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Pareto-Efficient Quantum Circuit Simulation Using Tensor Contraction Deferral

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arxiv 1710.05867 v4 pith:NGQOYYCJ submitted 2017-10-16 quant-ph

Pareto-Efficient Quantum Circuit Simulation Using Tensor Contraction Deferral

Edwin Pednault , John A. Gunnels , Giacomo Nannicini , Lior Horesh , Thomas Magerlein , Edgar Solomonik , Erik W. Draeger , Eric T. Holland
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Robert Wisnieff
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classification quant-ph
keywords quantumamplitudescircuitdepthqubitsrandomtimescalculations
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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With the current rate of progress in quantum computing technologies, systems with more than 50 qubits will soon become reality. Computing ideal quantum state amplitudes for circuits of such and larger sizes is a fundamental step to assess both the correctness, performance, and scaling behavior of quantum algorithms and the fidelities of quantum devices. However, resource requirements for such calculations on classical computers grow exponentially. We show that deferring tensor contractions can extend the boundaries of what can be computed on classical systems. To demonstrate this technique, we present results obtained from a calculation of the complete set of output amplitudes of a universal random circuit with depth 27 in a 2D lattice of $7 \times 7$ qubits, and an arbitrarily selected slice of $2^{37}$ amplitudes of a universal random circuit with depth 23 in a 2D lattice of $8 \times 7$ qubits. Combining our methodology with other decomposition approaches found in the literature, we show that we can simulate $7 \times 7$-qubit random circuits to arbitrary depth by leveraging secondary storage. These calculations were thought to be impossible due to resource requirements.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Adaptive Multi-Backend Simulation of Near-Clifford Quantum Circuits via Spatial Stabilizer-Frame Partitioning

    quant-ph 2026-07 conditional novelty 6.0

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  2. Optimizing Memory Efficiency and Index Ordering to Simulate Quantum Circuits Using Tensor Decision Diagrams

    cs.ET 2026-07 conditional novelty 5.0

    Hardware-aware FTDD memory management plus a Path index-order heuristic bounds RAM and simulates structured circuits (e.g. QFT) up to 100 qubits, with large topology-dependent speedups.

  3. GPU-Accelerated Quantum Simulation: Empirical Backend Selection, Gate Fusion, and Adaptive Precision

    quant-ph 2026-04 unverdicted novelty 5.0

    A new GPU quantum simulator framework achieves 64x-146x speedups for 20-28 qubit circuits via backend selection, gate fusion, and adaptive precision while integrating with Qiskit and others.

  4. Many-Body Physics and Quantum Simulations with Strongly Interacting Photons

    quant-ph 2019-07 unverdicted novelty 1.0

    Review of proposals and experiments using coupled cavity arrays and superconducting circuits to realize many-body physics with photons, including Mott transitions, fractional quantum Hall states, and dissipative phase...