Pith. sign in

REVIEW 4 major objections 3 minor 101 references

Synchronization for Fault-Tolerant Quantum Computers

T0 review · 4 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Splitting a waiting qubit's idle time into short slices across syndrome rounds cuts surface-code logical error rates by up to 2.4x, and adding a few extra error-correction rounds pushes the gain to 3.4x.

desk verdict Useful systems paper on a real FTQC problem; headline LER numbers are model-dependent but the model is disclosed and the artifact is there. read the letter →

arxiv 2506.10258 v1 pith:UA7Q27QE submitted 2025-06-12 quant-ph cs.AR

Satvik Maurya , Swamit Tannu This is my paper
classification quant-phcs.AR
keywords quantumerrorcorrectionsurfacecodelatticesurgerysynchronizationlogicalrateidlingerrorsdynamicaldecouplingfault-tolerantcomputing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Future fault-tolerant quantum computers will often find their logical qubits out of step: magic states arrive at unpredictable times, defective qubits force longer syndrome cycles, and different error-correction codes run at different speeds, so before a lattice-surgery operation the fast logical qubit must wait for the slow one. This paper names that waiting problem and compares three ways to handle it: Passive (idle for the whole slack right before surgery), Active (slice the slack into small idle periods distributed across several syndrome-generation rounds), and Hybrid (run a few additional error-correction rounds while using Active-style small idle periods). The paper's central claim is that Active reduces the logical error rate by up to $2.4\times$ and Hybrid by up to $3.4\times$ compared with Passive, in circuit-level simulations of surface-code lattice surgery at physical error rate $10^{-3}$. These reductions matter because synchronization will be a routine event in large fault-tolerant systems, and lower logical error rates per synchronization translate into deeper circuits and faster decoding.

What carries the argument

The load-bearing mechanism is slack fragmentation: instead of one long idle period, the synchronization slack $\tau$ is divided into short idle segments placed before multiple syndrome-generation rounds, so each qubit's wait is shorter and the total decoherence exposure is lower; the paper verifies the mechanism by showing the syndrome-Hamming-weight spike during lattice surgery is smaller under Active than Passive. The Hybrid variant uses a slack tolerance $\epsilon$ and an integer round count $z$ chosen so that $zT_P + \tau$ is within $\epsilon$ of a multiple of $T'_P$, trading a few extra error-correction rounds for much shorter idle periods.

What would settle it

Run a surface-code lattice-surgery merge on a real device with a controlled 1000 ns synchronization slack between two patches of distance 5 or 7, alternating Passive (all idle time inserted before surgery) and Active (same total idle split across the preceding syndrome rounds), and count logical failures over at least $10^6$ shots. If Active does not lower the logical error rate relative to Passive under the device's actual crosstalk and leakage, the central claim fails in that noise regime.

Watch

Extended reading notes

Core claim

The paper's discovery is a synchronization policy rather than a new code: when two logical patches with equal syndrome-cycle time $T_{\mathrm{cycle}}$ are desynchronized by a slack $\tau$, the leading patch can either wait out $\tau$ immediately before the merge (Passive) or have its next $n$ cycles each slowed by $\tau/n$ (Active). The claim is that the second option lowers the logical error rate of the merged computation by up to $2.4\times$, because each idle fragment is short, the decoherence exposure of every data qubit is smaller, and the syndrome-Hamming-weight spike at the surgery moment is correspondingly reduced. When the two patches have unequal cycle times, the Hybrid policy finds the smallest number $z$ of extra rounds such that $zT_P + \tau$ lands within a slack tolerance $\epsilon$ of a multiple of $T'_P$, then uses a short Active-style idle to finish, yielding up to $3.4\times$ reduction. The numbers come from $10^8$ shots of a stabilizer-circuit simulation with Pauli-twirled idling errors set by $T_1$ and $T_2$ decay times.

Load-bearing premise

The reported error-rate reductions rest on the assumption that idle-time errors are independent and decay exponentially with the qubit relaxation times $T_1$ and $T_2$, with no crosstalk, spectator effects, or leakage; if real idle noise is correlated or non-Markovian, the benefit of splitting the idle time could be larger or smaller than simulated.

Editorial extensions

If this is right

  • A single Active synchronization at slack 1000 ns puts the logical error rate of a distance-15 patch close to that of an ideal system that never needs synchronization, while Passive stays several times worse.
  • Across a full program with many lattice-surgery operations, Passive synchronization can increase the final logical error rate by up to about $23\times$ relative to Active, so the per-operation gain compounds linearly in the number of surgeries.
  • Because Active produces fewer hard-to-decode syndromes, a hierarchical lookup-table-plus-minimum-weight-matching decoder can recover up to $2.2\times$ faster per lattice-surgery operation.
  • For neutral-atom systems with very long coherence times, Active improves on Passive by only about 2% and Hybrid is worse, so Passive waiting is effectively sufficient there.
  • When two patches have equal syndrome-cycle times, no extra-round strategy exists, so Active versus Passive is the only meaningful choice.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A hardware experiment measuring logical error rate rather than single-qubit fidelity would be the next direct test; the current hardware data on physical qubit fidelity does not validate the logical-level $2.4\times$ and $3.4\times$ numbers.
  • The same slack-fragmentation logic should transfer to any QEC code whose logical operations need phase-aligned cycles, such as color codes, qLDPC memories, and twist-based surgery, even though the simulations here are surface-code-only.
  • If future hardware suppresses idle errors far better than current $T_1/T_2$ noise, or if the dominant idle noise is correlated, the advantage of Active over Passive could shrink, so the practical value of the policy is tied to how idling errors actually behave.
  • The runtime microarchitecture described for computing slack and choosing a policy could be extended to pick Passive, Active, or Hybrid per operation based on measured idle-error calibration, making the reported reductions a function of scheduling policy rather than fixed constants.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 3 minor

Summary. The paper addresses a systems-level problem in fault-tolerant quantum computing: synchronization of surface-code logical patches that have fallen out of phase in their syndrome-generation cycles. It introduces three synchronization policies — Passive, Active, and Hybrid — and evaluates them using Stim-based circuit-level simulations of lattice surgery. The principal claims are that Active synchronization reduces the logical error rate by up to 2.4x compared to Passive, Hybrid reduces it by up to 3.4x, and the resulting lower error rates enable a decoing-latency speedup of up to 2.2x. The paper also presents physical qubit experiments on IBM hardware showing that splitting idle time improves mean single-qubit fidelity, and it releases an open-source lattice-surgery simulator artifact.

