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Title: Logical abstractions for noisy variational Quantum algorithm simulation
Due to the unreliability and limited capacity of existing quantum computer prototypes, quantum circuit simulation continues to be a vital tool for validating next generation quantum computers and for studying variational quantum algorithms, which are among the leading candidates for useful quantum computation. Existing quantum circuit simulators do not address the common traits of variational algorithms, namely: 1) their ability to work with noisy qubits and operations, 2) their repeated execution of the same circuits but with different parameters, and 3) the fact that they sample from circuit final wavefunctions to drive a classical optimization routine. We present a quantum circuit simulation toolchain based on logical abstractions targeted for simulating variational algorithms. Our proposed toolchain encodes quantum amplitudes and noise probabilities in a probabilistic graphical model, and it compiles the circuits to logical formulas that support efficient repeated simulation of and sampling from quantum circuits for different parameters. Compared to state-of-the-art state vector and density matrix quantum circuit simulators, our simulation approach offers greater performance when sampling from noisy circuits with at least eight to 20 qubits and with around 12 operations on each qubit, making the approach ideal for simulating near-term variational quantum algorithms. And for simulating noise-free shallow quantum circuits with 32 qubits, our simulation approach offers a 66X reduction in more » sampling cost versus quantum circuit simulation techniques based on tensor network contraction. « less
Authors:
; ; ; ;
Award ID(s):
1730449 1837129 1730082
Publication Date:
NSF-PAR ID:
10286343
Journal Name:
26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’21)
Page Range or eLocation-ID:
456 to 472
Sponsoring Org:
National Science Foundation
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