Context-Verified, Error-Budget-Aware Decomposition Selection for Toffoli Networks

• URL: http://arxiv.org/abs/2606.31791v1
• Date: Tue, 30 Jun 2026 15:11:28 GMT
• Title: Context-Verified, Error-Budget-Aware Decomposition Selection for Toffoli Networks
• Abstract summary: We present the first compiler pass that selects a per-Toffoli decomposition to minimize a two-qubit-infidelity error budget.<n>Our verifier flags 66 library rewrites of a deployed open as non-equivalent without a context check, and count-greedy substitution silently corrupts 6 of 12 benchmark circuits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Two-qubit-gate error dominates the failure budget of near-term quantum circuits, so the decomposition chosen for each Toffoli (CCX) gate should minimize hardware two-qubit infidelity, not gate count. The cheapest decompositions - relative-phase and approximate Toffolis - are only correct in context: their residual phase or bounded error must be cancelled or absorbed downstream. We present the first compiler pass that selects a per-Toffoli decomposition to minimize a two-qubit-infidelity error budget. It admits each context-dependent decomposition only when an exact, instance-specific equivalence check certifies its validity in that circuit context, coupling an error-budget objective with per-instance verification and closing the gap between context-aware-but-unverified and verified-but-context-free optimizers. The central result is a safety one: pattern-matched relative-phase substitution is silently incorrect. Our verifier flags 66 library rewrites of a deployed open optimizer as non-equivalent without a context check, and count-greedy substitution silently corrupts 6 of 12 benchmark circuits; the verification gate certifies 0 errors while still applying every valid decomposition. The two-qubit-gate reduction is real but workload-dependent: up to 39.5% fewer two-qubit gates and 36.7% lower infidelity over exact-only on a compute/uncompute-heavy suite (approx. 39%/35% versus Qiskit opt-3 and tket), and 15.6% aggregate on a larger 12-24-qubit suite, with decision-diagram checking certifying every substitution past the exhaustive-verification limit. At current superconducting and trapped-ion error rates, the certified substitutions lower estimated circuit infidelity by 36-43%, and on a quantum state-resetting circuit, the pass removes 48.8% of the native two-qubit gates, every substitution verified.

Related papers

• Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures [4.287745257410248]
  We present a runtime-assurance benchmark for cost-aware commit gates.<n>Its 48 task templates yield 2,880 scenarios across six fault regimes.<n>Results motivate explicit evidence lineage, cost-aware selection, and commit-time enforcement.
  arXiv  Detail & Related papers  (2026-09-10T01:42:40Z)
• Post-Hoc Trajectory-Risk Certification for Modular LLM-Based Security Agents [1.4915002678801434]
  We show that Bonferroni allocation is distribution-free but conservative under correlated errors.<n> coarse-to-fine label selection can create near-perfect measured correlation without learned dependence.<n>A modular certificate using per-stage certificates and a pairwise overlap bound yields a positive average gain of 0.6%.
  arXiv  Detail & Related papers  (2026-08-04T23:48:36Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• Adaptive Consensus in LLM Ensembles via Sequential Evidence Accumulation: Automatic Budget Identification and Calibrated Commit Signals [0.0]
  DASE produces a commit-type routing partition that generalises across benchmarks.<n> Adaptive stopping, not injection bandwidth, drives accuracy.<n>Injection-based methods exhibit an inverted-U accuracy-vs-inference trajectory.
  arXiv  Detail & Related papers  (2026-05-05T19:24:10Z)
• BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models [56.504879072674015]
  We propose Bit-Plane Decomposition Quantization (BPDQ), which constructs a variable quantization grid via bit-planes and scalar coefficients.<n>BPDQ enables serving Qwen2.5-72B on a single GTX 3090 with 83.85% GSM8K accuracy (vs. 90.83% at 16-bit)
  arXiv  Detail & Related papers  (2026-02-04T02:54:37Z)
• A biased-erasure cavity qubit with hardware-efficient quantum error detection [19.031335768368475]
  Erasure qubits are beneficial for quantum error correction due to their relaxed threshold requirements.<n>We realize a hardware-efficient biased-erasure qubit encoded in the vacuum and two-photon Fock states of a single microwave cavity.
  arXiv  Detail & Related papers  (2026-01-29T12:19:59Z)
• A fidelity-driven approach to quantum circuit partitioning via weighted hypergraphs for noise-resilient computation [0.0]
  This paper introduces Fidelipart, a novel framework that transforms quantum circuits into a fidelity-aware hypergraph.<n>We evaluate Fidelipart against BQSKit's QuickPartitioner on 6-qubit/22-gate, 10-qubit/55-gate, and 24-qubit/88-gate benchmarks.<n>Results demonstrate Fidelipart's superior performance. It achieved SWAP gate reductions ranging from 77.3% to 100% and up to a 52.2% decrease in cut qubits.
  arXiv  Detail & Related papers  (2025-06-07T17:13:51Z)
• Demonstrating a long-coherence dual-rail erasure qubit using tunable transmons [59.63080344946083]
  We show that a "dual-rail qubit" consisting of a pair of resonantly coupled transmons can form a highly coherent erasure qubit. We demonstrate mid-circuit detection of erasure errors while introducing $ 0.1%$ dephasing error per check. This work establishes transmon-based dual-rail qubits as an attractive building block for hardware-efficient quantum error correction.
  arXiv  Detail & Related papers  (2023-07-17T18:00:01Z)
• On Fault Tolerance of Circuits with Intermediate Qutrit-assisted Gate Decomposition [3.452050192629253]
  An intermediate qutrit implies that a qubit is operated as a qutrit in a particular execution cycle. We study the challenges of including fault-tolerance in such a decomposition.
  arXiv  Detail & Related papers  (2022-12-15T14:33:45Z)
• Software mitigation of coherent two-qubit gate errors [55.878249096379804]
  Two-qubit gates are important components of quantum computing. But unwanted interactions between qubits (so-called parasitic gates) can degrade the performance of quantum applications. We present two software methods to mitigate parasitic two-qubit gate errors.
  arXiv  Detail & Related papers  (2021-11-08T17:37:27Z)
• Fault-tolerant parity readout on a shuttling-based trapped-ion quantum computer [64.47265213752996]
  We experimentally demonstrate a fault-tolerant weight-4 parity check measurement scheme. We achieve a flag-conditioned parity measurement single-shot fidelity of 93.2(2)%. The scheme is an essential building block in a broad class of stabilizer quantum error correction protocols.
  arXiv  Detail & Related papers  (2021-07-13T20:08:04Z)
• Second-Order Provable Defenses against Adversarial Attacks [63.34032156196848]
  We show that if the eigenvalues of the network are bounded, we can compute a certificate in the $l$ norm efficiently using convex optimization. We achieve certified accuracy of 5.78%, and 44.96%, and 43.19% on 2,59% and 4BP-based methods respectively.
  arXiv  Detail & Related papers  (2020-06-01T05:55:18Z)
• Bayesian Bits: Unifying Quantization and Pruning [73.27732135853243]
  We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. We experimentally validate our proposed method on several benchmark datasets and show that we can learn pruned, mixed precision networks.
  arXiv  Detail & Related papers  (2020-05-14T16:00:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.