Discovering state-of-the-art quantum circuits with large language models
Harvard Quantum Initiative for Science and Engineering（哈佛量子科学与工程倡议）；Department of Physics, Harvard University（哈佛大学物理系）；Anthropic；QuEra Computing（QuEra计算公司）
Andi Gu, Alek H. Kemeny, Susanne F. Yelin, Mikhail D. Lukin
Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems the dominant resource costs are associated with a small set of computational primitives, optimizing them in the quantum setting presents unique challenges, including a reversibility constraint absent from ordinary classical computation. Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs and an independent evaluator verifies and scores them. We apply this framework to tasks spanning numerical computation, chemistry, optimization, quantum simulation, and number theory. In seven of the eight tasks, the discovered circuits significantly reduce both Toffoli and qubit counts relative to the state of the art. In the eighth, the search discovers a new reversible architecture for prime-field modular inversion. By handling reversibility differently, the architecture makes divsteps -- a classical algorithm previously impractical for reversible integer arithmetic -- viable and substantially improves the Toffoli-qubit trade-off for modular inversion. These results open the door for evaluator-guided LLM co-design of new algorithmic structures for quantum computation.
OmniQEC: discovering practical quantum error-correcting codes by an AI scientist
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Tokyo University of Agriculture and Technology（东京农工大学）；Zhejiang University of Technology（浙江工业大学）；City University of Hong Kong（香港城市大学）；Hon Hai (Foxconn) Research Institute（鸿海研究院）；School of Physical and Mathematical Science, Nanyang Technological University（南洋理工大学物理与数学科学学院）
DiTTO-LLM: Framework for Discovering Topic-based Technology Opportunities via Large Language Model
Department of Library and Information Science（图书馆与信息科学系）；Chung-Ang University（Chung-Ang 大学）；Insurance Strategy Planning Team（保险战略规划团队）；Postal Savings & Insurance Development Institute（邮政储蓄与保险发展研究所）；Future Technology Analysis Center（未来技术分析中心）；Korea Institute of Science and Technology Information（韩国科学技术信息院）
Discovering Failure Modes in Vision-Language Models using RL
Mila – Québec AI Institute（魁北克AI研究所）；Université de Montréal（蒙特利尔大学）；Michigan State University（密歇根州立大学）；McGill University（麦吉尔大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
Discovering heuristics in a complex SAT solver with large language models
School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院，上海，中国）；Key Laboratory of System Software, Institute of Software, Chinese Academy of Sciences, Beijing, China（中国科学院软件研究所系统软件重点实验室，北京，中国）；SeedMath Technology Limited, Beijing, China（SeedMath技术有限公司，北京，中国）
Uncovering shortcut learning in audio classifiers by discovering recurring concepts in temporal explanations
Departamento de Computación, FCEyN, UBA（布宜诺斯艾利斯大学精确与自然科学学院计算系）；ICC, CONICET-UBA（阿根廷国家科学与技术研究理事会-布宜诺斯艾利斯大学跨学科研究中心）；Music and Audio Research Lab, New York University（纽约大学音乐与音频研究实验室）；Integrated Design & Media, New York University（纽约大学综合设计与媒体系）
Discovering Heuristics with Large Language Models (LLMs) for Mixed-Integer Programs: Single-Machine Scheduling
Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA（弗吉尼亚理工大学工业与系统工程系）；Department of Computer Science, Virginia Tech, Arlington, VA（弗吉尼亚理工大学计算机科学系）
Discovering and Reasoning of Causality in the Hidden World with Large Language Models
TMLR Group, Hong Kong Baptist University（香港 Baptist 大学 TMLR 组）；Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed 人工智能大学）；Department of Philosophy, Carnegie Mellon University（哲学系，卡内基梅隆大学）；School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；School of Mathematics and Statistics, The University of Melbourne（墨尔本大学数学与统计学学院）；The Chinese University of Hong Kong（香港中文大学）
Sparse-QITE: Discovering Compact Generators for Quantum Imaginary-Time Evolution via Regularization Methods
Universitat de València-CSIC（瓦伦西亚大学-西班牙国家科学研究委员会）；Universidad CEU Cardenal Herrera（塞乌红衣主教埃雷拉大学）；Instituto Universitario de Investigación de Matemática Pura y Aplicada, Universitat Politècnica de València（瓦伦西亚理工大学纯数学与应用数学研究所）
Calculating the energy spectrum of a quantum system is an important task, for example to analyse reaction rates in drug discovery and catalysis. There has been significant progress in developing algorithms to calculate the ground state energy of molecules on near-term quantum computers. However, calculating excited state energies has attracted comparatively less attention, and it is currently unclear what the optimal method is. We introduce a low depth, variational quantum algorithm to sequentially calculate the excited states of general Hamiltonians. Incorporating a recently proposed techniqu…
Yquant is a LaTeX-only package -- i.e., involving no external programs -- that allows to typeset quantum circuits in a simple and human-readable language. It is inspired by the well-known qasm language and in fact even supports this language; however, the yquant language brings much more flexibility. yquant is based on TikZ and, while producing high-quality output out-of-the-box, it is highly configurable.
