This article explores search strategies for the design of parameterized quantum circuits. We propose several optimization approaches including random search plus survival of the fittest, reinforcement learning both with classical and hybrid quantum classical controllers and Bayesian optimization as decision makers to design a quantum circuit in an automated way for a specific task such as multi-labeled classification over a dataset. We introduce nontrivial circuit architectures that are arduous to be hand-designed and efficient in terms of trainability. In addition, we introduce reuploading of initial data into quantum circuits as an option to find more general designs. We numerically show that some of the suggested architectures for the Iris dataset accomplish better results compared to the established parameterized quantum circuit designs in the literature. In addition, we investigate the trainability of these structures on the unseen dataset Glass. We report meaningful advantages over the benchmarks for the classification of the Glass dataset which supports the fact that the suggested designs are inherently more trainable.
University of California, Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；St. Kliment Ohridski University of Sofia（索非亚圣克里门特·奥赫里德大学）；Max Planck Institute for the Physics of Complex Systems（马克斯·普朗克复杂系统物理研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only continuous optimization variables, the representational power of the variational ansatz can sometimes be significantly enhanced by adding certain discrete optimization variables, as is exemplified by the generalized quantum approximate optimization algorithm (QAOA). However, the hybrid discrete-continuous optimization problem in the generalized QAOA poses a challenge to the optimization. We propose a new algorithm ca…
Curriculum reinforcement learning for quantum architecture search under hardware errors
Leiden University（莱顿大学）；Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息研究所）；Silesian University of Technology（西里西亚工业大学）；Warsaw University of Technology（华沙工业大学）；Lorentz Institute（洛伦兹研究所）；LIACS, Leiden University（莱顿大学高级计算机科学研究所）
Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；Washington University in St. Louis（圣路易斯华盛顿大学）
The design automation of analog circuits is a longstanding challenge in the integrated circuit field. This paper presents a deep reinforcement learning method to expedite the design of analog circuits at the pre-layout stage, where the goal is to find device parameters to fulfill desired circuit specifications. Our approach is inspired by experienced human designers who rely on domain knowledge of analog circuit design (e.g., circuit topology and couplings between circuit specifications) to tackle the problem. Unlike all prior methods, our method originally incorporates such key domain knowled…
Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；Washington University in St. Louis（圣路易斯华盛顿大学）
The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit parameter optimization at the pre-layout stage, i.e., finding device parameters to fulfill desired circuit specifications. Unlike all prior methods, our approach is inspired by human experts who rely on domain knowledge of analog circuit design (e.g., circuit topology and couplings between circuit specifications) to tackle the problem. By originally incorporating such key domain knowledge into policy training with a …
Élivágar: Efficient Quantum Circuit Search for Classification
Pennsylvania State University（宾夕法尼亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas Austin（德克萨斯大学奥斯汀分校）；AWS Quantum Technologies（AWS量子技术）
We present a quantum synthesis algorithm designed to produce short circuits and to scale well in practice. The main contribution is a novel representation of circuits able to encode placement and topology using generic "gates", which allows the QFAST algorithm to replace expensive searches over circuit structures with few steps of numerical optimization. When compared against optimal depth, search based state-of-the-art techniques, QFAST produces comparable results: 1.19x longer circuits up to four qubits, with an increase in compilation speed of 3.6x. In addition, QFAST scales up to seven qub…
A quantum circuit to construct all maximal cliques using Grover search algorithm is presented. This oracle circuit takes as input an n-qubit state |x> and the adjacency matrix data A of an n-node network, and outputs the state (-1)^f(x) |x> where f(x)=1 if |x> is a maximal clique, and f(x)=0, otherwise. The oracle workspace requires 2n^2 qubits. Each oracle call makes about 10n^2 calls to the Toffoli gates. The overall Grover search algorithm takes as input a uniform superposition n-qubit state and the adjacency matrix data, and outputs a uniform superposition of all maximal clique states.
Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries
Perimeter Institute for Theoretical Physics（理论物理研究所）；Institute for Quantum Computing, University of Waterloo（量子计算研究所，滑铁卢大学）；Institute for Information Processing (tnt/L3S), Leibniz Universität Hannover, Germany（信息处理研究所（tnt/L3S），莱比锡大学）
Probabilistic Design of Parametrized Quantum Circuits through Local Gate Modifications
The Edward S. Rogers Sr. Department of Electrical \& Computer Engineering, University of Toronto 10 King's College Rd Toronto ON M5S 3G8 Canada；Department of Chemical；Physical Sciences, University of Toronto Mississauga 3359 Mississauga Road Mississauga ON L5L 1C6 Canada；Department of Computer Science, University of Toronto Toronto ON M5S 2E4 Canada；Vector Institute for Artificial Intelligence 108 College St W1140 Toronto ON M5G 0C6 Canada；The Edward S. Rogers Sr. Department of Electrical \& Computer Engineering, University of Toronto 10 King's College Rd Toronto ON M5G 0C6 Canada；Department of Chemistry, University of Calgary 2500 University Drive NW Calgary AB T2N 1N4 Canada；Department of Computer Science, University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada；The Edward S. Rogers Sr. Department of Electrical \& Computer Engineering, University of Toronto；Physical Sciences, University of Toronto Mississauga；Department of Computer Science, University of Toronto；Vector Institute for Artificial Intelligence；Department of Chemistry, University of Calgary
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术研究所）
Near-term quantum hardware can support two-qubit operations only on the qubits that can interact with each other. Therefore, to execute an arbitrary quantum circuit on the hardware, compilers have to first perform the task of qubit routing, i.e., to transform the quantum circuit either by inserting additional SWAP gates or by reversing existing CNOT gates to satisfy the connectivity constraints of the target topology. We propose a procedure for qubit routing that is architecture agnostic and that outperforms other available routing implementations on various circuit benchmarks. The depth of th…
