We present superconducting quantum circuits that exhibit atomic energy spectra and selection rules in ladder and lambda three-level configurations designed using genetic algorithms. These heuristic optimization techniques are employed to adapt the topology and parameters of a set of electrical circuits to find the suitable architecture matching the required energy levels and relevant transition matrix elements. We analyze the performance of the optimizer on one-dimensional single- and multiloop circuits to design ladder ( Ξ ) and lambda ( Λ ) three-level systems with specific transition matrix elements. As expected, attaining both the required energy spectrum and the needed selection rules is challenging for single-loop circuits, but they can be accurately obtained even with just two loops. Additionally, we show that our multiloop circuits are robust against random fluctuations in their circuit parameters, i.e., under eventual fabrication flaws. Developing an optimization algorithm for automated circuit quantization opens an avenue to engineering superconducting circuits with specific symmetry to be used as modules within large-scale setups. This may allow us to mitigate the well-known current errors observed in the first generation of quantum processors.
We present superconducting quantum circuits that exhibit atomic energy spectra and selection rules in ladder and lambda three-level configurations designed using genetic algorithms. These heuristic optimization techniques are employed to adapt the topology and parameters of a set of electrical circuits to find the suitable architecture matching the required energy levels and relevant transition matrix elements. We analyze the performance of the optimizer on one-dimensional single- and multiloop circuits to design ladder ( Ξ ) and lambda ( Λ ) three-level systems with specific transition matrix elements. As expected, attaining both the required energy spectrum and the needed selection rules is challenging for single-loop circuits, but they can be accurately obtained even with just two loops. Additionally, we show that our multiloop circuits are robust against random fluctuations in their circuit parameters, i.e., under eventual fabrication flaws. Developing an optimization algorithm for automated circuit quantization opens an avenue to engineering superconducting circuits with specific symmetry to be used as modules within large-scale setups. This may allow us to mitigate the well-known current errors observed in the first generation of quantum processors.
Abstract Superconducting circuits have emerged as a promising platform to build quantum processors. The challenge of designing a circuit is to compromise between realizing a set of performance metrics and reducing circuit complexity and noise sensitivity. At the same time, one needs to explore a large design space, and computational approaches often yield long simulation times. Here, we automate the circuit design task using SCILLA. The software SCILLA performs a parallelized, closed-loop optimization to design superconducting circuit diagrams that match predefined properties, such as spectral features and noise sensitivities. We employ it to design 4-local couplers for superconducting flux qubits and identify a circuit that outperforms an existing proposal with a similar circuit structure in terms of coupling strength and noise resilience for experimentally accessible parameters. This work demonstrates how automated design can facilitate the development of complex circuit architectures for quantum information processing.
Quantum algorithms will likely play a key role in future high-performance-computing (HPC) environments. These algorithms are typically expressed as quantum circuits composed of arbitrary gates or as unitary matrices. Executing these on physical devices, however, requires translation to device-compatible circuits, in a process called quantum compilation or circuit synthesis, since these devices support a limited number of native gates. Moreover, these devices typically have specific qubit topologies, which constrain how and where gates can be applied. Consequently, logical qubits in input circuits and unitaries may need to be mapped to and routed between physical qubits. Furthermore, current Noisy Intermediate-Scale Quantum (NISQ) devices present additional constraints. They are vulnerable to errors during gate application and their short decoherence times lead to qubits rapidly succumbing to accumulated noise and possibly corrupting computations. Therefore, circuits synthesized for NISQ devices need to minimize gates and execution times. The problem of synthesizing device-compatible circuits, while optimizing for low gate count and short execution times, can be shown to be computationally intractable using analytical methods. Therefore, interest has grown towards heuristics-based synthesis techniques, which are able to produce approximations of the desired algorithm, while optimizing depth and gate-count. In this work, we investigate using genetic algorithms (GA)—a proven gradient-free optimization technique based on natural selection—for circuit synthesis. In particular, we formulate the quantum synthesis problem as a multi-objective optimization (MOO) problem, with the objectives of minimizing the approximation error, number of multi-qubit gates, and circuit depth. We also employ fuzzy logic for runtime parameter adaptation of GA to enhance search efficiency and solution quality in our proposed method.