Significance. The paper tackles a genuinely under-explored problem — synchronization of desynchronized logical patches in heterogeneous FTQC systems — and the proposed Active policy is a simple, plausible idea that could matter for practical architectures. The artifact is a strength: the simulator is released, and the simulation methodology is internally consistent (circuit-level noise, 100M shots, same conditions across policies). The qualitative message that distributing idle time across syndrome rounds helps because errors are corrected between rounds is sound under the stated model. The main caveat is that all quantitative LER claims are computed under an idling model that includes only T1/T2 Pauli errors, with no leakage, crosstalk, or spectator effects, and the hardware validation measures physical fidelity rather than logical error rate. Thus the quantitative headline numbers are real simulation results, but the generalization to actual hardware is not demonstrated. If the paper explicitly conditions these claims on the noise model and adds a sensitivity analysis for the omitted error mechanisms, its contribution is valuable and publishable.

major comments (4)
  1. [Section 6, idling error model] The central LER reductions in Figures 14 and 19 are produced by the idling model p_x = p_y = (1 - e^{-tau/T1})/4, p_z = (1 - e^{-tau/T2})/2 - p_x. This model assumes that the benefit of splitting idle time into N intervals comes from the ability to detect and correct errors after each short interval. However, real idling noise includes leakage and crosstalk, which are not representable as single-qubit Pauli channels. A leakage transition can be event-based rather than time-proportional, so splitting a slack tau into N idle periods can multiply the number of leakage events by N, potentially reversing the Active policy's advantage. The hardware experiment in Figure 6 measures mean physical-qubit fidelity, not logical error rate in a lattice-surgery circuit, so it does not directly constrain the LER claims. Please add simulations that include a leakage/event-based idling component, or at minimum present the LER improvements as explicitly conditional on the idealized model and discuss the conditions under which the policy could lose its benefit.
  2. [Section 7.2.3] The statement "On actual hardware, the improvements with Active synchronization will likely be greater than reported" is not supported by the measurements in the paper. The omitted leakage and crosstalk effects could plausibly reduce the improvement, as argued above. Please remove this unsupported speculation or back it with a concrete model or experiment.
  3. [Section 4.2, Eq. (2)] Equation (2) is garbled: the displayed formula involving z.T_P, T'_P, and epsilon does not parse as a well-formed modular inequality, and it is essential for defining the Hybrid policy. The accompanying example in the text uses tau = 800ns and epsilon = 200ns, while Table 2 uses tau = 1000ns and epsilon = 400ns; the numbers are inconsistent. Please correct the equation and reconcile the example with the table.
  4. [Section 4.2.1] The choice of the Hybrid slack tolerance epsilon = 400ns and the upper bound of 5 extra rounds is presented as a design decision, but no sensitivity analysis is provided beyond Figure 11. Since Figure 19 shows that the Hybrid policy's improvement depends strongly on epsilon, please add a sensitivity sweep or a principled justification for these values based on the expected distributions of slack and cycle times.
minor comments (3)
  1. [Abstract and Section 7.2.1] The abstract and Section 7.2.1 state the 'up to 2.4x' and 'up to 3.4x' LER reductions without qualification, while Section 6 explicitly says the idling model excludes crosstalk, spectator effects, and leakage. Please add a short qualifier in the abstract (e.g., 'under a T1/T2-only idling model') so that the headline claims do not overstate hardware relevance.
  2. [Figure 5(b)] The schematic in Figure 5(b) would be clearer if the placement and duration of each inserted idle interval were explicitly marked on the round-by-round timeline; currently it is easy to misread the Active policy as a single continuous idle rather than a distribution over rounds.
  3. [Section 3.4.1] The sentence 'From Figure 4(a), we assume the slack to be 500/1000ns for all evaluations' is abrupt; please add one or two sentences explaining why the median and worst-case values from the magic-state cultivation case study are representative across other desynchronization sources (qLDPC teleportation, dropouts, twist-based surgery).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the LER reductions are simulation outputs under a stated idling-error model, and the hardware experiments independently support the splitting-idle benefit.

full rationale

The paper's central 2.4x/3.4x reductions are produced by Stim simulations over 100M shots (Sections 7.2.1 and 7.3.1), not by an equation that assumes the result. The idling error model in Section 6, p_x=p_y=(1-e^{-tau/T1})/4 and p_z=(1-e^{-tau/T2})/2-p_x, assigns error probability as a function of idle duration; it does not encode a preference for splitting the slack, and to first order the total Pauli error probability is the same for one long idle and many short idles of equal total length. The Active policy's advantage therefore emerges from the interaction of the inserted idle segments with syndrome extraction and decoding, which is an independent simulation result. The hardware experiments in Figure 6 directly compare contiguous versus split idling on IBM Brisbane and show improved physical fidelity for the split schedule, providing external support for the mechanism. The slack values (500/1000 ns) and tolerance epsilon (400 ns) are chosen from case studies and design considerations and are not fitted to maximize the reported reductions. The only self-citation is the simulator artifact [69], which is open-source and not used as evidence for the headline claims. Concerns that leakage, crosstalk, or spectator effects could alter or reverse the benefit are model-completeness and correctness risks, not circularity: the paper explicitly flags these omissions in Sections 6 and 7.2.3. No derived quantity reduces to its own definition, and no load-bearing argument rests on a self-citation chain.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claims rest on standard QEC/lattice surgery domain assumptions and on a simplified idling error model. The paper introduces no new physical entities. The main free parameters are the slack values and the Hybrid slack tolerance, which are chosen by hand from case studies and design considerations, not fitted to produce the reported LER reductions.

free parameters (3)
  • Synchronization slack tau = 500 ns and 1000 ns
    Chosen as average and worst-case slacks from magic state cultivation case study (Section 3.4.1, Figure 4a). These values bound the evaluation and strongly influence the reported LER reductions.
  • Hybrid slack tolerance epsilon = 400 ns
    Set to 400 ns for all evaluations 'due to its more general application' (Section 4.2.1). The choice affects the number of extra rounds and hence the Hybrid LER improvement.
  • Maximum extra rounds bound = 5
    The paper sets an upper bound of 5 additional rounds for the Hybrid policy (Section 4.2.1), which limits the achievable synchronization configurations.
assumptions (3)
  • domain assumption Lattice surgery operations require all participating patches to start their syndrome generation cycle at the same time.
    Stated in Sections 2.2 and 4.1, citing standard lattice surgery references [31,38,54,56]. If this were not true, the entire synchronization problem would dissolve.
  • domain assumption Idling errors during synchronization are accurately modeled as uncorrelated single-qubit Pauli errors with exponential T1/T2 decay.
    Section 6 describes the Pauli twirl approximation model, which the authors note does not include crosstalk, spectator effects, or leakage. The quantitative LER results depend on this assumption.
  • domain assumption A homogeneous defect-free surface code system with equal cycle times does not require synchronization.
    Section 3.1 states synchronization is unnecessary in a perfect homogeneous system, which motivates focusing on heterogeneous/defective systems. This is a simplifying baseline assumption.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Synchronization for Fault-Tolerant Quantum Computers." pith.science (2026). https://pith.science/paper/UA7Q27QE