In this research, we present a quantum circuit design and implementation for a parallel universal linear bounded automata. This circuit is able to accelerate the inference of algorithmic structures in data for discovering causal generative models. The computation model is practically restricted in time and space resources. A classical exhaustive enumeration of all possible programs on the automata is shown for a couple of example cases. The precise quantum circuit design that allows executing a superposition of programs, along with a superposition of inputs as in the standard quantum Turing ma…
We introduce the LOv-calculus, a graphical language for reasoning about linear optical quantum circuits with so-called vacuum state auxiliary inputs. We present the axiomatics of the language and prove its soundness and completeness: two LOv-circuits represent the same quantum process if and only if one can be transformed into the other with the rules of the LOv-calculus. We give a confluent and terminating rewrite system to rewrite any polarisation-preserving LOv-circuit into a unique triangular normal form, inspired by the universal decomposition of Reck et al. (1994) for linear optical quan…
Isfahan University of Technology（伊斯法罕理工大学）；Shahid Bahonar University of Kerman（克尔曼沙希德·巴霍纳尔大学）；University of Basque Country UPV/EHU（巴斯克大学（UPV/EHU））
In this paper, we develop a compositional vector-based semantics of positive transitive sentences in quantum natural language processing for a non-English language, i.e. Persian, to compare the parametrized quantum circuits of two synonymous sentences in two languages, English and Persian. By considering grammar+meaning of a transitive sentence, we translate DisCoCat diagram via ZX-calculus into quantum circuit form. Also, we use a bigraph method to rewrite DisCoCat diagram and turn into quantum circuit in the semantic side.
In this paper, we introduce dynamic lifting to a quantum circuit-description language, following the Proto-Quipper language approach. Dynamic lifting allows programs to transfer the result of measuring quantum data -- qubits -- into classical data -- booleans -- . We propose a type system and an operational semantics for the language and we state safety properties. Next, we introduce a concrete categorical semantics for the proposed language, basing our approach on a recent model from Rios\&Selinger for Proto-Quipper-M. Our approach is to construct on top of a concrete category of circuits wit…
In the realm of quantum computing, circuit description languages represent a valid alternative to traditional QRAM-style languages. They indeed allow for finer control over the output circuit, without sacrificing flexibility nor modularity. We introduce a generalization of the paradigmatic lambda-calculus Proto-Quipper-M, itself modeling the core features of the quantum circuit description language Quipper. The extension, called Proto-Quipper-K, is meant to capture a very general form of dynamic lifting. This is made possible by the introduction of a rich type and effect system in which not on…
Quipper is a practical programming language for describing families of quantum circuits. In this paper, we formalize a small, but useful fragment of Quipper called Proto-Quipper-M. Unlike its parent Quipper, this language is type-safe and has a formal denotational and operational semantics. Proto-Quipper-M is also more general than Quipper, in that it can describe families of morphisms in any symmetric monoidal category, of which quantum circuits are but one example. We design Proto-Quipper-M from the ground up, by first giving a general categorical model of parameters and state. The distincti…
In this research we present a quantum circuit for estimating algorithmic complexity using the coding theorem method. This accelerates inferring algorithmic structure in data for discovering causal generative models. The computation model is restricted in time and space resources to make it computable in approximating the target metrics. The quantum circuit design based on our earlier work that allows executing a superposition of automata is presented. As a use-case, an application framework for protein-protein interaction ontology based on algorithmic complexity is proposed. Using small-scale …
