{"id":31103715,"url":"https://github.com/ahmadjamil888/q-spe","last_synced_at":"2025-09-17T02:51:04.565Z","repository":{"id":311816514,"uuid":"1045177172","full_name":"Ahmadjamil888/Q-SPE","owner":"Ahmadjamil888","description":"Q-SPE or Quantum Super Position Entanglement is a Model Architecture developed by me on the traditions of Quantum Physics.","archived":false,"fork":false,"pushed_at":"2025-08-26T19:20:38.000Z","size":15,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-27T03:19:33.644Z","etag":null,"topics":["acrhitecture","ai","demonstration","quantum-computing","quantum-mechanics","quantum-physics","research","superposition"],"latest_commit_sha":null,"homepage":"https://github.com/Ahmadjamil888/Q-SPE","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Ahmadjamil888.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-08-26T19:08:36.000Z","updated_at":"2025-08-26T19:22:10.000Z","dependencies_parsed_at":"2025-08-27T03:21:45.345Z","dependency_job_id":"dc36c1d3-2008-4dd5-881f-74123da812e4","html_url":"https://github.com/Ahmadjamil888/Q-SPE","commit_stats":null,"previous_names":["ahmadjamil888/q-spe"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/Ahmadjamil888/Q-SPE","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ahmadjamil888%2FQ-SPE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ahmadjamil888%2FQ-SPE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ahmadjamil888%2FQ-SPE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ahmadjamil888%2FQ-SPE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ahmadjamil888","download_url":"https://codeload.github.com/Ahmadjamil888/Q-SPE/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ahmadjamil888%2FQ-SPE/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275526416,"owners_count":25480460,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-17T02:00:09.119Z","response_time":84,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["acrhitecture","ai","demonstration","quantum-computing","quantum-mechanics","quantum-physics","research","superposition"],"created_at":"2025-09-17T02:51:02.608Z","updated_at":"2025-09-17T02:51:04.557Z","avatar_url":"https://github.com/Ahmadjamil888.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Q-SPE: Quantum Superposition Entanglement Model Architecture\n\n**Author**: Ahmad Jamil  \n**Founder \u0026 CEO, ZehanX Technologies**\n\n---\n\n## Overview\n\n**Q-SPE (Quantum Superposition Entanglement)** is a novel model architecture inspired by the principles of **quantum mechanics**, specifically **superposition** and **entanglement**.  \n\nThe architecture introduces the idea of representing computational states not as fixed binary values, but as **probabilistic distributions over multiple simultaneous possibilities**. Unlike classical deep learning layers that deterministically transform vectors, Q-SPE layers encode information in a **superpositional state space**.  \n\nThe motivation behind Q-SPE is to explore how quantum-theoretic phenomena can be **simulated or approximated** on classical hardware, while laying theoretical groundwork for deployment on true **quantum computers** in the future.\n\n---\n\n## Background\n\n- **Superposition**: A system can exist in multiple states at once until observed.  \n- **Entanglement**: Two or more systems exhibit correlated behavior, such that observing one instantaneously affects the other, regardless of distance.  \n- **Collapse**: Measurement forces a system to resolve into a single definite state.  \n\nQ-SPE draws upon these ideas to create a **mathematical and computational framework** for model training. The system’s *intermediate layers* exist in multi-state forms, with probabilities determining their eventual output upon collapse.