@misc{pith2026250610258,
  author       = {Pith},
  title        = {Pith review of: Synchronization for Fault-Tolerant Quantum Computers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UA7Q27QE}},
  note         = {Machine review of arXiv:2506.10258}
}
read the original abstract

Quantum Error Correction (QEC) codes store information reliably in logical qubits by encoding them in a larger number of less reliable qubits. The surface code, known for its high resilience to physical errors, is a leading candidate for fault-tolerant quantum computing (FTQC). Logical qubits encoded with the surface code can be in different phases of their syndrome generation cycle, thereby introducing desynchronization in the system. This can occur due to the production of non-Clifford states, dropouts due to fabrication defects, and the use of other QEC codes with the surface code to reduce resource requirements. Logical operations require the syndrome generation cycles of the logical qubits involved to be synchronized. This requires the leading qubit to pause or slow down its cycle, allowing more errors to accumulate before the next cycle, thereby increasing the risk of uncorrectable errors. To synchronize the syndrome generation cycles of logical qubits, we define three policies - Passive, Active, and Hybrid. The Passive policy is the baseline, and the simplest, wherein the leading logical qubits idle until they are synchronized with the remaining logical qubits. On the other hand, the Active policy aims to slow the leading logical qubits down gradually, by inserting short idle periods before multiple code cycles. This approach reduces the logical error rate (LER) by up to 2.4x compared to the Passive policy. The Hybrid policy further reduces the LER by up to 3.4x by reducing the synchronization slack and running a few additional rounds of error correction. Furthermore, the reduction in the logical error rate with the proposed synchronization policies enables a speedup in decoding latency of up to 2.2x with a circuit-level noise model.

Figures

Figures reproduced from arXiv: 2506.10258 by the authors.

Figure 1
Figure 1. (a) A heterogeneous FTQC system can optimize space-time volume by combining different QEC codes: surface codes [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (a) A patch in the lattice consists of a grid of physical [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. (a) Teleporting logical qubits between different codes will require synchronization since every code has a different [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (15 more)
Figure 5
Figure 5. Figure 5: (a) Patches 𝑃 and 𝑃 ′ are desynchronized by a slack of 𝜏. This corresponds to both patches being in different phases of their syndrome generation cycles (of duration 𝑇𝑐𝑦𝑐𝑙𝑒 = 𝑇𝑃,𝑇 ′ 𝑃 ); (b) 𝑃 and 𝑃 ′ can be synchronized by idling 𝑃 for a period equal to 𝜏 right before…
Figure 4
Figure 4. Figure 4: (a) Slack distribution for IBM and Google-like sys￾tems when magic state cultivation [35] is used. The square represents the mean; (b) Slack as a function of error correc￾tion rounds when qLDPC codes are used as memories with surface code patches for IBM and Google sys…
Figure 7
Figure 7. Figure 7: (a) Logical error rate is influenced by the syndrome [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 6
Figure 6. Figure 6: Circuits used to emulate the (a) Passive and (b) Active policies. The delay 𝑡𝑎 is repeated 𝑁 times to yield a net delay of𝑡𝑝 , resulting in both circuits having the same total runtime. Both circuits were run on 20 qubits on IBM Brisbane [40] for 20,000 shots. (c) Exper…
Figure 10
Figure 10. Figure 10: Different example configurations to show how running 𝑃 for 𝑚 extra rounds to achieve synchronization can be advantageous in some cases and impossible in others with the syndrome generation cycle times of 𝑃, 𝑃′ as𝑇𝑃,𝑇 ′ 𝑃 (ns) and an initial slack 𝜏 (ns). 4.2 Combining…
Figure 9
Figure 9. Figure 9: Extra Rounds: for patch 𝑃 ′ with a cycle time of 𝑇 ′ 𝑃 and 𝑃 with a cycle time of 𝑇𝑃 , synchronization before Lattice Surgery can be achieved by running 𝑃 (𝑃 ′ ) for 𝑚(𝑛) extra rounds; Hybrid policy: a small idling period is added after every round and 𝑃 is run for 𝑧 e…
Figure 12
Figure 12. Figure 12: Microarchitecture for enabling synchronization. [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]
Figure 13
Figure 13. Figure 13: Two surface code patches 𝑃, 𝑃 ′ are initialized, with some synchronization slack added to 𝑃. After 𝑑 + 1 rounds, they are synchronized and then merged, after which the merged patch is run for another 𝑑 +1 rounds. The observables 𝑋𝑃 , 𝑋𝑃 ′ , 𝑍𝑃𝑍𝑃 ′ , 𝑋𝑃𝑋𝑃 ′ of the merg…
Figure 14
Figure 14. Figure 14: Reduction in the logical error rate for one synchronization when using Active synchronization instead of Passive for different code distances with a system configuration similar to (a)-(b) IBM systems, and (c)-(d) Google systems for both 𝑋 and 𝑍 basis Lattice Surgery …
Figure 16
Figure 16. Figure 16: Relative increase in the final LER when the Passive policy is used instead of Active (𝑑 = 15). 3 5 7 9 11 13 15 Code Distance (d) 0.98 1.00 1.02 1.04 1.06 1.08 1.10 1.12 Reduction ZPZP ' τ: 500 ns τ: 1000 ns 3 5 7 9 11 13 15 Code Distance (d) 0.90 0.95 1.00 1.05 1.10 …
Figure 17
Figure 17. Figure 17: Reduction in the LER with the Active-intra policy for 𝑋 and 𝑍 basis Lattice Surgery. The Active-intra policy leads to an increase in the LER for some cases. before Lattice Surgery. As shown in [PITH_FULL_IMAGE:figures/full_fig_p010_17.png]
Figure 18
Figure 18. Figure 18: (a) Reduction in the LER compared to the [PITH_FULL_IMAGE:figures/full_fig_p011_18.png]
Figure 20
Figure 20. Figure 20: Time required to synchronize all patches as a func￾tion of the number of patches in the system. 7.3.2 Compilation time for synchronization. How long will it take to determine the slack and the number of additional rounds re￾quired by the Hybrid policy? We run a numeri…
Figure 21
Figure 21. Figure 21: Reduction in the LER compared to the Passive policy with the use of the Active and Hybrid policies on a neutral atom system. Running additional rounds, even with the Hybrid policy, is detrimental to the LER. 3 5 7 Code Distance (d) 0.0 0.5 1.0 1.5 2.0 Speedup 1.030 2.…
Figure 22
Figure 22. Figure 22: Speed-up gained by Active synchronization over Passive per Lattice Surgery operation and the corresponding LUT hit rates. 7.5 Performance Improvements Active synchronization outperforms Passive synchronization in terms of the logical error rate, but how does this impa…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

