QMA with subset state witnesses

The class QMA plays a fundamental role in quantum complexity theory and it has found surprising connections to condensed matter physics and in particular in the study of the minimum energy of quantum systems. In this paper, we further investigate the class QMA and its related class QCMA by asking what makes quantum witnesses potentially more powerful than classical ones. We provide a definition of a new class, SQMA, where we restrict the possible quantum witnesses to the "simpler" subset states, i.e. a uniform superposition over the elements of a subset of n-bit strings. Surprisingly, we prove that this class is equal to QMA, hence providing a new characterisation of the class QMA. We also prove the analogous result for QMA(2) and describe a new complete problem for QMA and a stronger lower bound for the class QMA$_1$

Quantum proof systems and entanglement theory

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2009.Includes bibliographical references (p. 99-106).Quantum complexity theory is important from the point of view of not only theory of computation but also quantum information theory. In particular, quantum multi-prover interactive proof systems are defined based on complexity theory notions, while their characterization can be formulated using LOCC operations. On the other hand, the main resource in quantum information theory is entanglement, which can be considered as a monotonic decreasing quantity under LOCC maps. Indeed, any result in quantum proof systems can be translated to entanglement theory, and vice versa. In this thesis I mostly focus on quantum Merlin-Arthur games as a proof system in quantum complexity theory. I present a new complete problem for the complexity class QMA. I also show that computing both the Holevo capacity and the minimum output entropy of quantum channels are NP-hard. Then I move to the multiple-Merlin-Arthur games and show that assuming some additivity conjecture for entanglement of formation, we can amplify the gap in QMA(2) protocols. Based on the same assumption, I show that the QMA(k)-hierarchy collapses to QMA(2). I also prove that QMAlog(2), which is defined the same as QMA(2) except that the size of witnesses is logarithmic, with the gap n-(3+e) contains NP. Finally, motivated by the previous results, I show that the positive partial transpose test gives no bound on the trace distance of a given bipartite state from the set of separable states.by Salman Abolfathe Beikidezfuli.Ph.D

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