Compiling highlevel quantum programs to machines that are size constrained (i.e. limited number of quantum bits) and time constrained (i.e. limited number of quantum operations) is challenging. In this paper, we present SQUARE (Strategic QUantum Ancilla REuse), a compilation infrastructure that tackles allocation and reclamation of scratch qubits (called ancilla) in modular quantum programs. At its core, SQUARE strategically performs uncomputation to create opportunities for qubit reuse.
Current Noisy IntermediateScale Quantum (NISQ) computers and forwardlooking FaultTolerant (FT) quantum computers have fundamentally different constraints such as data locality, instruction parallelism, and communication overhead. Our heuristicbased ancillareuse algorithm balances these considerations and fits computations into resourceconstrained NISQ or FT quantum machines, throttling parallelism when necessary. To precisely capture the workload of a program, we propose an improved metric, the "active quantum volume," and use this metric to evaluate the effectiveness of our algorithm. Our results show that SQUARE improves the average success rate of NISQ applications by 1.47X. Surprisingly, the additional gates for uncomputation create ancilla with better locality, and result in substantially fewer swap gates and less gate noise overall. SQUARE also achieves an average reduction of 1.5X (and up to 9.6X) in active quantum volume for FT machines.
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Graph Neural Network Assisted Quantum Compilation for Qubit Allocation
Quantum computers in the current noisy intermediatescale quantum
(NISQ) era face two major limitations  size and error vulnerability.
Although quantum error correction (QEC) methods exist,
they are not applicable at the current size of computers, requiring
thousands of qubits, while NISQ systems have nearly one hundred
at most. One common approach to improve reliability is to
adjust the compilation process to create a more reliable final circuit,
where the two most critical compilation decisions are the qubit
allocation and qubit routing problems. We focus on solving the
qubit allocation problem and identifying initial layouts that result
in a reduction of error. To identify these layouts, we combine reinforcement
learning with a graph neural network (GNN)based
Qnetwork to process the mesh topology of the quantum computer,
known as the backend, and make mapping decisions, creating a
Graph Neural Network Assisted Quantum Compilation (GNAQC)
strategy. We train the architecture using a set of four backends
and six circuits and find that GNAQC improves output fidelity by
roughly 12.7% over preexisting allocation methods.
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 Award ID(s):
 2019511
 NSFPAR ID:
 10431815
 Date Published:
 Journal Name:
 Proceedings of 33rd Great Lakes Symposium on VLSI (GLSVLSI)
 Volume:
 1
 Page Range / eLocation ID:
 415 to 419
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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