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Physical Review A

Robust hybrid qubit plane architecture for enhancing lattice-surgery-based surface codes

Sengthai Heng, Sovanmonynuth Heng, Dongmin Kim, Youngsun Han

Published Feb 29, 2024• 1 citation
Publisher

Abstract

Quantum computing offers unprecedented speed and power for solving complex problems, but its susceptibility to errors makes error-correction codes crucial. An important metric for evaluating the performance of error-correction codes is the code distance, which measures the number of errors a code can correct. Current qubit-plane architectures are implemented using the same code distance. However, a larger code distance reduces the logical error rate but increases resources. In fact, distance-5 code uses more resources but offers higher resilience to logical errors compared to distance-3 code. We propose a qubit-plane architecture for rotated and unrotated surface codes called Qentaurus that combines various code distances of logical qubits for robust error correction while maintaining qubit efficiency. Qentaurus is designed to be highly scalable regarding computation, layout, and code distance. We performed a case study using Qentaurus with distance-3 and -7 codes to evaluate the performance. Moreover, we additionally developed a qubit placement scheme that considers the critical path of the quantum circuit to reduce the logical error rate and gate count. Our main experimental results demonstrated that Qentaurus considerably reduces the overall logical error rate (by 15.76% on average and up to 25.25%) than that of the Distance-3 Checkerboard architecture. After additionally implementing the qubit placement scheme, the average reduction increases to 13.44% (up to 39.07%). Notably, the Qentaurus-37 maintains logical error rates comparable to those of the Distance-5 Checkerboard, achieving considerable qubit efficiency. The Distance-5 Checkerboard demands 227.7% more physical qubits than that required by the Distance-3 Checkerboard, while Qentaurus-37 utilizes just 131.2% of the physical qubits required by Distance-3 the Checkerboard, marking a substantial reduction of 96.5% in the resource gap. The overall gate count is reduced by an average of 1.74% with Qentaurus alone and by 3.33% on average with the qubit placement scheme. We believe that the proposed Qentaurus and qubit placement scheme have the potential to significantly improve the error-correction performance of large-scale quantum computers in practical applications.