Using Grover algorithm, this work investigates a technique for encoding search phrases used in wildcard searches. The technique involves creating a phase Oracle that loads data into a quantum circuit together with the search terms that have been encoded. The method entails constructing a phase Oracle programmatically using encoded input information and encoded search terms. By combining Grover diffusion operator with the phase Oracle, Hadamard gates, and zero-initialized three-qubit states, a complete quantum circuit is created. Trapped ion and superconducting qubit quantum computers, which we…
Simulating quantum circuits using classical computers lets us analyse the inner workings of quantum algorithms. The most complete type of simulation, strong simulation, is believed to be generally inefficient. Nevertheless, several efficient strong simulation techniques are known for restricted families of quantum circuits and we develop an additional technique in this article. Further, we show that strong simulation algorithms perform another fundamental task: solving search problems. Efficient strong simulation techniques allow solutions to a class of search problems to be counted and found …
In Noisy Intermediate-Scale Quantum (NISQ) era, quantum processing units (QPUs) suffer from, among others, highly limited connectivity between physical qubits. To make a quantum circuit effectively executable, a circuit transformation process is necessary to transform it, with overhead cost the smaller the better, into a functionally equivalent one so that the connectivity constraints imposed by the QPU are satisfied. While several algorithms have been proposed for this goal, the overhead costs are often very high, which degenerates the fidelity of the obtained circuits sharply. One major reas…
The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）；SenseTime（商汤科技）；University of Technology Sydney（悉尼科技大学）；Hon Hai Quantum Computing Research Center（鸿海量子计算研究中心）
Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, …
CutVQA: Co-Designing Circuit Cutting and Architecture Search for Scaling Variational Quantum Algorithms
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Hefei National Laboratory, University of Science and Technology of China（中国科学技术大学合肥国家实验室）
Finding a maximum or minimum is a fundamental building block in many mathematical models. Compared with classical algorithms, Durr, Hoyer's quantum algorithm (DHA) achieves quadratic speed. However, its key step, the quantum exponential searching algorithm (QESA), which is based on Grover algorithm, is not a sure-success algorithm. Meanwhile, quantum circuits encounter the gate decomposition problem due to variation of the scale of data. In this paper, we propose an optimized quantum algorithm for searching maximum and minimum, based on DHA and the optimal quantum exact search algorithm. Furth…
Recently Neil Ross and Peter Selinger analyzed the problem of approximating z- rotations by means of single-qubit Clifford+T circuits. Their main contribution is a deterministic-search technique which allowed them to make approximating circuits shallower. We adapt the deterministic-search technique to the case of Pauli+V circuits and prove similar results. Because of the relative simplicity of the Pauli+V framework, we use much simpler geometric methods.
Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform which can significantly accelerate current efforts to identify quantum advantages of error-prone and depth-limited quantum circuits in the NISQ era. Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion. We present several examples of circuit design problems to demonstrate the power of DQAS…
Mila - Quebec AI Institute, Université de Montréal（魁北克人工智能研究所，蒙特利尔大学）
Designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing, particularly for noisy intermediate-scale quantum (NISQ) devices, where circuit efficiency and resilience to errors are paramount. The search space of gate sequences grows combinatorially, and handcrafted templates often waste scarce qubit and depth budgets. We introduce \textsc{FlowQ-Net} (Flow-based Quantum design Network), a generative framework for automated quantum circuit synthesis based on Generative Flow Networks (GFlowNets). This framework learns a stochastic policy to constru…
In this paper, a new non-search based synthesis algorithm for reversible circuits is proposed. Compared with the widely used search-based methods, our algorithm is guarantied to produce a result and can lead to a solution with much fewer steps. To evaluate the proposed method, several circuits taken from the literature are used. The experimental results corroborate the expected findings.
Mapping logical quantum circuits to Noisy Intermediate-Scale Quantum (NISQ) devices is a challenging problem which has attracted rapidly increasing interests from both quantum and classical computing communities. This paper proposes an efficient method by (i) selecting an initial mapping that takes into consideration the similarity between the architecture graph of the given NISQ device and a graph induced by the input logical circuit; and (ii) searching, in a filtered and depth-limited way, a most useful SWAP combination that makes executable as many as possible two-qubit gates in the logical…
Executing quantum circuits on currently available quantum computers requires compiling them to a representation that conforms to all restrictions imposed by the targeted architecture. Due to the limited connectivity of the devices' physical qubits, an important step in the compilation process is to map the circuit in such a way that all its gates are executable on the hardware. Existing solutions delivering optimal solutions to this task are severely challenged by the exponential complexity of the problem. In this paper, we show that the search space of the mapping problem can be limited drast…
Inspired by a class of algorithms proposed by Farhi et al. (arXiv:1411.4028), namely the quantum approximate optimization algorithm (QAOA), we present a circuit-based quantum algorithm to search for a needle in a haystack, obtaining the same quadratic speedup achieved by Grover's original algorithm. In our algorithm, the problem Hamiltonian (oracle) and a transverse field are applied alternately to the system in a periodic manner. We introduce a technique, based on spin-coherent states, to analyze the composite unitary in a single period. This composite unitary drives a closed transition betwe…
Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates
Department of Physics, Indian Institute of Technology (BHU)（印度理工学院（BHU）物理系）；eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（纽约大学阿布扎赫分校（NYUAD）工程系eBRAIN实验室）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD（纽约大学阿布扎赫分校（NYUAD）量子与拓扑系统中心（CQTS））