101 extracted references · 13 canonical work pages

  1. [1]

    Andersen, Markus Ansmann, Frank Arute, Kunal Arya, Abraham Asfaw, Nikita Astrakhantsev, Juan Atalaya, Ryan Babbush, Dave Bacon, Brian Ballard, Joseph C

    Rajeev Acharya, Laleh Aghababaie-Beni, Igor Aleiner, Trond I. Andersen, Markus Ansmann, Frank Arute, Kunal Arya, Abraham Asfaw, Nikita Astrakhantsev, Juan Atalaya, Ryan Babbush, Dave Bacon, Brian Ballard, Joseph C. Bardin, Johannes Bausch, Andreas Bengtsson, Alexander Bilmes, Sam Blackwell, Sergio Boixo, Gina Bortoli, Alexandre Bourassa, Jenna Bovaird, Le...

    arXiv 2024

  2. [2]

    Andersen, Markus Ansmann, Frank Arute, Kunal Arya, Abraham Asfaw, Juan Atalaya, Ryan Babbush, Dave Bacon, Joseph C

    Rajeev Acharya, Igor Aleiner, Richard Allen, Trond I. Andersen, Markus Ansmann, Frank Arute, Kunal Arya, Abraham Asfaw, Juan Atalaya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Joao Basso, Andreas Bengtsson, Sergio Boixo, Gina Bortoli, Alexandre Bourassa, Jenna Bovaird, Leon Brill, Michael Broughton, Bob B. Buckley, David A. Buell, Tim Burger, Brian Burk...

    2023

  3. [3]

    Narges Alavisamani, Suhas Vittal, Ramin Ayanzadeh, Poulami Das, and Moinud- din Qureshi. 2024. Promatch: Extending the Reach of Real-Time Quantum Error Correction with Adaptive Predecoding. In Proceedings of the 29th ACM Interna- tional Conference on Architectural Support for Programming Languages and Operat- ing Systems, Volume 3 (ASPLOS ’24). ACM, 818–8...

    arXiv 2024

  4. [4]

    Dutoi, Peter J

    Alan Aspuru-Guzik, Anthony D. Dutoi, Peter J. Love, and Martin Head-Gordon

  5. [5]

    Auger, Hussain Anwar, Mercedes Gimeno-Segovia, Thomas M

    James M. Auger, Hussain Anwar, Mercedes Gimeno-Segovia, Thomas M. Stace, and Dan E. Browne. 2017. Fault-tolerance thresholds for the surface code with fabrication errors. Physical Review A 96, 4 (Oct. 2017). doi: 10.1103/physreva.96. 042316

    doi:10.1103/physreva.96 2017

  6. [6]

    Johannes Bausch, Andrew W Senior, Francisco J H Heras, Thomas Edlich, Alex Davies, Michael Newman, Cody Jones, Kevin Satzinger, Murphy Yuezhen Niu, Sam Blackwell, George Holland, Dvir Kafri, Juan Atalaya, Craig Gidney, Demis Hassabis, Sergio Boixo, Hartmut Neven, and Pushmeet Kohli. 2023. Learning to Decode the Surface Code with a Recurrent, Transformer-B...

    arXiv doi:10.48550/arxiv.2310.05900 2023

  7. [7]

    Michael E Beverland, Prakash Murali, Matthias Troyer, Krysta M Svore, Torsten Hoeffler, Vadym Kliuchnikov, Guang Hao Low, Mathias Soeken, Aarthi Sundaram, and Alexander Vaschillo. 2022. Assessing requirements to scale to practical quantum advantage. arXiv preprint arXiv:2211.07629 (2022). https://doi.org/10. 48550/arXiv.2211.07629

    arXiv doi:10.48550/arxiv.2211.07629 2022

  8. [8]

    Evered, Alexandra A

    Dolev Bluvstein, Simon J. Evered, Alexandra A. Geim, Sophie H. Li, Hengyun Zhou, Tom Manovitz, Sepehr Ebadi, Madelyn Cain, Marcin Kalinowski, Dominik Hangleiter, J. Pablo Bonilla Ataides, Nishad Maskara, Iris Cong, Xun Gao, Pe- dro Sales Rodriguez, Thomas Karolyshyn, Giulia Semeghini, Michael J. Gullans, Markus Greiner, Vladan Vuletić, and Mikhail D. Luki...

    doi:10.1038/s41586-023-06927-3 2023

Show all 101 references
  1. [9]

    Cross, Jay M

    Sergey Bravyi, Andrew W. Cross, Jay M. Gambetta, Dmitri Maslov, Patrick Rall, and Theodore J. Yoder. 2024. High-threshold and low-overhead fault-tolerant quantum memory. Nature 627, 8005 (March 2024), 778–782. doi: 10.1038/s41586- 024-07107-7

    2024 doi

  2. [10]

    Sergey B Bravyi and A Yu Kitaev. 1998. Quantum codes on a lattice with boundary. arXiv preprint quant-ph/9811052 (1998). https://doi.org/10.48550/arXiv.quant- ph/9811052

    1998 doi arXiv

  3. [11]

    Friederike Butt, Sascha Heußen, Manuel Rispler, and Markus Müller. 2024. Fault- Tolerant Code-Switching Protocols for Near-Term Quantum Processors. PRX Quantum 5, 2 (May 2024). doi: 10.1103/prxquantum.5.020345

    2024 doi

  4. [12]

    Cory, Yasunobu Nakamura, Jaw-Shen Tsai, and William D

    Jonas Bylander, Simon Gustavsson, Fei Yan, Fumiki Yoshihara, Khalil Harrabi, George Fitch, David G. Cory, Yasunobu Nakamura, Jaw-Shen Tsai, and William D. Oliver. 2011. Noise spectroscopy through dynamical decoupling with a su- perconducting flux qubit. Nature Physics 7, 7 (Ma...

