{"id":25264590,"url":"https://github.com/lockwo/quantum_computation","last_synced_at":"2025-10-27T04:30:37.356Z","repository":{"id":43714382,"uuid":"253099682","full_name":"lockwo/quantum_computation","owner":"lockwo","description":"Code for implementing and experimenting with quantum algorithms","archived":false,"fork":false,"pushed_at":"2023-10-04T16:58:37.000Z","size":10508,"stargazers_count":69,"open_issues_count":1,"forks_count":28,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-02T20:45:12.694Z","etag":null,"topics":["quantum-algorithms","quantum-circuits","quantum-computation","quantum-machine-learning","reinforcement-learning","tensorflow-quantum"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lockwo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2020-04-04T21:12:41.000Z","updated_at":"2025-02-16T12:32:34.000Z","dependencies_parsed_at":"2023-10-04T22:46:00.327Z","dependency_job_id":null,"html_url":"https://github.com/lockwo/quantum_computation","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/lockwo/quantum_computation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lockwo%2Fquantum_computation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lockwo%2Fquantum_computation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lockwo%2Fquantum_computation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lockwo%2Fquantum_computation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lockwo","download_url":"https://codeload.github.com/lockwo/quantum_computation/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lockwo%2Fquantum_computation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281215199,"owners_count":26462905,"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":"2025-10-27T02:00:05.855Z","response_time":61,"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":["quantum-algorithms","quantum-circuits","quantum-computation","quantum-machine-learning","reinforcement-learning","tensorflow-quantum"],"created_at":"2025-02-12T07:39:18.193Z","updated_at":"2025-10-27T04:30:36.035Z","avatar_url":"https://github.com/lockwo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Quantum Computation\n\nUnofficial implementations of various papers and algorithms using various tools such as Cirq, TensorFlow-Quantum, Pennylane, etc. If you use this code or base your code on it, cite it using the following: \n\n[![DOI](https://zenodo.org/badge/253099682.svg)](https://zenodo.org/badge/latestdoi/253099682)\n\n\n# Implemented Algorithms\n\nCode for different Quantum Computing and Quantum Machine Learing. All of the following ones have a video discussion on: https://www.youtube.com/channel/UC0U0HDNbdh0aI-9FbpYhPgg\n\n## TensorFlow-Quantum (TFQ) and Cirq\n\nCurrently includes:\n\n- Single Qubit Classifier\n\n- Solving XOR with QML\n\n- Replicating [Reinforcement learning with quantum variational circuits](https://ojs.aaai.org/index.php/AIIDE/article/view/7437/7289)\n\n- Quantum Approximate Optimization Algorithm (QAOA) in TFQ\n\n- Variational Quantum Eigensolver (VQE) in TFQ: include 1 and 2 qubit hamiltonians and replication of [Scalable Quantum Simulation of Molecular Energies](https://arxiv.org/pdf/1512.06860.pdf)\n\n- Rotosolve Optimizer for VQEs in TFQ: from [Structure optimization for parameterized quantum circuits](https://quantum-journal.org/papers/q-2021-01-28-391/pdf/)\n\n- VQE for arbitrarily many qubits in Cirq\n\n- Custom ParameterShift and Adam optimization comparison with TFQ\n\n- Arbitrary Qubit VQE in TFQ\n\n- [SSVQE](https://arxiv.org/abs/1810.09434) for excited states in TFQ\n\n- QOSF Application Problems:\n\n  - Swap Test in Cirq\n\n  - Simple Quantum Error Correction in Cirq\n\n  - Quantum Simulator from Scratch\n\n  - Weighted MaxCut QAOA in Cirq\n\n- Barren Plateaus in TFQ\n\n- Variational Quantum Classifiers/Regressors in TFQ for Circles, Moons, Blobs and Boston Housing\n\n- [Data Re-Uploading](https://quantum-journal.org/papers/q-2020-02-06-226/pdf/) Custom Layer (and VQC comparisons)\n\n- Replication of [Variational quantum policies for reinforcement learning](https://arxiv.org/pdf/2103.05577.pdf)\n\n- Replication of [Quantum-assisted quantum compiling](https://quantum-journal.org/papers/q-2019-05-13-140/pdf/)\n\n- Replication of [One qubit as a Universal Approximant](https://arxiv.org/pdf/2102.04032.pdf)\n\n- Code for [Playing Atari with Hybrid Quantum-Classical Reinforcement Learning](http://proceedings.mlr.press/v148/lockwood21a/lockwood21a.pdf)\n\n- Quantum Autoencoders using TFQ and TFQ datasets\n\n- [Noise resilience of variational quantum circuits](https://arxiv.org/abs/2011.01125) in TFQ  \n- Noisy VQE for Molecular Hamiltonians in TFQ \n- Adapt-VQE in TFQ\n- ADAPT-QAOA in TFQ\n- Trotterization with TFQ\n- Generalization Bounds of QML Analysis in TFQ\n- [Noise can be helpful in VQAs for saddle points](https://arxiv.org/abs/2210.06723) in TFQ\n- [Noise-induced barren plateaus](https://arxiv.org/abs/2007.14384) in TFQ\n- SPSA in TFQ\n\n## Pennylane\n\nCode for Pennylane experiments (largely from the [QHack](https://qhack.ai/) hackathon). Problems here: https://challenge.qhack.ai/team/problems. \n\n- Simple Circuits (20, 30, 50)\n\n- Quantum Gradients (100, 200, 500)\n\n- Circuit Training (100, 200, 500)\n\n- Variational Quantum Eigensolvers (100, 200, 500)\n\n## Other\n\n- OpenQAOA Intro Code\n\n- OpenQAOA parameter concentration and warm starting\n\n- Quantum volume needed for FTQC in Qiskit\n\n## Classic Algorithms\n\n- Quantum Teleportation\n\n- Deutsch–Jozsa Algorithm\n\n- Grover's Algorithm\n\n- Simon's Algorithm\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flockwo%2Fquantum_computation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flockwo%2Fquantum_computation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flockwo%2Fquantum_computation/lists"}