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        <identifier>oai:drops-oai.dagstuhl.de:19529</identifier>
        <datestamp>2024-03-06T11:04:14Z</datestamp>
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          <dc:title>A Qubit, a Coin, and an Advice String Walk into a Relational Problem</dc:title>
          <dc:creator>Aaronson, Scott</dc:creator>
          <dc:creator>Buhrman, Harry</dc:creator>
          <dc:creator>Kretschmer, William</dc:creator>
          <dc:subject>Relational problems</dc:subject>
          <dc:subject>quantum advice</dc:subject>
          <dc:subject>randomized advice</dc:subject>
          <dc:subject>FBQP</dc:subject>
          <dc:subject>FBPP</dc:subject>
          <dc:description>Relational problems (those with many possible valid outputs) are different from decision problems, but it is easy to forget just how different. This paper initiates the study of FBQP/qpoly, the class of relational problems solvable in quantum polynomial-time with the help of polynomial-sized quantum advice, along with its analogues for deterministic and randomized computation (FP, FBPP) and advice (/poly, /rpoly).&#13;
Our first result is that FBQP/qpoly ≠ FBQP/poly, unconditionally, with no oracle - a striking contrast with what we know about the analogous decision classes. The proof repurposes the separation between quantum and classical one-way communication complexities due to Bar-Yossef, Jayram, and Kerenidis. We discuss how this separation raises the prospect of near-term experiments to demonstrate "quantum information supremacy," a form of quantum supremacy that would not depend on unproved complexity assumptions.&#13;
Our second result is that FBPP ̸ ⊂ FP/poly - that is, Adleman’s Theorem fails for relational problems - unless PSPACE ⊂ NP/poly. Our proof uses IP = PSPACE and time-bounded Kolmogorov complexity. On the other hand, we show that proving FBPP ̸ ⊂ FP/poly will be hard, as it implies a superpolynomial circuit lower bound for PromiseBPEXP.&#13;
We prove the following further results:  &#13;
- Unconditionally, FP ≠ FBPP and FP/poly ≠ FBPP/poly (even when these classes are carefully defined). &#13;
- FBPP/poly = FBPP/rpoly (and likewise for FBQP). For sampling problems, by contrast, SampBPP/poly ≠ SampBPP/rpoly (and likewise for SampBQP).</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Scott Aaronson and Harry Buhrman and William Kretschmer</dc:contributor>
          <dc:date>2024</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ITCS.2024.1</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-195290</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.1</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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