Efficient modeling of superconducting quantum circuits with tensor networks

Paolo, Agustín Di; Baker, Thomas E.; Foley, Alexandre; Sénéchal, David; Blais, Alexandre

doi:10.1038/s41534-020-00352-4

Cited by 23 publications

(14 citation statements)

References 59 publications

(116 reference statements)

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“…While it is trivially demonstrated from QLR that excitations can be found, the performance of block Lanczos will allow for the resolving of degeneracies with greater ease. This was recently demonstrated in tensor network algorithms [39,40].…”

Section: Introductionmentioning

confidence: 79%

Block Lanczos method for excited states on a quantum computer

Baker

2021

Preprint

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The method of quantum Lanczos recursion is extended to solve for multiple excitations on the quantum computer. While quantum Lanczos recursion is in principle capable of obtaining excitations, the extension to a block Lanczos routine can resolve degeneracies with better precision and only costs O(d 2 ) for d excitations on top of the previously introduced quantum Lanczos recursion method. The formal complexity in applying all operators to the system at once with oblivious amplitude amplification is exponential, but this cost can be kept small to obtain the ground state by incrementally adding operators. The error of the ground state energy based on the accuracy of the Lanczos coefficients is investigated and the error of the ground state energy. It is demonstrated to scale linearly with the uncertainty of the Lanczos coefficients. Extension to non-Hermitian operators is also discussed.

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Section: Introductionmentioning

confidence: 79%

Block Lanczos method for excited states on a quantum computer

Baker

2021

Preprint

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“…To maximize the qubit coherence time, the superinductor must be engineered such that its self-resonant modes are far detuned from the qubit operation frequency [75,76]. Furthermore, in the case of Josephson-junction-array-based superinductances, special care must be taken to avoid introducing charge dispersion to the qubit transition freqeuncy due to quantum phase slips in the superinductance [60,77].…”

Section: B High Coherence Of Charge and Flux Modesmentioning

confidence: 99%

Moving beyond the transmon: Noise-protected superconducting quantum circuits

Gyenis,

Di Paolo,

Koch

et al. 2021

Preprint

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Artificial atoms realized by superconducting circuits offer unique opportunities to store and process quantum information with high fidelity. Among them, implementations of circuits that harness intrinsic noise protection have been rapidly developed in recent years. These noise-protected devices constitute a new class of qubits in which the computational states are largely decoupled from local noise channels. The main challenges in engineering such systems are simultaneously guarding against both bit-and phase-flip errors, and also ensuring high-fidelity qubit control. Although partial noise protection is possible in superconducting circuits relying on a single quantum degree of freedom, the promise of complete protection can only be fulfilled by implementing multimode or hybrid circuits. This Perspective reviews the theoretical principles at the heart of these new qubits, describes recent experiments, and highlights the potential of robust encoding of quantum information in superconducting qubits.

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“…In particular, the tensor network based techniques have proven their power in constructing effective simulators of rather large quantum circuits with memory requirements that scale in accordance with the quantum state entanglement properties [24,31]. More generally, tensor processing has been recognized as a computing technique applicable to many scientific and engineering domains [13,17,32,33] that has resulted in highly-optimized software leveraging the state-of-the-art classical hardware capabilities to simulate complex physical phenomena [29].…”

Section: Introductionmentioning

confidence: 99%

Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

Nguyen¹,

Lyakh²,

Dumitrescu³

et al. 2021

Preprint

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The numerical simulation of quantum circuits is an indispensable tool for development, verification and validation of hybrid quantum-classical algorithms on near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. We present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable tensor network processing library, ExaTN, and provides multiple configurable quantum circuit simulators enabling either exact quantum circuit simulation via the full tensor network contraction, or approximate quantum state representations via suitable tensor factorizations. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical backend we introduce an end-to-end virtual quantum development environment which can scale from laptops to future exascale platforms. We report initial benchmarks of our framework which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on the Google Sycamore superconducting architecture.

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Efficient modeling of superconducting quantum circuits with tensor networks

Cited by 23 publications

References 59 publications

Block Lanczos method for excited states on a quantum computer

Block Lanczos method for excited states on a quantum computer

Moving beyond the transmon: Noise-protected superconducting quantum circuits

Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

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