Oracles Are Subtle But Not Malicious

Theoretical computer scientists have been debating the role of oracles since the 1970's. This paper illustrates both that oracles can give us nontrivial insights about the barrier problems in circuit complexity, and that they need not prevent us from trying to solve those problems. First, we give an oracle relative to which PP has linear-sized circuits, by proving a new lower bound for perceptrons and low- degree threshold polynomials. This oracle settles a longstanding open question, and generalizes earlier results due to Beigel and to Buhrman, Fortnow, and Thierauf. More importantly, it implies the first nonrelativizing separation of "traditional" complexity classes, as opposed to interactive proof classes such as MIP and MA-EXP. For Vinodchandran showed, by a nonrelativizing argument, that PP does not have circuits of size n^k for any fixed k. We present an alternative proof of this fact, which shows that PP does not even have quantum circuits of size n^k with quantum advice. To our knowledge, this is the first nontrivial lower bound on quantum circuit size. Second, we study a beautiful algorithm of Bshouty et al. for learning Boolean circuits in ZPP^NP. We show that the NP queries in this algorithm cannot be parallelized by any relativizing technique, by giving an oracle relative to which ZPP^||NP and even BPP^||NP have linear-size circuits. On the other hand, we also show that the NP queries could be parallelized if P=NP. Thus, classes such as ZPP^||NP inhabit a "twilight zone," where we need to distinguish between relativizing and black-box techniques. Our results on this subject have implications for computational learning theory as well as for the circuit minimization problem.Comment: 20 pages, 1 figur

Quantum Meets the Minimum Circuit Size Problem

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction

Relations and Equivalences Between Circuit Lower Bounds and Karp-Lipton Theorems

A frontier open problem in circuit complexity is to prove P^{NP} is not in SIZE[n^k] for all k; this is a necessary intermediate step towards NP is not in P_{/poly}. Previously, for several classes containing P^{NP}, including NP^{NP}, ZPP^{NP}, and S_2 P, such lower bounds have been proved via Karp-Lipton-style Theorems: to prove C is not in SIZE[n^k] for all k, we show that C subset P_{/poly} implies a "collapse" D = C for some larger class D, where we already know D is not in SIZE[n^k] for all k. It seems obvious that one could take a different approach to prove circuit lower bounds for P^{NP} that does not require proving any Karp-Lipton-style theorems along the way. We show this intuition is wrong: (weak) Karp-Lipton-style theorems for P^{NP} are equivalent to fixed-polynomial size circuit lower bounds for P^{NP}. That is, P^{NP} is not in SIZE[n^k] for all k if and only if (NP subset P_{/poly} implies PH subset i.o.- P^{NP}_{/n}). Next, we present new consequences of the assumption NP subset P_{/poly}, towards proving similar results for NP circuit lower bounds. We show that under the assumption, fixed-polynomial circuit lower bounds for NP, nondeterministic polynomial-time derandomizations, and various fixed-polynomial time simulations of NP are all equivalent. Applying this equivalence, we show that circuit lower bounds for NP imply better Karp-Lipton collapses. That is, if NP is not in SIZE[n^k] for all k, then for all C in {Parity-P, PP, PSPACE, EXP}, C subset P_{/poly} implies C subset i.o.-NP_{/n^epsilon} for all epsilon > 0. Note that unconditionally, the collapses are only to MA and not NP. We also explore consequences of circuit lower bounds for a sparse language in NP. Among other results, we show if a polynomially-sparse NP language does not have n^{1+epsilon}-size circuits, then MA subset i.o.-NP_{/O(log n)}, MA subset i.o.-P^{NP[O(log n)]}, and NEXP is not in SIZE[2^{o(m)}]. Finally, we observe connections between these results and the "hardness magnification" phenomena described in recent works

