Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

Abstract

The numerical simulation of quantum circuits is an indispensable tool for development, verification and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. We present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) which serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators which perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment which can scale from laptops to future exascale platforms. We report initial benchmarks of our framework which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.

Parallelizing Quantum Simulation With Decision Diagrams

“…The edges specify which indices are contracted [18]. Tensor networks are a well-established tool for analyzing quantum systems, and they have also been adapted for simulating quantum circuits [19] [20] [21]. Quantum states and quantum gates are represented by lowrank tensors.…”

Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease

“…One of the areas where quantum tensor networks show great potential is quantum simulations. The density matrix renormalization group method of matrix product states (MPS) and tensor network quantum virtual machines (TNQVM) provide a good representation of the quantum state and allow simulation of large-scale quantum circuits [47,48].…”

Fast Emulation of Fermionic Circuits with Matrix Product States

“…For example, approximate tensor network contraction, − stabilizer simulation, or Clifford perturbation theory have been used to classically emulate a quantum computational task at much lower complexity than exact statevector simulation of the quantum circuit. Beyond defining the boundary of quantum advantage, efficient circuit simulation has become crucial for quantum algorithm design and analysis. − In fact, the requirement for computational utility does not demand that classical emulators consider precisely the same circuit as the quantum implementation as long as the same result is obtained. In Fermionic settings, this has led to the development of Fermion-specific simulators and frameworks that take advantage of additional structure in the problems of interest , resulting in substantially lower emulation costs.…”