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Bulletin of the American Physical Society

Noise-Aware Qubit Allocation Techniques for NISQ Devices

Michael Cubeddu, Will Finigan, Vitali Vinokour, Prineha Narang

Published Mar 2, 2020• 0 citations
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Quantum Neural Networks (QNNs) based on parameterized quantum circuits (PQCs) are gaining significant research attention due to their potential to achieve quantum advantages on Near-Term Noisy Intermediate-Scale Quantum (NISQ) computers. However, executing QNNs on NISQ devices is challenging due to quantum noise. To address this, we propose a noise-resilient QNN (NR-QNN) that leverages the unique characteristics of PQCs to perform noise-aware optimizations during the inference stage of QNNs. Specifically, NR-QNN employs two optimization techniques to mitigate the impact of noise on QNNs: quantum pruning and sensitivity-aware qubit mapping. The first technique is quantum pruning which identifies gates with small angle of rotations and removes them to simplify circuit of QNNs. The second optimization technique is sensitivity-aware qubit mapping which maps more important logical qubits to more reliable physical qubits. This technique is based on the observation that there can be variation in the sensitivity of an output to input qubits in a QNN. NR-QNN exploits this variability and guides qubit allocation to use reliable physical qubits for sensitive logical qubits. Our evaluation on a real quantum computer demonstrates that NR-QNN enhances the robustness of QNNs, enabling them to operate effectively on NISQ devices.

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Concerted efforts by the academia and the industries e.g., IBM, Google and Intel have brought us to the era of Noisy Intermediate-Scale Quantum (NISQ) computers. Qubits, the basic elements of quantum computer, have been proven extremely susceptible to different noises. Recent experiments have exhibited spatial variations among the qubits in NISQ hardware. Therefore, conventional mapping of qubit done without quality awareness results in significant loss of fidelity for a given workload. In this paper, we have analyzed the effects of various noise sources on the overall fidelity of the given workload for a real NISQ hardware. We have also presented novel optimization technique namely, Qubit Re-allocation (QURE) to maximize the sequence fidelity of a given workload. QURE is scalable and can be applied to future large scale quantum computers. QURE can improve the fidelity of a quantum workload up to 1.54X (1.39X on average) in simulation and up to 1.7X in real device compared to variation oblivious qubit allocation without incurring any physical overhead.

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Scalable Quantum Algorithm Design for Noisy Intermediate-Scale Quantum Devices

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Designing quantum algorithms that scale gracefully with problem size on NISQ devices -- maintaining useful approximation quality as qubit count, circuit depth, and noise accumulation increase -- remains the central engineering challenge of the NISQ era. Current variational quantum algorithms face two fundamental scalability barriers: barren plateaus (exponentially vanishing gradients that prevent training of deep parameterised circuits) and noise accumulation (gate errors compounding with circuit depth, degrading output fidelity). This paper proposes the Scalable NISQ Algorithm Design (SNAD) framework, a systematic methodology for designing quantum algorithms that remain effective as problem scale increases, comprising four design principles and associated algorithmic techniques: locality-structured ansatz design (LSAD) to avoid barren plateaus through geometric locality constraints; adaptive noise budget allocation (ANBA) distributing gate operations to maximise fidelity within noise constraints; circuit compression and synthesis (CCS) reducing circuit depth through gate cancellation and re-synthesis; and scalability-aware performance prediction (SAPP) using classical simulation to predict NISQ performance at target scale before hardware execution. SNAD is validated on QAOA and VQE circuits across n = 12-84 qubits on IBM Quantum Eagle and Falcon. LSAD eliminates barren plateaus for n up to 84 qubits, maintaining gradient magnitude > 10^-3 where standard random ansatz shows < 10^-8 at n=48. CCS reduces circuit depth by 28.4% (SD = 4.8%) for QAOA circuits on heavy-hex topology. ANBA improves approximation ratio by 6.4pp vs. uniform depth allocation under fixed noise budget. SAPP predicts NISQ approximation ratios with mean error 3.2% (SD = 1.4%) across 284 circuits.