A Full Characterization of Quantum Advice

We prove the following surprising result: given any quantum state rho on n qubits, there exists a local Hamiltonian H on poly(n) qubits (e.g., a sum of two-qubit interactions), such that any ground state of H can be used to simulate rho on all quantum circuits of fixed polynomial size. In terms of complexity classes, this implies that BQP/qpoly is contained in QMA/poly, which supersedes the previous result of Aaronson that BQP/qpoly is contained in PP/poly. Indeed, we can exactly characterize quantum advice, as equivalent in power to untrusted quantum advice combined with trusted classical advice. Proving our main result requires combining a large number of previous tools -- including a result of Alon et al. on learning of real-valued concept classes, a result of Aaronson on the learnability of quantum states, and a result of Aharonov and Regev on "QMA+ super-verifiers" -- and also creating some new ones. The main new tool is a so-called majority-certificates lemma, which is closely related to boosting in machine learning, and which seems likely to find independent applications. In its simplest version, this lemma says the following. Given any set S of Boolean functions on n variables, any function f in S can be expressed as the pointwise majority of m=O(n) functions f1,...,fm in S, such that each fi is the unique function in S compatible with O(log|S|) input/output constraints.Comment: We fixed two significant issues: 1. The definition of YQP machines needed to be changed to preserve our results. The revised definition is more natural and has the same intuitive interpretation. 2. We needed properties of Local Hamiltonian reductions going beyond those proved in previous works (whose results we'd misstated). We now prove the needed properties. See p. 6 for more on both point

On TC0 Lower Bounds for the Permanent

Abstract In this paper we consider the problem of proving lower bounds for the permanent. An ongoing line of research has shown super-polynomial lower bounds for slightly-non-uniform small-depth threshold and arithmetic circuits [All99, KP09, JS11, JS12]. We prove a new parameterized lower bound that includes each of the previous results as sub-cases. Our main result implies that the permanent does not have Boolean threshold circuits of the following kinds

Bounded Relativization

Relativization is one of the most fundamental concepts in complexity theory, which explains the difficulty of resolving major open problems. In this paper, we propose a weaker notion of relativization called bounded relativization. For a complexity class ?, we say that a statement is ?-relativizing if the statement holds relative to every oracle ? ? ?. It is easy to see that every result that relativizes also ?-relativizes for every complexity class ?. On the other hand, we observe that many non-relativizing results, such as IP = PSPACE, are in fact PSPACE-relativizing. First, we use the idea of bounded relativization to obtain new lower bound results, including the following nearly maximum circuit lower bound: for every constant ? > 0, BPE^{MCSP}/2^{?n} ? SIZE[2?/n]. We prove this by PSPACE-relativizing the recent pseudodeterministic pseudorandom generator by Lu, Oliveira, and Santhanam (STOC 2021). Next, we study the limitations of PSPACE-relativizing proof techniques, and show that a seemingly minor improvement over the known results using PSPACE-relativizing techniques would imply a breakthrough separation NP ? L. For example: - Impagliazzo and Wigderson (JCSS 2001) proved that if EXP ? BPP, then BPP admits infinitely-often subexponential-time heuristic derandomization. We show that their result is PSPACE-relativizing, and that improving it to worst-case derandomization using PSPACE-relativizing techniques implies NP ? L. - Oliveira and Santhanam (STOC 2017) recently proved that every dense subset in P admits an infinitely-often subexponential-time pseudodeterministic construction, which we observe is PSPACE-relativizing. Improving this to almost-everywhere (pseudodeterministic) or (infinitely-often) deterministic constructions by PSPACE-relativizing techniques implies NP ? L. - Santhanam (SICOMP 2009) proved that pr-MA does not have fixed polynomial-size circuits. This lower bound can be shown PSPACE-relativizing, and we show that improving it to an almost-everywhere lower bound using PSPACE-relativizing techniques implies NP ? L. In fact, we show that if we can use PSPACE-relativizing techniques to obtain the above-mentioned improvements, then PSPACE ? EXPH. We obtain our barrier results by constructing suitable oracles computable in EXPH relative to which these improvements are impossible