    2011 doi

  5. [13]

    Ilkwon Byun, Junpyo Kim, Dongmoon Min, Ikki Nagaoka, Kosuke Fukumitsu, Iori Ishikawa, Teruo Tanimoto, Masamitsu Tanaka, Koji Inoue, and Jangwoo Kim

  6. [14]

    Laura Caune, Joan Camps, Brendan Reid, and Earl Campbell. 2023. Belief prop- agation as a partial decoder. arXiv:2306.17142 [quant-ph] https://doi.org/10. 48550/arXiv.2208.01178

    2023 arXiv

  7. [15]

    Campbell

    Christopher Chamberland and Earl T. Campbell. 2022. Circuit-level protocol and analysis for twist-based lattice surgery. Physical Review Research 4, 2 (May 2022). doi: 10.1103/physrevresearch.4.023090

    2022 doi

  8. [16]

    Christopher Chamberland, Luis Goncalves, Prasahnt Sivarajah, Eric Peterson, and Sebastian Grimberg. 2022. Techniques for combining fast local decoders with global decoders under circuit-level noise. arXiv:2208.01178 [quant-ph] https://arxiv.org/abs/2208.01178

    2022 arXiv

  9. [17]

    Christopher Chamberland and Kyungjoo Noh. 2020. Very low overhead fault- tolerant magic state preparation using redundant ancilla encoding and flag qubits. npj Quantum Information 6, 1 (Oct. 2020). doi: 10.1038/s41534-020-00319-5 13 ISCA ’25, June 21–25, 2025, Tokyo, Japan Sat...

    2020 doi

  10. [18]

    Hyeongrak Choi, Frederic T Chong, Dirk Englund, and Yongshan Ding. 2023. Fault Tolerant Non-Clifford State Preparation for Arbitrary Rotations. arXiv preprint arXiv:2303.17380 (2023). https://arxiv.org/abs/2303.17380

    2023 arXiv

  11. [19]

    Bishop, Steven Heidel, Colm A

    Andrew Cross, Ali Javadi-Abhari, Thomas Alexander, Niel De Beaudrap, Lev S. Bishop, Steven Heidel, Colm A. Ryan, Prasahnt Sivarajah, John Smolin, Jay M. Gambetta, and Blake R. Johnson. 2022. OpenQASM 3: A Broader and Deeper Quantum Assembly Language. ACM Transactions on Quantu...

    2022 doi

  12. [20]

    Lucas Daguerre and Isaac H. Kim. 2024. Code switching revisited: low-overhead magic state preparation using color codes. doi: 10.48550/ARXIV.2410.07327

    2024 doi arXiv

  13. [21]

    Poulami Das, Aditya Locharla, and Cody Jones. 2022. LILLIPUT: A Lightweight Low-Latency Lookup-Table Decoder for near-Term Quantum Error Correction. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems...

    2022

  14. [24]

    Debroy, Matt McEwen, Craig Gidney, Noah Shutty, and Adam Zalcman

    Dripto M. Debroy, Matt McEwen, Craig Gidney, Noah Shutty, and Adam Zalcman

  15. [25]

    Nicolas Delfosse. 2020. Hierarchical decoding to reduce hardware requirements for quantum computing. arXiv:2001.11427 [quant-ph] https://doi.org/10.48550/ arXiv.2001.11427

    2020 doi arXiv

  16. [26]

    Yongshan Ding, Adam Holmes, Ali Javadi-Abhari, Diana Franklin, Margaret Martonosi, and Frederic Chong. 2018. Magic-State Functional Units: Mapping and Scheduling Multi-Level Distillation Circuits for Fault-Tolerant Quantum Architectures. In 2018 51st Annual IEEE/ACM Internatio...

    2018

  17. [27]

    Baker, David I

    Casey Duckering, Jonathan M. Baker, David I. Schuster, and Frederic T. Chong

  18. [28]

    Chen, Ben Scharmann, Eyob A

    Mark Field, Angela Q. Chen, Ben Scharmann, Eyob A. Sete, Feyza Oruc, Kim Vu, Valentin Kosenko, Joshua Y. Mutus, Stefano Poletto, and Andrew Bestwick. 2024. Modular superconducting-qubit architecture with a multichip tunable coupler. Physical Review Applied 21, 5 (May 2024). do...

    2024 doi

  19. [29]

    Flammia and Joel J

    Steven T. Flammia and Joel J. Wallman. 2020. Efficient Estimation of Pauli Channels. ACM Transactions on Quantum Computing 1, 1 (Dec. 2020), 1–32. doi: 10.1145/3408039

    2020 doi

  20. [30]

    Austin G. Fowler. 2011. Two-dimensional color-code quantum computation. Physical Review A 83, 4 (April 2011). doi: 10.1103/physreva.83.042310

    2011 doi

  21. [31]

    Fowler and Craig Gidney

    Austin G. Fowler and Craig Gidney. 2019. Low overhead quantum computation using lattice surgery. arXiv:1808.06709 [quant-ph] https://doi.org/10.48550/ arXiv.1808.06709

    2019 doi arXiv

  22. [32]

    Fowler, Matteo Mariantoni, John M

    Austin G. Fowler, Matteo Mariantoni, John M. Martinis, and Andrew N. Cleland

  23. [33]

    Fowler, and Michael R

    Joydip Ghosh, Austin G. Fowler, and Michael R. Geller. 2012. Surface code with decoherence: An analysis of three superconducting architectures. Phys. Rev. A 86 (Dec 2012), 062318. Issue 6. doi: 10.1103/PhysRevA.86.062318

    2012 doi

  24. [34]

    Craig Gidney. 2021. Stim: a fast stabilizer circuit simulator. Quantum 5 (July 2021), 497. doi: 10.22331/q-2021-07-06-497

    2021 doi

  25. [35]

    Craig Gidney, Noah Shutty, and Cody Jones. 2024. Magic state cultivation: growing T states as cheap as CNOT gates. doi: 10.48550/ARXIV.2409.17595

    2024 doi arXiv

  26. [36]

    Google Quantum AI. 2024. Google Quantum Roadmap . https://quantumai. google/qecmilestone Accessed: October 18, 2024

    2024

  27. [37]

    Oscar Higgott and Craig Gidney. 2023. Sparse Blossom: correcting a million errors per core second with minimum-weight matching. doi: 10.48550/ARXIV. 2303.15933

    2023 doi arXiv

  28. [38]

    Clare Horsman, Austin G Fowler, Simon Devitt, and Rodney Van Meter. 2012. Surface code quantum computing by lattice surgery. New Journal of Physics 14, 12 (2012), 123011. https://dx.doi.org/10.1088/1367-2630/14/12/123011

    2012 doi

  29. [39]

    Fei Hua, Yanhao Chen, Yuwei Jin, Chi Zhang, Ari Hayes, Youtao Zhang, and Eddy Z. Zhang. 2021. AutoBraid: A Framework for Enabling Efficient Sur- face Code Communication in Quantum Computing. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO ...

