Full-state quantum circuit simulation by using data compression

Wu, Xingfu; Di, Sheng; Dasgupta, Emma Maitreyee; Cappello, Franck; Finkel, Hal; Alexeev, Yuri; Chong, Frederic T.

doi:10.1145/3295500.3356155

Cited by 98 publications

(50 citation statements)

References 62 publications

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“…There are a number of tensornetwork-based simulators developed for such simulations: QFlex [40], AC-QDP [41], Quimb [42], and QTensor [43]. These simulators are typically much faster and more efficient than state vector simulators for shallow circuits [44] such as the circuits in this work. In these tensor simulators, the circuits are not directly represented by tensors, but rather use line graphs, which was proposed by Boixo et al [45].…”

Section: Contraction Complexity and Relation To Tensor-network Simulamentioning

confidence: 99%

Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

et al. 2021

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Our recent work (Ayral et al. in Proceedings of IEEE computer society annual symposium on VLSI, ISVLSI, pp 138–140, 2020. 10.1109/ISVLSI49217.2020.00034) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with fewer qubits and shallower depth. This accommodates the limited number of qubits and short coherence times of quantum processors. This article investigates the impact of different noise sources—readout error, gate error and decoherence—on the success probability of the QDC procedure. We perform detailed noise modeling on the Atos Quantum Learning Machine, allowing us to understand tradeoffs and formulate recommendations about which hardware noise sources should be preferentially optimized. We also describe in detail the noise models we used to reproduce experimental runs on IBM’s Johannesburg processor. This article also includes a detailed derivation of the equations used in the QDC procedure to compute the output distribution of the original quantum circuit from the output distribution of its fragments. Finally, we analyze the computational complexity of the QDC method for the circuit under study via tensor-network considerations, and elaborate on the relation the QDC method with tensor-network simulation methods.

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Section: Contraction Complexity and Relation To Tensor-network Simulamentioning

confidence: 99%

Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

et al. 2021

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“…In contrast, compression of LUTs in LILLIPUT relies on the fact that not all error events are equally likely and error assignments data tend to be sparse. In another work [88], compression has been found to be effective in simulating quantum circuits for program verification.…”

Section: Related Workmentioning

confidence: 99%

LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correction

Das

Locharla

Jones

2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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The error rates of quantum devices are orders of magnitude higher than what is needed to run most quantum applications. To close this gap, Quantum Error Correction (QEC) encodes logical qubits and distributes information using several physical qubits. By periodically executing a syndrome extraction circuit on the logical qubits, information about errors (called syndrome) is extracted while running programs. A decoder uses these syndromes to identify and correct errors in real time, which is necessary to prevent accumulation of errors. Unfortunately, software decoders are slow and hardware decoders are fast but less accurate. Thus, almost all QEC studies so far have relied on offline decoding.To enable real-time decoding in near-term QEC, we propose LILLIPUT-a Lightweight Low Latency Look-Up Table decoder. LILLIPUT consists of two parts-First, it translates syndromes into error detection events that index into a Look-Up Table (LUT) whose entry provides the error information in real-time. Second, it programs the LUTs with error assignments for all possible error events by running a software decoder offline. LILLIPUT tolerates an error on any operation in the quantum hardware, including gates and measurements, and the number of tolerated errors grows with the size of the code. LILLIPUT utilizes less than 7% logic on off-the-shelf FPGAs enabling practical adoption, as FPGAs are already used to design the control and readout circuits in existing systems. LIL-LIPUT incurs a latency of a few nanoseconds and enables real-time decoding. We also propose Compressed LUTs (CLUTs) to reduce the memory required by LILLIPUT. By exploiting the fact that not all error events are equally likely and only storing data for the most probable error events, CLUTs reduce the memory needed by up-to 107x (from 148 MB to 1.38 MB) without degrading the accuracy. CCS CONCEPTS• Hardware → Quantum technologies; • Computer systems organization → Quantum computing.

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“…Since all the amplitudes are generally involved in every quantum gate computation, storing the state vector on hard disk would introduce too much I/O overhead to simulate. In order to reduce the footprint in memory, the latest approach is to apply lossy compression (SZ compressor) for floating-point data (quantum state amplitudes) to the quantum circuit simulation (Wu et al 2018a(Wu et al , 2018b.…”

Section: Reducing the Memory Footprintmentioning

confidence: 99%

Use cases of lossy compression for floating-point data in scientific data sets

Cappello

et al. 2019

The International Journal of High Performance Computing Applica

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110

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Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. This article surveys and presents experimental results of currently identified use cases of generic lossy compression to address the different limitations of scientific computing systems. The article shows from a collection of experiments run on parallel systems of a leadership facility that lossy data compression not only can reduce the footprint of scientific data sets on storage but also can reduce I/O and checkpoint/restart times, accelerate computation, and even allow significantly larger problems to be run than without lossy compression. These results suggest that lossy compression will become an important technology in many aspects of high performance scientific computing. Because the constraints for each use case are different and often conflicting, this collection of results also indicates the need for more specialization of the compression pipelines.

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Full-state quantum circuit simulation by using data compression

Cited by 98 publications

References 62 publications

Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correction

Use cases of lossy compression for floating-point data in scientific data sets

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