[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118632-en":3,"doc-seo-118632-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118632,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries","Quantum computing and machine learning—quantum machine learning—enable new directions for advancing chemical research and drug discovery. This review focuses on quantum neural networks implemented on gate-based quantum computers, outlining key theoretical elements such as data encoding, variational quantum circuits, and hybrid quantum-classical workflows. Applications emphasize molecular property prediction and molecular generation, while offering a balanced assessment of expected benefits alongside practical challenges and research gaps.","arXiv :2409 . 15645v1 [ quant-ph] 24 Sep 2024  \nQuantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical  \nIndustries  \nAnthony M. Smaldone,†,⊥ Yu Shee,†,⊥ Gregory W. Kyro,† Chuzhi Xu,† Nam P. Vu,‡,† Rishab Dutta,† Marwa H. Farag,¶ Alexey Galda, § Sandeep Kumar, § Elica  \nKyoseva,¶ and Victor S. Batista ∗ ,†, ∥  \n†Department of Chemistry, Yale University, New Haven, CT 06520, USA ‡Department of Chemistry, Lafayette College, Easton, PA 18042, USA ¶Quantum Algorithm Engineering, NVIDIA Corporation, Santa Clara, CA 95051, USA §Molecule Design and Modeling Group, Computational Science, Moderna Inc., 325 Binney  \nStreet, Cambridge, MA 02142  \n∥ Yale Quantum Institute, Yale University, New Haven, CT 06511, USA ⊥These authors contribute equally to this article.  \nE-mail: [victor.batista@yale.edu](victor.batista@yale.edu)  \nAbstract  \nThe nexus of quantum computing and machine learning––quantum machine learning––offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery. We discuss the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. Applications to drug discovery are highlighted,  \nincluding molecular property prediction and molecular generation. We provide a balanced perspective, emphasizing both the potential benefits and the challenges that must be addressed.  \n1 Introduction  \n1.1 Quantum Computing  \nIn this introduction, we discuss the general methodology of quantum computing based on unitary transformations (gates) of quantum registers, which underpin the potential advancements in computational power over classical systems. We introduce the unique properties of quantum bits, or qubits, quantum calculations implemented by algorithms that evolve qubit states through unitary transformations, followed by measurements that collapse the superposition states to produce specific outcomes, and lastly the challenges faced in practical quantum computing limited by noise, with hybrid approaches that integrate quantum and classical computing to address current limitations. This introductory discussion sets the stage for a deeper exploration into quantum computing for machine learning applications in subsequent sections.  \nCalculations with quantum computers generally require evolving the state of a quantum register by applying a sequence of pulses that implement unitary transformations according to a designed algorithm. A measurement of the resulting quantum state then collapses the coherent state, yielding a specific outcome of the calculation. To obtain reliable results, the process is typically repeated thousands of times, with averages taken over all of the measurements to account for quantum randomness and ensure statistical accuracy. This repetition is essential to achieve convergence, as each individual measurement only provides probabilistic information about the quantum state.  \nQuantum registers are commonly based on qubits. Like classical bits, qubits can be observed in either of two possible states (0 or 1) . However, unlike classical bits, they can  \nbe prepared in superposition states, representing both 0 and 1 simultaneously with certain probability. In fact, the state of a single qubit can be described using the ket notation, as follows:  \n|ψ⟩ = α|0⟩ + β|1⟩ , (1)  \nwhere α and β are complex amplitudes satisfying the normalization condition |α|2 +|β|2 = 1 . Such a state represent the states |0⟩ and |1⟩ simultaneously with probability |α|2 and |β|2 , respectively.  \nQuantum registers with n qubits represent states that are linear combinations of tensor products of qubit states. Therefore, a register with n qubits represents 2n states simultaneously, offering a representation with exponential advantage over classical registers. For instance, the state o","cbCaigEDI0aLTNTJ","https://ap.wps.com/l/cbCaigEDI0aLTNTJ","pdf",3587441,1,70,"English","en",105,"# Abstract\n# Introduction\n## Quantum Computing\n## Quantum States and Qubits\n## Quantum Gates and Measurements","[{\"question\":\"What does quantum machine learning focus on in the context of drug discovery?\",\"answer\":\"It investigates quantum neural networks on gate-based quantum computers for drug-discovery tasks, including molecular property prediction and molecular generation.\"},{\"question\":\"What are the core theoretical building blocks discussed for quantum machine learning?\",\"answer\":\"The review covers data encoding, variational quantum circuits, and hybrid quantum-classical approaches as key foundations.\"},{\"question\":\"How does quantum computing produce results reliably for calculations?\",\"answer\":\"Quantum measurements collapse superposition into probabilistic outcomes, so the process is repeated many shots and averaged to achieve statistical convergence.\"}]","Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries | 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