    2021

  30. [40]

    IBM Brisbane. 2024. IBM Brisbane 127-qubit Quantum Computer . https: //quantum.ibm.com/services/resources?system=ibm_brisbane Accessed: October 18, 2024

    2024

  31. [41]

    IBM Brisbane. 2024. IBM Fez 156-qubit Quantum Computer . https://quantum. ibm.com/services/resources?system=ibm_fez Accessed: October 18, 2024

    2024

  32. [42]

    IBM Quantum. 2024. IBM Quantum Roadmap. https://www.ibm.com/quantum/ roadmap Accessed: October 18, 2024

    2024

  33. [43]

    IBM Quantum. 2024. IBM Unveils 400 Qubit Plus Quantum Processor . https:// research.ibm.com/blog/next-wave-quantum-centric-supercomputing Accessed: October 18, 2023

    2024

  34. [44]

    IBM Sherbrooke. 2024. IBM Sherbrooke 127-qubit Quantum Computer . https://quantum.ibm.com/services/resources?system=ibm_sherbrooke Accessed: October 18, 2024

    2024

  35. [45]

    Tomohiro Itogawa, Yugo Takada, Yutaka Hirano, and Keisuke Fujii. 2024. Even more efficient magic state distillation by zero-level distillation. doi: 10.48550/ ARXIV.2403.03991

    2024 doi arXiv

  36. [46]

    Brown, Margaret Martonosi, and Frederic T

    Ali Javadi-Abhari, Pranav Gokhale, Adam Holmes, Diana Franklin, Kenneth R. Brown, Margaret Martonosi, and Frederic T. Chong. 2017. Optimized surface code communication in superconducting quantum computers. In Proceedings of the 50th Annual IEEE/ACM International Symposium on M...

    2017

  37. [47]

    Khodjasteh and D

    K. Khodjasteh and D. A. Lidar. 2005. Fault-Tolerant Quantum Dynamical Decou- pling. Phys. Rev. Lett. 95 (Oct 2005), 180501. Issue 18. doi: 10.1103/PhysRevLett. 95.180501

    2005 doi

  38. [48]

    Kaveh Khodjasteh and Daniel A. Lidar. 2007. Performance of deterministic dynamical decoupling schemes: Concatenated and periodic pulse sequences. Phys. Rev. A 75 (Jun 2007), 062310. Issue 6. doi: 10.1103/PhysRevA.75.062310

    2007 doi

  39. [50]

    Emanuel Knill, Raymond Laflamme, and Wojciech H. Zurek. 1998. Resilient Quantum Computation. Science 279, 5349 (Jan. 1998), 342–345. doi: 10.1126/ science.279.5349.342

    1998

  40. [51]

    Norris, Christian Kraglund Andersen, Markus Müller, Alexandre Blais, Christopher Eichler, and Andreas Wallraff

    Sebastian Krinner, Nathan Lacroix, Ants Remm, Agustin Di Paolo, Elie Genois, Catherine Leroux, Christoph Hellings, Stefania Lazar, Francois Swiadek, Johannes Herrmann, Graham J. Norris, Christian Kraglund Andersen, Markus Müller, Alexandre Blais, Christopher Eichler, and Andre...

    2022 doi

  41. [52]

    Tyler Leblond, Christopher Dean, George Watkins, and Ryan Bennink. 2024. Realistic Cost to Execute Practical Quantum Circuits using Direct Clifford+T Lattice Surgery Compilation. ACM Transactions on Quantum Computing 5, 4 (Oct. 2024), 1–28. doi: 10.1145/3689826

    2024 doi

  42. [54]

    Daniel Litinski. 2019. A Game of Surface Codes: Large-Scale Quantum Computing with Lattice Surgery. Quantum 3 (March 2019), 128. doi: 10.22331/q-2019-03- 05-128

    2019 doi

  43. [55]

    Daniel Litinski. 2019. Magic State Distillation: Not as Costly as You Think. Quantum 3 (Dec. 2019), 205. doi: 10.22331/q-2019-12-02-205

    2019 doi

  44. [56]

    Daniel Litinski and Felix von Oppen. 2018. Lattice Surgery with a Twist: Sim- plifying Clifford Gates of Surface Codes. Quantum 2 (May 2018), 62. doi: 10.22331/q-2018-05-04-62

    2018 doi

  45. [57]

    Seth Lloyd. 1996. Universal Quantum Simulators. Science 273, 5278 (Aug. 1996), 1073–1078. doi: 10.1126/science.273.5278.1073

    1996

  46. [58]

    Matt McEwen, Dave Bacon, and Craig Gidney. 2023. Relaxing Hardware Require- ments for Surface Code Circuits using Time-dynamics. Quantum 7 (Nov. 2023),

    2023

  47. [59]

    Yusuf, Gaurav Agarwal, Michael Hatridge, and Alex K

    Evan McKinney, Girgis Falstin, Israa G. Yusuf, Gaurav Agarwal, Michael Hatridge, and Alex K. Jones. 2024. Towards Error Budgeting for Superconducting Modular Quantum Architecture Designs. doi: 10.48550/ARXIV.2409.18262

    2024 doi

  48. [60]

    Evan McKinney, Mingkang Xia, Chao Zhou, Pinlei Lu, Michael Hatridge, and Alex K. Jones. 2023. Co-Designed Architectures for Modular Superconducting Quantum Computers. In 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 759–772. doi: 10....

    2023

  49. [61]

    Áron Márton and János K. Asbóth. 2024. Optimal number of stabilizer measure- ment rounds in an idling surface code patch. doi: 10.48550/ARXIV.2408.07529

    2024 doi arXiv

  50. [62]

    Ramon W. J. Overwater, Masoud Babaie, and Fabio Sebastiano. 2022. Neural- Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs. IEEE Transactions on Quantum Engineering 3 (2022), 1–19. doi: 10.1109/TQ...

    2022

  51. [63]

    Hendrik Poulsen Nautrup, Nicolai Friis, and Hans J. Briegel. 2017. Fault-tolerant interface between quantum memories and quantum processors. Nature Commu- nications 8, 1 (Nov. 2017). doi: 10.1038/s41467-017-01418-2

    2017 doi

  52. [64]

    Nils Quetschlich, Lukas Burgholzer, and Robert Wille. 2023. MQT Bench: Bench- marking Software and Design Automation Tools for Quantum Computing.Quan- tum (2023). MQT Bench is available at https://www.cda.cit.tum.de/mqtbench/

    2023

  53. [65]

    Baker, Arash Fayyazi, Sophia Fuhui Lin, Ali Javadi-Abhari, Massoud Pedram, and Frederic T

    Gokul Subramanian Ravi, Jonathan M. Baker, Arash Fayyazi, Sophia Fuhui Lin, Ali Javadi-Abhari, Massoud Pedram, and Frederic T. Chong. 2023. Better Than Worst- Case Decoding for Quantum Error Correction. In Proceedings of the 28th ACM International Conference on Architectural S...

