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Revision #2 to TR20-043 | 23rd September 2020 09:33

Two combinatorial MA-complete problems

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Revision #2
Authors: Dorit Aharonov, Alex Bredariol Grilo
Accepted on: 23rd September 2020 09:33
Downloads: 136
Keywords: 


Abstract:

Despite the interest in the complexity class MA, the randomized analog of NP, just a few natural MA-complete problems are known. The first problem was found by (Bravyi and Terhal, SIAM Journal of Computing 2009); it was then followed by (Crosson, Bacon and Brown, PRE 2010) and (Bravyi, Quantum Information and Computation 2015). Surprisingly, two of these problems are defined using terminology from quantum computation, while the third is inspired by quantum computation and keeps a physical terminology. This prevents classical complexity theorists from studying these problems, delaying potential progress, e.g., on the NP vs. MA question.
Here, we define two new combinatorial problems and prove their MA-completeness. The first problem, ACAC, gets as input a succinctly described graph, with some marked vertices. The problem is to decide whether there is a connected component with only unmarked vertices, or the graph is far from having this property. The second problem, SetCSP, generalizes standard constraint satisfaction problem (CSP) into constraints involving sets of strings.
Technically, our proof that SetCSP is MA-complete is based on an observation by (Aharonov and Grilo, FOCS 2019), in which it was noted that a restricted case of Bravyi and Terhal's problem (namely, the uniform case) is already MA-complete; a simple trick allows to state this restricted case using combinatorial language. The fact that the first, more natural, problem of ACAC is MA-hard follows quite naturally from this proof, while the containment of ACAC in MA is based on the theory of random walks.
We notice that the main result of Aharonov and Grilo carries over to the SetCSP problem in a straightforward way, implying that finding a gap-amplification procedure for SetCSP (as in Dinur's PCP proof) is equivalent to MA=NP. This provides an alternative new path towards the major problem of derandomizing MA.



Changes to previous version:

Added a new MA-complete problem


Revision #1 to TR20-043 | 23rd September 2020 09:31

Two combinatorial MA-complete problems





Revision #1
Authors: Dorit Aharonov, Alex Bredariol Grilo
Accepted on: 23rd September 2020 09:31
Downloads: 103
Keywords: 


Abstract:

Despite the interest in the complexity class MA, the randomized analog of NP, just a few natural MA-complete problems are known. The first problem was found by (Bravyi and Terhal, SIAM Journal of Computing 2009); it was then followed by (Crosson, Bacon and Brown, PRE 2010) and (Bravyi, Quantum Information and Computation 2015). Surprisingly, two of these problems are defined using terminology from quantum computation, while the third is inspired by quantum computation and keeps a physical terminology. This prevents classical complexity theorists from studying these problems, delaying potential progress, e.g., on the NP vs. MA question.
Here, we define two new combinatorial problems and prove their MA-completeness. The first problem, ACAC, gets as input a succinctly described graph, with some marked vertices. The problem is to decide whether there is a connected component with only unmarked vertices, or the graph is far from having this property. The second problem, SetCSP, generalizes standard constraint satisfaction problem (CSP) into constraints involving sets of strings.
Technically, our proof that SetCSP is MA-complete is based on an observation by (Aharonov and Grilo, FOCS 2019), in which it was noted that a restricted case of Bravyi and Terhal's problem (namely, the uniform case) is already MA-complete; a simple trick allows to state this restricted case using combinatorial language. The fact that the first, more natural, problem of ACAC is MA-hard follows quite naturally from this proof, while the containment of ACAC in MA is based on the theory of random walks.
We notice that the main result of Aharonov and Grilo carries over to the SetCSP problem in a straightforward way, implying that finding a gap-amplification procedure for SetCSP (as in Dinur's PCP proof) is equivalent to MA=NP. This provides an alternative new path towards the major problem of derandomizing MA.



Changes to previous version:

Added a new MA-complete problem


Paper:

TR20-043 | 29th March 2020 19:09

A combinatorial MA-complete problem





TR20-043
Authors: Dorit Aharonov, Alex Bredariol Grilo
Publication: 5th April 2020 22:37
Downloads: 279
Keywords: 


Abstract:

Despite the interest in the complexity class MA, the randomized analog of NP, there is just a couple of known natural (promise-)MA-complete problems, the first due to Bravyi and Terhal (SIAM Journal of Computing 2009) and the second due to Bravyi (Quantum Information and Computation 2015). Surprisingly, both problems are stated using terminology from quantum computation. This fact makes it hard for classical complexity theorists to study these problems, and prevents possible progress, e.g., on the important question of derandomizing MA.

In this note we define a natural combinatorial problem called SetCSP and prove its MA-completeness. The problem generalizes the constraint satisfaction problem (CSP) into constraints on sets of strings. This note is, in fact, a combination of previously known works: the brilliant work of Bravyi and Terhal, together with an observation made in our previous work (Aharonov and Grilo, FOCS 2019) that a restricted case of the Bravyi and Terhal's MA complete problem (namely, the uniform case) is already complete, and moreover, that this restricted case can be stated using a classical, combinatorial description. Here we flesh out this observation.

This note, along with a translation of the main result of Aharonov and Grilo to the SetCSP language, implies that finding a gap-amplification procedure for SetCSP problems (namely a generalization to SetCSPs of the gap-amplification used in Dinur's PCP proof) would imply MA=NP. This would provide a resolution of the major problem of derandomizing MA; in fact, the problem of finding gap-amplification for SetCSP is in fact equivalent to the MA=NP problem.



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