Quantum Complexity, Relativized Worlds, and Oracle Separations

Η κλάση πολυπλοκότητας QMA, που ορίσθηκε από τον Watrous, το 2000, είναι το κβαντικό ανάλογο της MA, που ορίσθηκε από τον Babai, το 1985, και η οποία είναι μία γενίκευση της κλάσης NP. Η κλάση MA γενικεύει την NP με την εξής έννοια: η επαληθευτική διαδικασία στην κλάση MA είναι πιθανοκρατική, ενώ στην NP είναι πλήρως ντετερμινιστική. Το 2014, οι Grilo, Kerenidis και Sikora, απέδειξαν ότι η κβαντική απόδειξη που ανακύπτει στον ορισμό της QMA μπορεί, σε κάθε περίπτωση, να αντικατασταθεί από μία, κατάλληλα ορισμένη, κβαντική κατάσταση-υποσύνολο. Οι Grilo κ.ά. ονόμασαν την κλάση αυτή SQMA, για ‘subset-state quantum Merlin-Arthur.’ ΄Αρα QMA ⊆ SQMA, και κάποιος θα μπορούσε να γράψει ότι QMA = SQMA, μιά και ο εγκλεισμός SQMA ⊆ QMA ισχύει τετριμμένα. Μετά από αυτό το αποτέλεσμα, από τους Grilo κ.ά., οι Fefferman και Kimmel, το 2015, απέδει- ξαν ότι υπάρχει κάποιο κβαντικό μαντείο A—παρόμοιο με αυτό που εισήγαγαν οι Aaronson και Kuperberg, το 2006, για να δείξουν ότι υπάρχει μαντείο A τέτοιο ώστε QMAA 1 6⊆ QCMAA—το οποίο είναι τέτοιο ώστε QMAA = SQMAA 6⊆ QCMAA. Σημειώνουμε εδώ ότι η QCMA είναι αυτή η έκδοση της QMA, που ορίσθηκε από τους Aharonov και Naveh, το 2002, σύμφωνα με την οποία η προς επαλήθευση απόδειξη είναι πλήρως κλασσική, π.χ. μία συμβολοσειρά 0-1-χαρακτήρων, και η QMA1 είναι η έκδοση τέλειας πληρότητας της QMA, δηλαδή είναι η έκδοση της QMA κατά την οποία για κάθε ΝΑΙ απάντηση, στο εξεταζόμενο πρόβλημα απόφασης, υπάρχει μία απόδειξη που κά- νει τον επαληθευτή να απαντήσει ΝΑΙ με πιθανότητα ίση με ένα. Στον μαντειακό τους διαχωρισμό οι Fefferman και Kimmel, εισήγαγαν, και χρησιμοποίησαν, μία ενδιαφέρουσα διαδικασία κατά την οποία κάποιος μπορεί να αποδείξει ότι L ∈/ QCMA, για κάποια γλώσσα L που ικανοποιεί κάποιες συγκεκριμένες ιδιότητες. Χρησιμοποιώντας αυτό το αποτέλεσμα των Fefferman και Kimmel, αποδεικνύουμε ότι υπάρχει κάποιο κβαντικό μαντείο τέτοιο ώστε SQMAA 1 6⊆ QCMAA. Σημειώνουμε εδώ ότι η κλάση SQMA1 είναι η έκδοση τέλειας πληρότητας της SQMA. Στην απόδειξή μας χρησιμοποιήσαμε την εν λόγω διαδικασία των Fefferman και Kimmel, μία εκδοχή των βασικών μαντειακών τους κατασκευών, όπως και το πρόβλημα απόφασης που χρησιμοποίησαν για την απόδειξη του διαχωρισμού τους. Σημειώνουμε εδώ ότι το αποτέλεσμά μας συνεπάγεται αυτό των Fefferman και Kimmel, μιά και ισχύει ότι SQMA1 ⊆ SQMA. Αφού διατυπώσουμε και αποδείξουμε το αποτέλεσμά μας, κάνουμε μία παράκαμψη για να εξερευνή- σουμε τον κόσμο των μαντειακών διαχωρισμών τόσο στον κλασσικό όσο και τον κβαντικό κόσμο. Εξερευνούμε κάποια αποτελέσματα, και τις υποβόσκουσες μεθόδους τους, που είναι σχετικά με την χρήση κλασσικών ή κβαντικών μαντείων σε μαντειακούς διαχωρισμούς που αφορούν σε κλασσικές ή κβαντικές κλάσεις πολυπλοκότητας. ΄Αρα εξερευνούμε κάποιες πολύ ενδιαφέρουσες πτυχές των διαχωριστικών αποτελεσμάτων που είναι σχετικά με σχετικιστικούς κόσμους. Τελικά, επιστρέφουμε, στο ερευνητικό τοπίο, ώστε να προσεγγίσουμε την ερώτηση σχετικά με την υποτιθέμενη ύπαρξη, ή όχι, ενός μαντείου A που είναι τέτοιο ώστε QMAA 1 6⊆ SQMAA 1 . Καταγρά- φουμε τις πρώτες μας προσπάθειες, και ιδέες, μέχρι τώρα.The complexity class QMA, defined by Watrous, in 2000, is the quantum analogue of MA, defined by Babai, in 1985, which, in turn, is a generalization of the class NP. The class MA generalizes the class NP in the sense that the verification procedure of the purported proof, put forth by the prover, is carried out by a probabilistic machine, rather than a deterministic one—as the definition of the class NP demands. In 2014, Grilo, Kerenidis, and Sikora, proved that the quantum proof, in the setting of QMA, may always be replaced by, an appropriately defined, quantum subset state—without any conceptual loss. That is, QMA ⊆ SQMA. Grilo et al., named their new class SQMA, for subset-state quantum MerlinArthur. Thus, one could write that SQMA = QMA, as the inclusion SQMA ⊆ QMA holds trivially. After this result, by Grilo, Kerenidis, and Sikora, Fefferman and Kimmel, in 2015, used this new characterization of QMA, and further proved that there exists some quantum oracle A—similar to that Aaronson and Kuperberg introduced, and used, in 2006, to show that QMAA 1 6⊆ QCMAA—which is such that QMAA = SQMAA 6⊆ QCMAA. Here, QCMA is that version of QMA, defined by Aharonov, and Naveh, in 2002, in which the purported proof is purely-classical, that is, a bitstring, and QMA1 is the perfect completeness version of QMA. In their separation, Fefferman and Kimmel introduced, and used, an interesting template to obtain oracle separations against the class QCMA. Drawing upon this recent result, by Fefferman and Kimmel, we prove that there exists some quantum oracle A, such that SQMAA 1 6⊆ QCMAA. We note that the class SQMA1 is the perfect completeness version of the class SQMA. In our proof, we used the template of Fefferman and Kimmel, a modified version of their basic quantum oracle construction, as well as the basic decision problem, that they themselves used for their separation. Note that our result implies that of Fefferman and Kimmel, as the inclusion xiii SQMA1 ⊆ SQMA holds. After we state and prove our result, we take a detour to explore a bit the world of oracle separations, both in the classical and the quantum setting. That is, we explore some results, and their underlying methods, about classical and quantum oracles being employed for proving separations— about classical, or quantum, complexity classes. Hence, we investigate some gems pertaining to the, not few at all, nor uninteresting, privileged relativized worlds. Finally, we return, to the research setting, to approach the open question of whether there exists some classical, or quantum, oracle A, such that QMAA 1 6⊆ SQMAA 1 , or not. We record our efforts, and some of our first ideas, thus far