DOI
10.1103/physreva.109.022440

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Quantum computing offers unprecedented speed and power for solving complex problems, but its susceptibility to errors makes error-correction codes crucial. An important metric for evaluating the performance of error-correction codes is the code distance, which measures the number of errors a code can correct. Current qubit-plane architectures are implemented using the same code distance. However, a larger code distance reduces the logical error rate but increases resources. In fact, distance-5 code uses more resources but offers higher resilience to logical errors compared to distance-3 code. We propose a qubit-plane architecture for rotated and unrotated surface codes called Qentaurus that combines various code distances of logical qubits for robust error correction while maintaining qubit efficiency. Qentaurus is designed to be highly scalable regarding computation, layout, and code distance. We performed a case study using Qentaurus with distance-3 and -7 codes to evaluate the performance. Moreover, we additionally developed a qubit placement scheme that considers the critical path of the quantum circuit to reduce the logical error rate and gate count. Our main experimental results demonstrated that Qentaurus considerably reduces the overall logical error rate (by 15.76% on average and up to 25.25%) than that of the Distance-3 Checkerboard architecture. After additionally implementing the qubit placement scheme, the average reduction increases to 13.44% (up to 39.07%). Notably, the Qentaurus-37 maintains logical error rates comparable to those of the Distance-5 Checkerboard, achieving considerable qubit efficiency. The Distance-5 Checkerboard demands 227.7% more physical qubits than that required by the Distance-3 Checkerboard, while Qentaurus-37 utilizes just 131.2% of the physical qubits required by Distance-3 the Checkerboard, marking a substantial reduction of 96.5% in the resource gap. The overall gate count is reduced by an average of 1.74% with Qentaurus alone and by 3.33% on average with the qubit placement scheme. We believe that the proposed Qentaurus and qubit placement scheme have the potential to significantly improve the error-correction performance of large-scale quantum computers in practical applications.
Low-overhead quantum error-correction schemes are essential for enabling quantum computation on registers containing multiple logical qubits. For planar architectures with limited nearest-neighbor qubit connectivity, the surface code has emerged as the leading paradigm. Recent theoretical and experimental work has shown that a physical-qubit connectivity of degree three is sufficient to implement fault-tolerant quantum error correction. In this work, we study lattice surgery in the context of such trivalent architectures and introduce scalable circuit constructions to implement it. Compared with the four-valent measurement scheme, the trivalent lattice-surgery protocol reduces the required resources by O(d)\mathcal{O}(d)O(d) qubits out of a total qubit count of O(d2)\mathcal{O}(d^2)O(d2) and by O(d)\mathcal{O}(d)O(d) two-qubit gates out of a total two-qubit gate count of O(d3)\mathcal{O}(d^3)O(d3). We benchmark the logical fidelity of both lattice-surgery schemes in terms of experimentally realistic simulations targeting an implementation with a fluxonium qubit based architecture and find a potential improvement of up to ≈25%\approx25\%≈25% for distance-three. These results open a way for scalable planar trivalent qubit architectures to host a surface-code-based logical quantum processor.
Abstract Quantum error correction (QEC) and fault-tolerant (FT) mechanisms are essential for reliable quantum computing. However, QEC considerably increases the computation size up to four orders of magnitude. Moreover, FT implementation has specific requirements on qubit layouts, causing both resource and time overhead. Reducing spatial-temporal costs becomes critical since it is beneficial to decrease the failure rate of quantum computation. To this purpose, scalable qubit plane architectures and efficient mapping passes including placement and routing of qubits as well as scheduling of operations are needed. This paper proposes a full mapping process to execute lattice surgery-based quantum circuits on two surface code architectures, namely a checkerboard and a tile-based one. We show that the checkerboard architecture is 2x qubit-efficient but the tile-based one requires lower communication overhead in terms of both operation overhead (up to ∼86%) and latency overhead (up to ∼79%).
In order to achieve error rates necessary for advantageous quantum algorithms, Quantum Error Correction (QEC) will need to be employed, improving logical qubit fidelity beyond what can be achieved physically. As today's devices begin to scale, co-designing architectures for QEC with the underlying hardware will be necessary to reduce the daunting overheads and accelerate the realization of practical quantum computing. In this work, we focus on logical computation in QEC. We address quantum computers made from neutral atom arrays to design a surface code architecture that translates the hardware's higher physical connectivity into a higher logical connectivity. We propose groups of interleaved logical qubits, gaining all-to-all connectivity within the group via efficient transversal CNOT gates. Compared to standard lattice surgery operations, this reduces both the overall qubit footprint and execution time, lowering the spacetime overhead needed for small-scale QEC circuits. We also explore the architecture's scalability. We look at using physical atom movement schemes and propose interleaved lattice surgery which allows an all-to-all connectivity between qubits in adjacent interleaved groups, creating a higher connectivity routing space for large-scale circuits. Using numerical simulations, we evaluate the total routing time of interleaved lattice surgery and atom movement for various circuit sizes. We identify a cross-over point defining intermediate-scale circuits where atom movement is best and large-scale circuits where interleaved lattice surgery is best. We use this to motivate a hybrid approach as devices continue to scale, with the choice of operation depending on the routing distance.
Abstract Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction 1,2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10 −6 logical error per cycle floor set by a single high-energy event (1.6 × 10 −7 excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.
Practical quantum computers will require resource-efficient error-correcting codes. The rotated surface code uses approximately half the number of qubits as the unrotated surface code to create a logical qubit with the same error-correcting distance. However, instead of distance, a more useful qubit-saving metric would be based on logical error rates. In this work we find the well-below-threshold scaling of logical to physical error rates under circuit-level noise for both codes at high odd and even distances and then compare the number of qubits used by each code to achieve equal logical error rates. We perform Monte Carlo sampling of memory experiment circuits with all valid CNOT orders using the stabilizer simulator Stim and the uncorrelated minimum-weight perfect matching decoder PyMatching 2. We find that the rotated code uses about 74 % the number of qubits used by the unrotated code to achieve a logical error rate of p L = 10 − 12 at the operational physical error rate of p = 10 − 3 . The ratio remains ≈ 75 % for p values within a factor of two of p = 10 − 3 for all useful p L . Our work finds the low- p L scaling of the surface code and clarifies the qubit savings provided by the rotated surface code, providing numerical justification for its use in future implementations of the surface code.
Resource consumption of the conventional surface code is expensive, in part due to the need to separate the defects that create the logical qubit far apart on the physical qubit lattice. We propose that instantiating the deformation-based surface code using superstabilizers will make it possible to detect short error chains connecting the superstabilizers, allowing us to place logical qubits close together. Additionally, we demonstrate the process of conversion from the defect-based surface code, which works as arbitrary state injection, and a lattice-surgery-like controlled not (cnot) gate implementation that requires fewer physical qubits than the braiding cnot gate. Finally, we propose a placement design for the deformation-based surface code and analyze its resource consumption; large-scale quantum computation requires 25d2+170d+2894\frac{25{d}^{2}+170d+289}{4}425d2+170d+289​ physical qubits per logical qubit, where ddd is the code distance of the standard surface code, whereas the planar code requires $16{d}^{2}\ensuremath{-}16d+4$ physical qubits per logical qubit, for a reduction of about 50%.