    2023 doi

  54. [66]

    Almudever

    Santiago Rodrigo, Sergi Abadal, Eduard Alarcon, and Carmen G. Almudever

  55. [67]

    Hogle, Ravi K

    Kenneth Rudinger, Craig W. Hogle, Ravi K. Naik, Akel Hashim, Daniel Lobser, David I. Santiago, Matthew D. Grace, Erik Nielsen, Timothy Proctor, Stefan Seritan, Susan M. Clark, Robin Blume-Kohout, Irfan Siddiqi, and Kevin C. Young

  56. [68]

    Ryan-Anderson, J

    C. Ryan-Anderson, J. G. Bohnet, K. Lee, D. Gresh, A. Hankin, J. P. Gaebler, D. Francois, A. Chernoguzov, D. Lucchetti, N. C. Brown, T. M. Gatterman, S. K. Halit, K. Gilmore, J. A. Gerber, B. Neyenhuis, D. Hayes, and R. P. Stutz. 2021. Realization of Real-Time Fault-Tolerant Qu...

    2021 doi

  57. [69]

    Satvik Maurya and Swamit Tannu. 2024. Lattice-Sim. https://zenodo.org/ records/15092177 Available at https://zenodo.org/records/15092177

    2024

  58. [70]

    Sete, Nicolas Didier, Angela Q

    Eyob A. Sete, Nicolas Didier, Angela Q. Chen, Shobhan Kulshreshtha, Riccardo Manenti, and Stefano Poletto. 2021. Parametric-Resonance Entangling Gates with a Tunable Coupler. Physical Review Applied 16, 2 (Aug. 2021). doi: 10.1103/ physrevapplied.16.024050

    2021

  59. [71]

    Peter W. Shor. 1995. Scheme for reducing decoherence in quantum computer memory. Phys. Rev. A52 (Oct 1995), R2493–R2496. Issue 4. doi: 10.1103/PhysRevA. 52.R2493

    1995 doi

  60. [72]

    Peter W. Shor. 1997. Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer. SIAM J. Comput. 26, 5 (oct 1997), 1484–1509. doi: 10.1137/S0097539795293172

    1997 doi

  61. [73]

    In 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS)

    Will Quantum Computers Scale Without Inter-Chip Comms? A Structured Design Exploration to the Monolithic vs Distributed Architectures Quest. In 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS) . IEEE, 1–6. doi: 10.1109/dcis51330.2020.9268630

    2020

  62. [74]

    Adam Siegel, Armands Strikis, Thomas Flatters, and Simon Benjamin. 2023. Adaptive surface code for quantum error correction in the presence of temporary or permanent defects. Quantum 7 (July 2023), 1065. doi: 10.22331/q-2023-07- 25-1065

    2023 doi

  63. [75]

    Matti Silveri and Tuure Orell. 2022. Many-qubit protection-operation dilemma from the perspective of many-body localization. Nature Communications 13, 1 (Oct. 2022). doi: 10.1038/s41467-022-33657-3

    2022 doi

  64. [76]

    Smith, Gokul Subramanian Ravi, Jonathan M

    Kaitlin N. Smith, Gokul Subramanian Ravi, Jonathan M. Baker, and Frederic T. Chong. 2022. Scaling Superconducting Quantum Computers with Chiplet Archi- tectures. In 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO). 1092–1109. doi: 10.1109/MICRO56248.2022.00078

    2022

  65. [77]

    Smith, Benjamin J

    Samuel C. Smith, Benjamin J. Brown, and Stephen D. Bartlett. 2023. Local Predecoder to Reduce the Bandwidth and Latency of Quantum Error Correction. Phys. Rev. Appl. 19 (Mar 2023), 034050. Issue 3. doi: 10.1103/PhysRevApplied.19. 034050

    2023 doi

  66. [78]

    Samuel Stein, Fei Hua, Chenxu Liu, Charles Guinn, James Ang, Eddy Zhang, Srivatsan Chakram, Yufei Ding, and Ang Li. 2023. Multi-mode Cavity Centric Architectures for Quantum Simulation. arXiv preprint arXiv:2309.15994 (2023). https://arxiv.org/abs/2309.15994

    2023 arXiv

  67. [79]

    Houck, Isaac L

    Samuel Stein, Sara Sussman, Teague Tomesh, Charles Guinn, Esin Tureci, Sophia Fuhui Lin, Wei Tang, James Ang, Srivatsan Chakram, Ang Li, Margaret Martonosi, Fred Chong, Andrew A. Houck, Isaac L. Chuang, and Michael Demarco

  68. [80]

    Cross, Theodore J

    Samuel Stein, Shifan Xu, Andrew W. Cross, Theodore J. Yoder, Ali Javadi-Abhari, Chenxu Liu, Kun Liu, Zeyuan Zhou, Charles Guinn, Yufei Ding, Yongshan Ding, and Ang Li. 2024. Architectures for Heterogeneous Quantum Error Correction Codes. doi: 10.48550/ARXIV.2411.03202

    2024 doi arXiv

  69. [81]

    Noah Shutty and Christopher Chamberland. 2022. Decoding merged color-surface codes and finding fault-tolerant Clifford circuits using solvers for satisfiability modulo theories. Physical Review Applied 18, 1 (2022), 014072

    2022

  70. [82]

    Yoder, Youngseok Kim, Muyuan Li, Edward H

    Neereja Sundaresan, Theodore J. Yoder, Youngseok Kim, Muyuan Li, Edward H. Chen, Grace Harper, Ted Thorbeck, Andrew W. Cross, Antonio D. Córcoles, and Maika Takita. 2023. Demonstrating multi-round subsystem quantum error correc- tion using matching and maximum likelihood decod...

