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Classical Simulation of Quantum Supremacy Circuits

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arxiv 2005.06787 v1 pith:WPZYW6MT submitted 2020-05-14 quant-ph

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keywords quantumclassicalsupremacytasksimulationcircuitscomputationalcomputer
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It is believed that random quantum circuits are difficult to simulate classically. These have been used to demonstrate quantum supremacy: the execution of a computational task on a quantum computer that is infeasible for any classical computer. The task underlying the assertion of quantum supremacy by Arute et al. (Nature, 574, 505--510 (2019)) was initially estimated to require Summit, the world's most powerful supercomputer today, approximately 10,000 years. The same task was performed on the Sycamore quantum processor in only 200 seconds. In this work, we present a tensor network-based classical simulation algorithm. Using a Summit-comparable cluster, we estimate that our simulator can perform this task in less than 20 days. On moderately-sized instances, we reduce the runtime from years to minutes, running several times faster than Sycamore itself. These estimates are based on explicit simulations of parallel subtasks, and leave no room for hidden costs. The simulator's key ingredient is identifying and optimizing the "stem" of the computation: a sequence of pairwise tensor contractions that dominates the computational cost. This orders-of-magnitude reduction in classical simulation time, together with proposals for further significant improvements, indicates that achieving quantum supremacy may require a period of continuing quantum hardware developments without an unequivocal first demonstration.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 84 citations worldwide. Full citation record

  1. Constructive interference at the edge of quantum ergodic dynamics

    quant-ph 2025-06 conditional novelty 7.0 of 10

    Second-order out-of-time-order correlators measured on 65-qubit random circuits remain sensitive to dynamics and are estimated to be beyond the reach of current classical tensor-network simulation.

  2. Hardness and Complexity Transition of Noisy Random Circuit Sampling

    quant-ph 2026-07 accept novelty 6.0 of 10

    Under the standard ideal-RCS #P-hardness conjecture, noisy random circuit sampling remains hard for depolarizing noise γ = O(log n/(nd)), and matching simulability results make γ = Θ(log n/(nd)) the transition scale.

  3. Optimizing Tensor Network Partitioning using Simulated Annealing

    quant-ph 2025-07 conditional novelty 6.0 of 10

    A simulated annealing refinement of tensor network partitionings for distributed contraction lowers estimated computational and memory cost by about 8x on average versus naive partitioning on MQT Bench circuits.

  4. Hierarchical Search of Tree Tensor Networks for High-Dimensional Data

    cs.CE 2026-03 conditional novelty 5.5 of 10

    A hierarchical, entropy-guided search algorithm automatically rewires tree tensor networks and reshapes their indices, delivering 2.5–100× better compression than fixed Tensor Train/Hierarchical Tucker formats on phys...

  5. Matrix Product Evolution: A Method for Simulating Quantum Circuits Using Tensor Networks

    quant-ph 2026-08 conditional novelty 5.0 of 10

    A depth-oriented tensor-network contraction method, called MPE, is introduced and shown to gain accuracy from post-selection, complementing standard MPS simulation.

  6. Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling

    cs.LG 2026-07 conditional novelty 5.0 of 10

    The paper argues that probabilistic scaling alone cannot fix the validity gap in quantum circuit generation, so quantum code assistants must build verification into generation rather than filter outputs after the fact.

  7. Loophole-Robust Certification of Quantum Advantage

    quant-ph 2026-07 accept novelty 5.0 of 10

    For any bounded-reward task, a classical strategy with benchmark-dependent side information can improve over the loophole-free classical score by at most the total-variation strength η of that dependence.

  8. Quantum Supremacy through Fock State $q$ boson Sampling with Transmon Qubits

    quant-ph 2025-06 reject novelty 4.0 of 10

    A transmon's nonlinear spectrum can be approximated by a q-boson with q=1+K/omega, and the paper argues this enables Fock-state q-boson sampling with potential quantum supremacy.

  9. Hybrid Quantum Neural Networks: Theory, Implementations, and Applications

    quant-ph 2026-08 conditional novelty 2.0 of 10

    A balanced review of hybrid quantum neural networks, concluding that quantum layers help on structured, small-scale and quantum-native problems but do not yet beat classical models on generic benchmarks.

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