Hidden Inverses: Coherent Error Cancellation at the Circuit Level

Zhang, Bichen; Majumder, Swarnadeep; Leung, Pak Hong; Crain, Stephen; Wang, Ye; Fang, Chao; Debroy, Dripto M.; Kim, Jungsang; Brown, Kenneth R.

doi:10.48550/arxiv.2104.01119

Cited by 4 publications

(4 citation statements)

References 51 publications

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“…In this section, we test the effectiveness of a circuit level error mitigation technique called hidden inverse [47,48] to mitigate precisely this type of error. The key idea behind this error mitigation technique is that each self-inverse gate (such as CNOT) can be experimentally implemented in standard or inverted configuration.…”

Section: Reducing Noise With Hidden Inversementioning

confidence: 99%

Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms

et al. 2021

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Quantum chemistry is a key application area for noisy-intermediate scale quantum (NISQ) devices, and therefore serves as an important benchmark for current and future quantum computer performance. Previous benchmarks in this field have focused on variational methods for computing ground and excited states of various molecules, including a benchmarking suite focused on the performance of computing ground states for alkali-hydrides under an array of error mitigation methods. State-of-the-art methods to reach chemical accuracy in hybrid quantum-classical electronic structure calculations of alkali hydride molecules on NISQ devices from IBM are outlined here. It is demonstrated how to extend the reach of variational eigensolvers with symmetry preserving Ansätze. Next, it is outlined how to use quantum imaginary time evolution and Lanczos as a complementary method to variational techniques, highlighting the advantages of each approach. Finally, a new error mitigation method is demonstrated which uses systematic error cancellation via hidden inverse gate constructions, improving the performance of typical variational algorithms. These results show that electronic structure calculations have advanced rapidly, to routine chemical accuracy for simple molecules, from their inception on quantum computers a few short years ago, and they point to further rapid progress to larger molecules as the power of NISQ devices grows.

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Section: Reducing Noise With Hidden Inversementioning

confidence: 99%

Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms

et al. 2021

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“…As such, many noise-adaptive quantum compilers have been proposed. For example, various gate errors can be suppressed by dynamical decoupling [8], [23], [35], [59], composite pulses [10], [40], [45], randomized compiling [60], hidden inverses [64], qubit mapping [34], [47], [57], instruction scheduling [48], [62], and frequency tuning [16], [26], [58]. Typically, the key to these techniques is to find opportunities for local error cancellation within a quantum circuit.…”

Section: B Noise-adaptive Quantum Compilingmentioning

confidence: 99%

QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

Wang¹,

Ding²,

Gu³

et al. 2021

Preprint

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Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research efforts have explored a higher level of optimization by making the quantum circuits themselves resilient to noise.In this paper, we propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing quantum neural networks for machine learning and variational ansatzes for quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search and parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates (e.g., U3 and CU3) and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. Then we perform an evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates in a fine-grained manner.Extensively evaluated with 12 quantum machine learning (QML) and variational quantum eigensolver (VQE) benchmarks on 10 quantum computers, QuantumNAS significantly outperforms noise-unaware search, human, random, and existing noiseadaptive qubit mapping baselines. For QML tasks, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real quantum computers. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD baselines. We also open-source QuantumEngine for fast training of parameterized quantum circuits to facilitate future research.

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“…Any asymmetry in the errors will affect the basis states differently and possibly cause certain states to not meet pseudo-threshold. For example, coherent errors are known to have a larger impact on smaller distance codes compared to larger distance codes [65] unless specifically designed to be robust against coherent errors [66][67][68][69][70][71]. We did not directly study coherent errors in this section since it would require full simulation and we leave such detailed comparisons to future work.…”

Section: B Quantum Error Correctionmentioning

confidence: 99%

Re-examining the quantum volume test: Ideal distributions, compiler optimizations, confidence intervals, and scalable resource estimations

Baldwin,

Mayer,

Brown

et al. 2021

Preprint

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The quantum volume test is a full-system benchmark for quantum computers that is sensitive to qubit number, fidelity, connectivity, and other quantities believed to be important in building useful devices. The test was designed to produce a single-number measure of a quantum computer's general capability, but a complete understanding of its limitations and operational meaning is still missing. We explore the quantum volume test to better understand its design aspects, sensitivity to errors, passing criteria, and what passing implies about a quantum computer. We elucidate some transient behaviors the test exhibits for small qubit number including the ideal measurement output distributions and the efficacy of common compiler optimizations. We then present an efficient algorithm for estimating the expected heavy output probability under different error models and compiler optimization options, which predicts performance goals for future systems. Additionally, we explore the original confidence interval construction and show that it underachieves the desired coverage level for single shot experiments and overachieves for more typical number of shots. We propose a new confidence interval construction that reaches the specified coverage for typical number of shots and is more efficient in the number of circuits needed to pass the test. We demonstrate these savings with a QV = 2 10 experimental dataset collected from Honeywell System Model H1. Finally, we discuss what the quantum volume test implies about a quantum computer's practical or operational abilities especially in terms of quantum error correction.

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Hidden Inverses: Coherent Error Cancellation at the Circuit Level

Cited by 4 publications

References 51 publications

Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms

Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms

QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

Re-examining the quantum volume test: Ideal distributions, compiler optimizations, confidence intervals, and scalable resource estimations

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