\n\n---\n\n## Mathematical Formulation\n\nLet the state vector of an input feature space be:\n\n\\[\n\\psi(x) = \\sum_i \\alpha_i |x_i\\rangle\n\\]\n\nwhere:\n- \\( |x_i\\rangle \\) are basis states of features,\n- \\( \\alpha_i \\in \\mathbb{C} \\) are complex coefficients,\n- \\( \\sum_i |\\alpha_i|^2 = 1 \\).\n\n### Q-SPE Layer Transformation\n\nEach Q-SPE layer applies a **unitary transformation**:\n\n\\[\n\\psi'(x) = U \\psi(x)\n\\]\n\nwhere \\( U \\) is a unitary operator satisfying:\n\n\\[\nU^\\dagger U = I\n\\]\n\nThis ensures preservation of total probability amplitude.\n\n### Entanglement Between Layers\n\nFor two states \\(\\psi_A\\) and \\(\\psi_B\\), the entangled joint state is expressed as:\n\n\\[\n\\Psi_{AB} = \\sum_{i,j} \\alpha_{ij} |x_i\\rangle_A \\otimes |x_j\\rangle_B\n\\]\n\nThe measurement of subsystem A influences subsystem B through the shared amplitudes \\(\\alpha_{ij}\\).\n\n### Collapse to Classical Output\n\nThe final observable output vector is obtained by probabilistic collapse:\n\n\\[\ny = \\text{argmax}_i \\; P(x_i) \\quad \\text{where } P(x_i) = |\\alpha_i|^2\n\\]\n\nThus, Q-SPE does not deterministically predict a single outcome, but instead encodes multiple states until observation.\n\n---\n\n## Implementation Guide\n\n### 1. Environment Setup\nClone this repository and install required dependencies:\n\n```bash\ngit clone https://github.com/Ahmadjamil888/Q-SPE.git\ncd Q-SPE\npip install -r requirements.txt\n```\n2. Running the Demo\nThe demo simulates superposition states on a classical machine:\n\n```bash\nCopy\nEdit\npython demo.py\nThe code generates probabilistic outputs reflecting the multi-state superposition and demonstrates entanglement effects between different input features.\n```\n3. Training\nWhile the current implementation is not fully quantum, training proceeds as follows:\n\nInputs are encoded as state vectors.\n\nEach Q-SPE layer applies unitary-like transformations.\n\nA measurement operation collapses states to observable values.\n\nDue to classical hardware limits, the simulation complexity grows exponentially with the number of qubits (states).\n\nExample Output\nFor a sample input vector \n[\n1\n,\n0\n]\n[1,0]:\n\nyaml\nCopy\nEdit\nInitial state: |ψ⟩ = [1, 0]\nAfter superposition: |ψ'⟩ = [0.707, 0.707]\nMeasurement probabilities: [0.50, 0.50]\nObserved output: [1, 0] or [0, 1] (probabilistic)\n\nThis demonstrates state indeterminacy until collapse.\n\nStrengths\nProvides a new perspective on hybrid quantum-classical model design.\n\nBridges theoretical physics and AI architecture.\n\nAllows experimentation with probabilistic layers.\n\nEstablishes a foundation for future quantum machine learning.\n\nLimitations\nSimulation cost: exponential growth in memory and computation on classical systems.\n\nNo true quantum speedup: actual quantum advantage only achievable on quantum processors.\n\nExperimental phase: The model is theoretical and primarily conceptual, with limited scalability.\n\nNoise sensitivity: Classical approximations may distort intended quantum properties.\n\nFuture Directions\nExtend Q-SPE for integration with TensorFlow Quantum or PennyLane.\n\nImplement hybrid layers combining classical CNN/RNN blocks with quantum-inspired Q-SPE blocks.\n\nDeploy Q-SPE prototypes on real quantum hardware (IBM Q, Rigetti, Xanadu).\n\nExplore applications in optimization, cryptography, and defense AI.\n\nCitation\nIf you use Q-SPE in your research, please cite:\n\ncss\nCopy\nEdit\nJamil, A. (2025). Q-SPE: Quantum Superposition Entanglement Model Architecture. ZehanX Technologies.\nLicense\n© 2025 ZehanX Technologies. All rights reserved.\nThis work is released under a research-only license.\nCommercial usage requires explicit permission from the author.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmadjamil888%2Fq-spe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fahmadjamil888%2Fq-spe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmadjamil888%2Fq-spe/lists"}