Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Constrained-optimization Approach Delivers Superior Classical
  Performance for Graph Partitioning via Quantum-ready Method   Graph partitioning is one of an important set of well-known compute-intense
(NP-hard) graph problems that devolve to discrete constrained optimization. We
sampled solutions to the problem via two different quantum-ready methods to
understand the pros and cons of each method. First we formulated and sampled
the problem as a quadratic unconstrained binary optimization (QUBO) problem,
via the best known QUBO formulation, using a best-in-class QUBO sampler running
purely classically. Second, we formulated the problem at a higher level, as a
set of constraints and an objective function, and sampled it with a
constrained-optimization sampler (which internally samples the problem via
QUBOs also sampled classically). We find that both approaches often deliver
better partitions than the purpose-built classical graph partitioners. Further,
we find that the constrained-optimization approach is often able to deliver
better partitions in less time than the bespoke-QUBO approach, without
knowledge of the graph-partitioning problem.
  Stepping back from graph partitioning itself, one key controversial question
is whether bespoke algorithms or general tools are more likely to deliver the
power of QCs to real-world users. These results bear on that question, though
they require confirmation on other problems and instances as well as
replacement of the low-level sampler by a QC. Still, this early evidence
supports the proposition that general tools will contribute significantly to a
range of problems, expanding the impact of QCs. This benefit is independent of
the low-level sampler employed, whether software or QC, so reinforces the need
for more work on high-level optimization. An early version of such software is
commercially available in the cloud today, delivering superior classical
performance for some problems, enables quantum-forward organizations to migrate
to quantum-ready methods now.

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The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
1
The categories of this paper are: cs
25
The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci