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Aalto University KQCircuits: Aalto University software package and open-source-license file for KQCircuits, a design software for superconducting qubits and other circuits.

Johannes Heinsoo

Published May 15, 2021• 0 citations
OpenAlex

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Pitkän koherenssiajan kubittien suunnittelu ja mallinnus käyttäen sähkömagneettisen energian jakautumismallia

Savola, Niko

Quantum computers are devices that use quantum phenomena for information processing, with the advantage of solving specific problems faster than classical computers. Superconducting circuits with Josephson junctions have emerged as one of the most promising platforms for large-scale quantum computing in the last decade. However, in the current noisy intermediate-scale quantum era, circuits and algorithms suffer from low fidelities and limited amount of superconducting quantum bits, or qubits. Short coherence times owing to undesired transitions between the excited and ground states of a qubit, are one of the primary sources of error. This work studies the contribution of losses arising from two-level systems, localized predominantly in material interfaces, on the coherence time for conventional superconducting-qubit geometries. This is accomplished by using the method of energy participation ratios with electromagnetic finite-element-method simulations. First, we compare energy participations in two-dimensional cross-sections to those in a similar three-dimensional environment with a thin-sheet approximation for metal thickness and no modelling of lossy material interfaces. After validating the approximations, the method of energy participation ratios is further applied to analyze real-world qubit designs, demonstrating how the approach can facilitate the development of high-coherence superconducting quantum devices. Individual design parameter sweeps are also conducted to provide insight into dependencies on the geometry. We discover that the electric field is concentrated near the Josephson junction area, and the resulting participations are primarily influenced by the lengths of etched gaps between metals in a chip. A Bayesian machine-learning-based optimizer is introduced to systematically discover design geometries that improve qubit relaxation time. This approach is applied to a modified version of a common qubit geometry defined by splines. Furthermore, the workflow is implemented in KQCircuits, a GPLv3-licensed open-source layout design tool for designing state-of-the-art superconducting circuits and processors.

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Preprint by Seth Douglas (seth.douglas@gmail.com) on the dimension and actual initial-entropy rate region of a closed device serving repeated uses of one fixed quantum channel against adaptive inputs. Includes the complete manuscript, appendices and reproducibility materials. Scoped licenses: the manuscript, appendices, PDF and TeX are licensed under Creative Commons Attribution 4.0 International (CC-BY-4.0). Original code and accompanying software documentation are MIT-licensed. These are separate file-specific licenses, not a choice of license for every file. See LICENSE.md and LICENSES/ in the archive. The aggregate Other (Open) category represents this mixed-license package. Lean coverage is partial: a finite quantum closing identity and six arithmetic lemmas, not the full entropy or asymptotic theorem. AI assistance is disclosed in the manuscript. No efficient circuit synthesis or AK resolution is claimed.

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VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

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We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summary Program Title: VAN-DAMME CPC Library link to program files:: https://doi.org/10.17632/zcgw2n5bjf.1 Licensing provisions: GNU General Public License 3 Programming language: C++ and CUDA Nature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability . Solution method: Quantum control , GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

Computer Physics Communications•Oct 17, 2024•4 citations
Quantum computers are devices that use quantum phenomena for information processing, with the advantage of solving specific problems faster than classical computers. Superconducting circuits with Josephson junctions have emerged as one of the most promising platforms for large-scale quantum computing in the last decade. However, in the current noisy intermediate-scale quantum era, circuits and algorithms suffer from low fidelities and limited amount of superconducting quantum bits, or qubits. Short coherence times owing to undesired transitions between the excited and ground states of a qubit, are one of the primary sources of error. This work studies the contribution of losses arising from two-level systems, localized predominantly in material interfaces, on the coherence time for conventional superconducting-qubit geometries. This is accomplished by using the method of energy participation ratios with electromagnetic finite-element-method simulations. First, we compare energy participations in two-dimensional cross-sections to those in a similar three-dimensional environment with a thin-sheet approximation for metal thickness and no modelling of lossy material interfaces. After validating the approximations, the method of energy participation ratios is further applied to analyze real-world qubit designs, demonstrating how the approach can facilitate the development of high-coherence superconducting quantum devices. Individual design parameter sweeps are also conducted to provide insight into dependencies on the geometry. We discover that the electric field is concentrated near the Josephson junction area, and the resulting participations are primarily influenced by the lengths of etched gaps between metals in a chip. A Bayesian machine-learning-based optimizer is introduced to systematically discover design geometries that improve qubit relaxation time. This approach is applied to a modified version of a common qubit geometry defined by splines. Furthermore, the workflow is implemented in KQCircuits, a GPLv3-licensed open-source layout design tool for designing state-of-the-art superconducting circuits and processors.
Preprint by Seth Douglas (seth.douglas@gmail.com) on the dimension and actual initial-entropy rate region of a closed device serving repeated uses of one fixed quantum channel against adaptive inputs. Includes the complete manuscript, appendices and reproducibility materials. Scoped licenses: the manuscript, appendices, PDF and TeX are licensed under Creative Commons Attribution 4.0 International (CC-BY-4.0). Original code and accompanying software documentation are MIT-licensed. These are separate file-specific licenses, not a choice of license for every file. See LICENSE.md and LICENSES/ in the archive. The aggregate Other (Open) category represents this mixed-license package. Lean coverage is partial: a finite quantum closing identity and six arithmetic lemmas, not the full entropy or asymptotic theorem. AI assistance is disclosed in the manuscript. No efficient circuit synthesis or AK resolution is claimed.
We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summary Program Title: VAN-DAMME CPC Library link to program files:: https://doi.org/10.17632/zcgw2n5bjf.1 Licensing provisions: GNU General Public License 3 Programming language: C++ and CUDA Nature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability . Solution method: Quantum control , GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.