De PH a IP : un curso en complejidad computacional

Tesis (Lic. en Cs. de la Computación)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2019.En este trabajo estudiamos algunas de las clases más importantes de la teoría de Complejidad Computacional. Nos basamos en el programa que propone el libro Computational Complexity a modern approach, del cual vemos la segunda mitad de la primera parte del programa (excluyendo Criptografía, Computación Cuántica y el Teorema PCP). En particular, estudiamos la clase de la Jerarquía Polinomial (PH), la clase de Circuitos Booleanos (P /poly ), la Computación Randomizada (BPP) y los Protocolos Interactivos (IP). Además vemos las principales técnicas de la teoría para obtener resultados las cuales son Diagonalización, Lower bounds y Arithmetization. Y estudiamos también sus respectivas limitaciones: Relativización, Natural proofs y Algebrization.In this work we study some of the most important classes of the Computational Complexity Theory. We base on the program proposed by the book Computational Complexity a modern approach, of which we see the second half of the first part of the program (excluding Cryptography, Quantum Computing and the PCP Theorem). In particular, we study the class of the Polynomial Hierarchy (PH), the class of Boolean Circuits (P /poly ), the Randomized Computing (BPP) and the Interactive Protocols (IP). In addition we see the main techniques of the theory to obtain results which are Diagonalization, Lower bounds and Arithmetization. And we also study their respective limitations: Relativization, Natural proofs and Algebrization.Fil: Made Vollenweider, Ignacio. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina

Computational complexity and the P vs NP problem

Orientador: Arnaldo Vieira MouraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação CientíficaResumo: A teoria de complexidade computacional procura estabelecer limites para a eficiência dos algoritmos, investigando a dificuldade inerente dos problemas computacionais. O problema P vs NP é uma questão central em complexidade computacional. Informalmente, ele procura determinar se, para uma classe importante de problemas computacionais, a busca exaustiva por soluções é essencialmente a melhor alternativa algorítmica possível. Esta dissertação oferece tanto uma introdução clássica ao tema, quanto uma exposição a diversos teoremas mais avançados, resultados recentes e problemas em aberto. Em particular, o método da diagonalização é discutido em profundidade. Os principais resultados obtidos por diagonalização são os teoremas de hierarquia de tempo e de espaço (Hartmanis e Stearns [54, 104]). Apresentamos uma generalização desses resultados, obtendo como corolários os teoremas clássicos provados por Hartmanis e Stearns. Essa é a primeira vez que uma prova unificada desses resultados aparece na literaturaAbstract: Computational complexity theory is the field of theoretical computer science that aims to establish limits on the efficiency of algorithms. The main open question in computational complexity is the P vs NP problem. Intuitively, it states that, for several important computational problems, there is no algorithm that performs better than a trivial exhaustive search. We present here an introduction to the subject, followed by more recent and advanced results. In particular, the diagonalization method is discussed in detail. Although it is a classical technique in computational complexity, it is the only method that was able to separate strong complexity classes so far. Some of the most important results in computational complexity theory have been proven by diagonalization. In particular, Hartmanis and Stearns [54, 104] proved that, given more resources, one can solve more computational problems. These results are known as hierarchy theorems. We present a generalization of the deterministic hierarchy theorems, recovering the classical results proved by Hartmanis and Stearns as corollaries. This is the first time that such unified treatment is presented in the literatureMestradoTeoria da ComputaçãoMestre em Ciência da Computaçã

Quantum meets optimization and machine learning

With the advent of the quantum era, what role the quantum computer will play in optimization and machine learning becomes a natural and salient question. The development of novel quantum computing techniques is essential to showcase the quantum advantage in these fields. At the same time, new findings in classical optimization and machine learning algorithms also have the potential to stimulate quantum computing research. In the dissertation, we explore the fascinating connections between quantum computing, optimization, and machine learning, paving the way for transformative advances in all three fields. First, on the quantum side, we present efficient quantum algorithms for fundamental problems in sampling, optimization, and quantum physics. Our results highlight the practical advantages of quantum computing in these fields. In addition, we introduce new approaches to quantum complexity theory for characterizing the quantum hardness of optimization and machine learning problems. Second, on the optimization side, we improve the efficiency of the state-of-the-art classical algorithms for solving semi-definite programming (SDP), Fourier sensing, dynamic distance estimation, etc. Our classical results are closely intertwined with quantum computing. Some of them serve as stepping stones to new quantum algorithms, while others are motivated by quantum applications or inspired by quantum techniques. Third, on the machine learning side, we develop fast classical and quantum algorithms for training over-parameterized neural networks with provable guarantees of convergence and generalization. Furthermore, we contribute to the security aspect of machine learning by theoretically investigating some potential approaches to (classically) protect private data in collaborative machine learning and to (quantumly) protect the copyright of machine learning models. Fourth, we investigate the concentration and discrepancy properties of hyperbolic polynomials and higher-order random walks, which could potentially be applied to quantum computing, optimization, and machine learning.Computer Science