    2023 doi

  71. [83]

    Yu Tomita and Krysta M. Svore. 2014. Low-distance surface codes under realistic quantum noise. Phys. Rev. A 90 (Dec 2014), 062320. Issue 6. doi: 10.1103/ PhysRevA.90.062320

    2014

  72. [84]

    Yosuke Ueno, Masaaki Kondo, Masamitsu Tanaka, Yasunari Suzuki, and Yutaka Tabuchi. 2021. QECOOL: On-Line Quantum Error Correction with a Supercon- ducting Decoder for Surface Code. In 2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE. doi: 10.1109/dac18074.2021.9586326

    2021

  73. [85]

    Yosuke Ueno, Masaaki Kondo, Masamitsu Tanaka, Yasunari Suzuki, and Yutaka Tabuchi. 2022. NEO-QEC: Neural Network Enhanced Online Superconducting Decoder for Surface Codes. arXiv:2208.05758 [quant-ph] https://doi.org/10. 48550/arXiv.2208.05758

    2022 doi arXiv

  74. [86]

    Yosuke Ueno, Masaaki Kondo, Masamitsu Tanaka, Yasunari Suzuki, and Yutaka Tabuchi. 2022. QULATIS: A Quantum Error Correction Methodology toward Lat- tice Surgery. In2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). 274–287. doi: 10.1109/HPCA53...

    2022

  75. [87]

    Varbanov, Marc Serra-Peralta, David Byfield, and Barbara M

    Boris M. Varbanov, Marc Serra-Peralta, David Byfield, and Barbara M. Ter- hal. 2023. Neural network decoder for near-term surface-code experiments. arXiv:2307.03280 [quant-ph] https://doi.org/10.48550/arXiv.2307.03280

    2023 doi arXiv

  76. [88]

    Suhas Vittal, Poulami Das, and Moinuddin Qureshi. 2023. Astrea: Accurate Quantum Error-Decoding via Practical Minimum-Weight Perfect-Matching. In Proceedings of the 50th Annual International Symposium on Computer Architecture (Orlando, FL, USA) (ISCA ’23). Association for Comp...

    2023

  77. [89]

    Suhas Vittal, Poulami Das, and Moinuddin Qureshi. 2023. ERASER: Towards Adaptive Leakage Suppression for Fault-Tolerant Quantum Computing. In56th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO ’23) . ACM. doi: 10.1145/3613424.3614251

    2023

  78. [90]

    Benjamin, and Benjamin J

    Armands Strikis, Simon C. Benjamin, and Benjamin J. Brown. 2023. Quantum Computing is Scalable on a Planar Array of Qubits with Fabrication Defects. Physical Review Applied 19, 6 (June 2023). doi: 10.1103/physrevapplied.19.064081

    2023 doi

  79. [91]

    Anbang Wu, Gushu Li, Hezi Zhang, Gian Giacomo Guerreschi, Yufei Ding, and Yuan Xie. 2022. A Synthesis Framework for Stitching Surface Code with Superconducting Quantum Devices. In Proceedings of the 49th Annual Inter- national Symposium on Computer Architecture (New York, New ...

    2022

  80. [92]

    Mitchison, and Marcus Huber

    Jake Xuereb, Florian Meier, Paul Erker, Mark T. Mitchison, and Marcus Huber

  81. [93]

    Humble, Ang Li, Yunong Shi, and Yufei Ding

    Keyi Yin, Xiang Fang, Travis S. Humble, Ang Li, Yunong Shi, and Yufei Ding. 2024. Surf-Deformer: Mitigating dynamic defects on surface code via adaptive deforma- tion. In MICRO 2024. https://www.amazon.science/publications/surf-deformer- mitigating-dynamic-defects-on-surface-c...

    2024

  82. [94]

    Hezi Zhang, Keyi Yin, Anbang Wu, Hassan Shapourian, Alireza Shabani, and Yufei Ding. 2024. MECH: Multi-Entry Communication Highway for Superconducting Quantum Chiplets. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages a...

    2024

  83. [95]

    Mengyu Zhang, Xiangyu Ren, Guanglei Xi, Zhenxing Zhang, Qiaonian Yu, Fum- ing Liu, Hualiang Zhang, Shengyu Zhang, and Yi-Cong Zheng. 2023. A Scalable, Fast and Programmable Neural Decoder for Fault-Tolerant Quantum Computa- tion Using Surface Codes. doi: 10.48550/ARXIV.2305.15...

    2023 doi arXiv

  84. [99]

    George Watkins, Hoang Minh Nguyen, Keelan Watkins, Steven Pearce, Hoi- Kwan Lau, and Alexandru Paler. 2024. A High Performance Compiler for Very Large Scale Surface Code Computations. Quantum 8 (May 2024), 1354. doi: 10.22331/q-2024-05-22-1354

    2024 doi

  85. [102]

    doi: 10.48550/ ARXIV.2301.10767

    The Impact of Imperfect Timekeeping on Quantum Control. doi: 10.48550/ ARXIV.2301.10767

    doi arXiv

  86. [1172]

    doi: 10.22331/q-2023-11-07-1172

    2023 doi

  87. [2005]

    Science 309, 5741 (Sept

    Simulated Quantum Computation of Molecular Energies. Science 309, 5741 (Sept. 2005), 1704–1707. doi: 10.1126/science.1113479

    2005 doi

  88. [2012]

    Surface codes: Towards practical large-scale quantum computation. Phys. Rev. A 86 (Sep 2012), 032324. Issue 3. doi: 10.1103/PhysRevA.86.032324

    2012 doi

  89. [2020]

    In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

    Virtualized Logical Qubits: A 2.5D Architecture for Error-Corrected Quan- tum Computing. In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 173–185. doi: 10.1109/micro50266.2020.00026

    2020

  90. [2021]

    PRX Quantum 2, 4 (Nov

    Experimental Characterization of Crosstalk Errors with Simultaneous Gate Set Tomography. PRX Quantum 2, 4 (Nov. 2021). doi: 10.1103/prxquantum.2. 040338

    2021 doi

  91. [2022]

    In Proceedings of the 49th Annual International Symposium on Computer Architecture (New York, New York) (ISCA ’22)

    XQsim: Modeling Cross-Technology Control Processors for 10+K Qubit Quantum Computers. In Proceedings of the 49th Annual International Symposium on Computer Architecture (New York, New York) (ISCA ’22) . Association for Computing Machinery, New York, NY, USA, 366–382. doi: 10.1...

    doi

  92. [2023]

    In Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture (Toronto, ON, Canada)(MICRO ’23)

    HetArch: Heterogeneous Microarchitectures for Superconducting Quantum Systems. In Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture (Toronto, ON, Canada)(MICRO ’23). Association for Computing Machinery, New York, NY, USA, 539–554. doi: 10.114...

  93. [2024]

    doi: 10.48550/ARXIV.2410.14891

    LUCI in the Surface Code with Dropouts. doi: 10.48550/ARXIV.2410.14891

    doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.