Superconducting circuits have emerged as a promising platform to build quantum processors. The challenge of designing a circuit is to compromise between realizing a set of performance metrics and reducing circuit complexity and noise sensitivity. At the same time, one needs to explore a large design space, and computational approaches often yield long simulation times. Here we automate the circuit design task using SCILLA, a software for automated discovery of superconducting circuits. SCILLA performs a parallelized, closed-loop optimization to design circuit diagrams that match pre-defined properties such as spectral features and noise sensitivities. We employ it to discover 4-local couplers for superconducting flux qubits and identify a circuit that outperforms an existing proposal with similar circuit structure in terms of coupling strength and noise resilience for experimentally accessible parameters. This work demonstrates how automated discovery can facilitate the design of complex circuit architectures for quantum information processing.
This paper explores the potential for algorithmic self-assembly to revolutionize quantum circuit design. Traditionally, quantum circuit design is a highly manual and often inefficient process, heavily reliant on expert intuition and trial-and-error. We propose a novel approach where a quantum circuit is treated as a self-assembling system, governed by an evolutionary algorithm. This algorithm iteratively refines circuit topology and gate parameters to meet pre-specified performance criteria – such as fidelity, circuit depth, and gate count. The core claim is that by mimicking the self-assembly behavior observed in biological systems, we can automate the creation of optimized quantum circuits. This work formalizes this process, outlining a genetic algorithm that drives the automated design. The system's ability to explore a vast design space and adapt to complex performance requirements represents a significant advancement in the field, potentially unlocking the full capabilities of quantum computation. This paper details the algorithm's architecture, the optimization criteria, and the underlying mathematical framework, showcasing a viable path towards automated quantum circuit construction.
Quantum interactions are strikingly different from their classical counterparts, and they lead to counter-intuitive effects such as quantum entanglement. The advances in quantum science and technology over the past two decades have shown that such interactions can be harnessed in quantum devices to address hard computational problems. The field of quantum computation has emerged, and superconducting circuits have become a leading platform for quantum processors. Because of the large design space and the difficulty to simulate design prototypes on a classical computer, a key challenge in quantum processor engineering is to translate desired quantum characteristics into a circuit design. A particular obstacle has been the implementation of interactions between multiple quantum bits (qubits) in one coupling mechanism. Multi-qubit interactions are necessary for a number of applications in quantum annealing, analog quantum simulation, and digital quantum computation. In this work, we develop classical and quantum computational methods to address the challenge of implementing such interactions. We present a computational method for automated design of superconducting circuits. Our method performs a parallelized, closed-loop optimization to design superconducting circuit diagrams that match predefined properties, such as dispersion features and noise sensitivities. We employ it to design a coupler for superconducting flux qubits that mediates 4-qubit interactions. The coupler circuit is then realized experimentally. We find that it has the desired ground state dispersion properties versus flux and that it does not adversely affect the lifetime of a connected flux qubit. Moreover, the coupling concept can be extended by systematically adding loops to the coupler. In a second iteration, we implement a circuit architecture in which the coupler mediates interactions between three flux qubits. The system Hamiltonian is estimated via multi-qubit pulse sequences that implement Ramsey-type interferometry between all neighboring excitation manifolds. We verify that the multi-qubit system exhibits a 3-qubit coupling that is coherently tunable, which is important for practical applications. Finally, we address the challenge that complex multi-qubit circuits are difficult to simulate accurately on a classical computer as they become larger. As a solution, we propose quantum algorithms to design and test the performance of next-generation quantum hardware on a quantum computer. We have therefore addressed the challenge of quantum device design both from a classical optimization and from a quantum computational angle.
This paper explores the application of genetic algorithms (GAs) to optimize quantum circuits. The core claim is that GA can effectively improve the efficiency and accuracy of quantum computation by strategically modifying the topology and control parameters of quantum circuits. The proposed mechanism treats a quantum circuit as a genetic individual, leveraging the principles of genetic algorithms – including crossover, mutation, and selection – to iteratively enhance circuit performance. This approach represents a novel method for quantum circuit optimization, offering a potentially robust and adaptable solution compared to traditional optimization techniques. We present a framework for implementing this approach, focusing on the algorithmic steps and considerations for successful application. The exploration will cover the challenges and potential benefits of integrating evolutionary algorithms with the unique characteristics of quantum computation. ---