Classical simulation of 1000-qubit circuits via MPS/KLT – open-source Python SDK
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I've been working on a classical quantum circuit simulator that uses matrix product states (MPS) to handle large-qubit circuits on standard CPU hardware. Releasing it today as an open-source Python SDK. **How it works** The engine routes each circuit to the most efficient representation based on qubit count and entanglement structure: - ≤20 qubits: exact statevector - 21–54 qubits: MPS with χ = 2^depth (exact for structured circuits) - 55–105 qubits: MPS χ = 32 - 106–1,000 qubits: MPS χ = 8 The key insight: most practically interesting circuits in this size range have bounded entanglement, which keeps the bond dimension tractable. A 1,000-qubit GHZ state at depth 3 runs in under 1 second using 1 MB of memory. **Benchmark numbers** | Circuit | Result | |---|---| | 1,000-qubit GHZ, depth 3 | < 1s, 1 MB | | 105-qubit Willow-layout RCS, depth 5 | < 0.5s | | 20-qubit RCS exact statevector | XEB error 0.00%, 8–9s | | 12×12 hafnian (GBS) | error < 5×10⁻¹⁵, 43ms | **Drop-in Qiskit / Cirq backend** from qumulator.backends import QumulatorBackend backend = QumulatorBackend() # use exactly like any Qiskit backend No code changes required if you're already on Qiskit or Cirq. **Free to use, MIT-licensed SDK** GitHub: https://github.com/qumulator/qumulator-sdk Docs + free API key: https://qumulator.com Happy to discuss the simulation approach, entanglement regimes, or the KLT engine in the comments. Obvious limitation: high-entanglement circuits (e.g., deep random circuits) will not compress well — the MPS bond dimension grows exponentially with entanglement, same as any MPS method. submitted by /u/nnoorbakhsh [link] [comments]