users for finding solutions.
The integration of neuromorphic computing into the Dynex platform signifies a transformative step... more The integration of neuromorphic computing into the Dynex platform signifies a transformative step in computational technology, particularly in the realms of machine learning and optimization. This advanced platform leverages the unique attributes of neuromorphic dynamics, utilizing neuromorphic annealing-a technique divergent from conventional computing methods-to adeptly address intricate problems in discrete optimization, sampling, and machine learning. Our research concentrates on enhancing the training process of Restricted Boltzmann Machines (RBMs), a category of generative models traditionally challenged by the intricacy of computing their gradient. Our proposed methodology, termed "quantum mode training," blends standard gradient updates with an off-gradient direction derived from RBM ground state samples. This approach significantly improves the training efficacy of RBMs, outperforming traditional gradient methods in terms of speed, stability, and minimized converged relative entropy (KL divergence). This study not only highlights the capabilities of the Dynex platform in progressing unsupervised learning techniques but also contributes substantially to the broader comprehension and utilization of neuromorphic computing in complex computational tasks.
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Papers by Adam Neumann