Resource estimate for quantum many-body ground-state preparation on a quantum computer

Lemieux, Jessica; Duclos-Cianci, Guillaume; Sénéchal, David; Poulin, David

doi:10.1103/physreva.103.052408

Cited by 17 publications

(14 citation statements)

References 35 publications

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“…This is surprising given the observed improvement obtained from rewind in Ref. [19] and our expectation that the unitary algorithm behaves essentially like Zeno without rewind.…”

Section: Numerical Resultsmentioning

confidence: 78%

“…It was first described in the context of Zeno state preparation in Ref. [19], but originates from Refs. [20,21].…”

Section: B Zeno With Rewindmentioning

confidence: 99%

“…The minimal total time to solution is obtained by minimizing over L. In Ref. [19], it was found that rewinding yields substantial savings compared to the regular Zeno strategy for the preparation of quantum many-body ground states.…”

Section: B Zeno With Rewindmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Quantum Walk Circuits for Metropolis-Hastings Algorithm

Lemieux,

Heim,

Poulin

et al. 2019

Preprint

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We present a detailed circuit implementation of Szegedy's quantization of the Metropolis-Hastings walk. This quantum walk is usually defined with respect to an oracle. We find that a direct implementation of this oracle requires costly arithmetic operations and thus reformulate the quantum walk in a way that circumvents the implementation of that specific oracle and which closely follows the classical Metropolis-Hastings walk. We also present heuristic quantum algorithms that use the quantum walk in the context of discrete optimization problems and numerically study their performances. Our numerical results indicate polynomial quantum speedups in heuristic settings.

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“…This is surprising given the observed improvement obtained from rewind in Ref. [19] and our expectation that the unitary algorithm behaves essentially like Zeno without rewind.…”

Section: Numerical Resultsmentioning

confidence: 78%

“…It was first described in the context of Zeno state preparation in Ref. [19], but originates from Refs. [20,21].…”

Section: B Zeno With Rewindmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Quantum Walk Circuits for Metropolis-Hastings Algorithm

Lemieux,

Heim,

Poulin

et al. 2019

Preprint

Self Cite

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“…Quantum computers promise substantial speedup over conventional machines for many important applications [47,66,74,85]. Unfortunately, high error-rates of quantum devices (about 1% on existing hardware [7]) limit us from running these applications as they require much lower error-rates (below 10 −10 ) [33,41,43,44]. To bridge this gap, quantum information must be protected by using Quantum Error Correction (QEC).…”

Section: Introductionmentioning

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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“…Quantum computers promise substantial speedup over conventional machines for many important applications [38,52]. Unfortunately, high error-rates of quantum devices (about 1% on existing hardware [6]) limit us from running these applications as they require lower error-rates (below 10 −10 ) [27,34,36,37]. To bridge this gap, quantum information must be protected by using Quantum Error Correction (QEC).…”

Section: Introductionmentioning

confidence: 99%

LILLIPUT: A Lightweight Low-Latency Lookup-Table Based Decoder for Near-term Quantum Error Correction

Das,

Locharla,

Jones

2021

Preprint

View full text Add to dashboard Cite

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 required to use feedback implemented in quantum algorithms [32]. 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 measurement, and the number of tolerated errors grows with the size of the code. It needs <7% logic on off-the-shelf FPGAs that allows it to be easily integrated alongside the control and readout circuits in existing systems [9]. LILLIPUT incurs a latency of few nanoseconds and enables real-time decoding. We also propose Compressed LUTs (CLUTs) to reduce the memory needed 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 accuracy.

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Resource estimate for quantum many-body ground-state preparation on a quantum computer

Cited by 17 publications

References 35 publications

Efficient Quantum Walk Circuits for Metropolis-Hastings Algorithm

Efficient Quantum Walk Circuits for Metropolis-Hastings Algorithm

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

LILLIPUT: A Lightweight Low-Latency Lookup-Table Based Decoder for Near-term Quantum Error Correction

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