Manjushri: A Tool for Equivalence Checking of Quantum Circuits

Verifying whether two quantum circuits are equivalent is a central challenge in the compilation and optimization of quantum programs. We introduce \textsc{Manjushri}, a new automated framework for scalable quantum-circuit equivalence checking. \textsc{Manjushri} uses local projections as discriminative circuit fingerprints, implemented with weighted binary decision diagrams (WBDDs), yielding a compact and efficient symbolic representation of quantum behavior. We present an extensive experimental evaluation that, for random 1D Clifford+$T$ circuits, explores the trade-off between \textsc{Manjushri} and \textsc{ECMC}, a tool for equivalence checking based on a much different approach. \textsc{Manjushri} is much faster up to depth 30 (with the crossover point varying from 39--49, depending on the number of qubits and whether the input circuits are equivalent or inequivalent): when inputs are equivalent, \textsc{Manjushri} is about 10$\times$ faster (or more); when inputs are inequivalent, \textsc{Manjushri} is about 8$\times$ faster (or more). For both kinds of equivalence-checking outcomes, \textsc{ECMC}'s success rate out to depth 50 is impressive on 32- and 64-qubit circuits: on such circuits, \textsc{ECMC} is almost uniformly successful. However, \textsc{ECMC} struggled on 128-qubit circuits for some depths. \textsc{Manjushri} is almost uniformly successful out to about depth 38, before tailing off to about 75\% at depth 50 (falling to 0\% at depth 48 for 128-qubit circuits that are equivalent). These results establish that \textsc{Manjushri} is a practical and scalable solution for large-scale quantum-circuit verification, and would be the preferred choice unless clients need to check equivalence of circuits of depth $>$38.