Quantum Frontiers Journal•Mar 28, 2024•0 citations
Quantum Neural Networks (QNNs) based on parameterized quantum circuits (PQCs) are gaining significant research attention due to their potential to achieve quantum advantages on Near-Term Noisy Intermediate-Scale Quantum (NISQ) computers. However, executing QNNs on NISQ devices is challenging due to quantum noise. To address this, we propose a noise-resilient QNN (NR-QNN) that leverages the unique characteristics of PQCs to perform noise-aware optimizations during the inference stage of QNNs. Specifically, NR-QNN employs two optimization techniques to mitigate the impact of noise on QNNs: quantum pruning and sensitivity-aware qubit mapping. The first technique is quantum pruning which identifies gates with small angle of rotations and removes them to simplify circuit of QNNs. The second optimization technique is sensitivity-aware qubit mapping which maps more important logical qubits to more reliable physical qubits. This technique is based on the observation that there can be variation in the sensitivity of an output to input qubits in a QNN. NR-QNN exploits this variability and guides qubit allocation to use reliable physical qubits for sensitive logical qubits. Our evaluation on a real quantum computer demonstrates that NR-QNN enhances the robustness of QNNs, enabling them to operate effectively on NISQ devices.
Concerted efforts by the academia and the industries e.g., IBM, Google and Intel have brought us to the era of Noisy Intermediate-Scale Quantum (NISQ) computers. Qubits, the basic elements of quantum computer, have been proven extremely susceptible to different noises. Recent experiments have exhibited spatial variations among the qubits in NISQ hardware. Therefore, conventional mapping of qubit done without quality awareness results in significant loss of fidelity for a given workload. In this paper, we have analyzed the effects of various noise sources on the overall fidelity of the given workload for a real NISQ hardware. We have also presented novel optimization technique namely, Qubit Re-allocation (QURE) to maximize the sequence fidelity of a given workload. QURE is scalable and can be applied to future large scale quantum computers. QURE can improve the fidelity of a quantum workload up to 1.54X (1.39X on average) in simulation and up to 1.7X in real device compared to variation oblivious qubit allocation without incurring any physical overhead.
Designing quantum algorithms that scale gracefully with problem size on NISQ devices -- maintaining useful approximation quality as qubit count, circuit depth, and noise accumulation increase -- remains the central engineering challenge of the NISQ era. Current variational quantum algorithms face two fundamental scalability barriers: barren plateaus (exponentially vanishing gradients that prevent training of deep parameterised circuits) and noise accumulation (gate errors compounding with circuit depth, degrading output fidelity). This paper proposes the Scalable NISQ Algorithm Design (SNAD) framework, a systematic methodology for designing quantum algorithms that remain effective as problem scale increases, comprising four design principles and associated algorithmic techniques: locality-structured ansatz design (LSAD) to avoid barren plateaus through geometric locality constraints; adaptive noise budget allocation (ANBA) distributing gate operations to maximise fidelity within noise constraints; circuit compression and synthesis (CCS) reducing circuit depth through gate cancellation and re-synthesis; and scalability-aware performance prediction (SAPP) using classical simulation to predict NISQ performance at target scale before hardware execution. SNAD is validated on QAOA and VQE circuits across n = 12-84 qubits on IBM Quantum Eagle and Falcon. LSAD eliminates barren plateaus for n up to 84 qubits, maintaining gradient magnitude > 10^-3 where standard random ansatz shows < 10^-8 at n=48. CCS reduces circuit depth by 28.4% (SD = 4.8%) for QAOA circuits on heavy-hex topology. ANBA improves approximation ratio by 6.4pp vs. uniform depth allocation under fixed noise budget. SAPP predicts NISQ approximation ratios with mean error 3.2% (SD = 1.4%) across 284 circuits.