Two-party adversarial interactions are of fundamental importance in the study of cryptography and machine learning, as they allow us to work in dynamic environments to model and adapt to various challenges, fostering reliability and robustness. In this thesis, we first consider such interactions in the domain of information-theoretic cryptography, where we propose new protocols and security bounds involving various two-party primitives such as coin flipping, bit commitment, and variants of oblivious transfer. We begin by developing the mathematical framework of stochastic selection, which is potentially useful for devising new protocols with improved security for a combination of various tasks. We then use this idea to develop the first protocols for Rabin oblivious transfer that offer partial information-theoretic security and also provide a non-trivial lower bound on its security. We also relax some of the more commonly used assumptions to consider the security of protocols in broader compositions and independently, allowing for devices that may be potentially malicious. In the former setting, we demonstrate that the weak coin flipping task -- which enjoys almost perfect standalone security -- may not be immune to attacks when used in larger compositions. In the latter, we establish the impossibility of determining the inner workings of a shared quantum mechanical device -- a phenomenon commonly known as self-testing -- in the presence of an adversary. Finally, we consider a slightly different task in a similar setting, where we discuss the classical online learnability of various quantum objects. We prove a sublinear regret bound for learning over general subsets of positive semidefinite matrices via the regularized follow-the-leader algorithm and apply it to various settings involving the learning of quantum objects. For concrete applications, we present a sublinear regret bound for learning quantum states, effects, channels, interactive measurements, strategies, co-strategies, and collections of inner products of pure states. In proving our regret bound, we establish various matrix analysis results useful in quantum information theory, including a generalization of Pinsker's inequality for arbitrary positive semidefinite operators with possibly different traces, which may be of independent interest in the study of more general classes of divergences.
In the last two decades, there has been much effort in finding secure protocols for two-party cryptographic tasks. It has since been discovered that even with quantum mechanics, many such protocols are limited in their security promises. In this work, we use stochastic selection, an idea from stochastic programming, to circumvent such limitations. For example, we find a way to switch between bit commitment, weak coin flipping, and oblivious transfer protocols to improve their security. We also use stochastic selection to turn trash into treasure yielding the first quantum protocol for Rabin oblivious transfer.
Online learning is a framework for interfacing with environments whose structure is revealed through interactions; quantum information theory is a means of representing data and its processing consistent with modern understanding of physics. Their union is symbiotic---sequential processing is light on precious quantum resources, and quantum algorithms can surpass known bounds of classical algorithms---their combination being an area of independent interest. This thesis aims to exemplify the above point via five case studies on tomography, change detection, and function evaluation. In the process of deriving efficient tomography procedures, prior work has shown that quantum states can be learned in online adversarial environments. We extend this notion to subsets of positive semidefinite operators, including general quantum objects such as states, measurements, channels, strategies, co-strategies, and Gram matrices. We tailor a regularized follow-the-leader algorithm and achieve sublinear regret. As a byproduct, we show a generalization of Pinsker's inequality that accounts for differences in trace, its proof method more broadly applicable to general divergences. We further extend online learnability to objects in arbitrary physical theories. We derive mappings between Euclidean Jordan algebras and generalized probabilistic theories, using methods for the former to show that states in the latter are learnable. Specifically, we develop a projective version of the symmetric cone multiplicative weights update algorithm that achieves sublinear regret when learning states of generalized probabilistic theories. We consider the task of detecting changes in sequences of unknown quantum states with a constraint of no false positives, developing efficient and optimal online quantum algorithms. These include online algorithms with small amounts of quantum memory based on the swap test, and optimal algorithms projecting onto the symmetric subspace. We find circuit constructions for these algorithms, analyze their complexity, and show their equivalence under certain memory limitations. The proposed algorithms are applicable to quality control and anomaly detection in quantum devices, and may be of independent interest for identity testing and optimal cloning. We elaborate on the quality control aspect of changepoint methods by developing procedures for aiding the calibration of near-term quantum devices. These consist of algorithms for certification and changepoint detection in Hamiltonian dynamics, which enable continuous monitoring and trigger recalibration procedures. Using a cumulative sum procedure, we attain an asymptotically-optimal scaling of the average times to detection versus false positive, dependent primarily on Hamiltonian norm bounds. Lastly, we quantify the effect of memory limits on Boolean function evaluation of quantum sequences---a task applicable to distributed sensing and quantum reading of classical memories, among others. We show that minimum-error function evaluation in the memoryless regime is equivalent to the so-called pretty good measurement and hence shares its performance guarantee. Additionally, we characterize the precise set of functions on which this strategy performs optimally---affine functions. The above studies reveal insights about learnability as a universal property and the tradeoffs between performance and resource requirements in online settings, informing the choice of quantum settings best-suited for online learning. We conclude by summarizing these takeaways, along with listing remaining open questions.
This thesis concerns the analysis of the unconditional security of quantum cryptographic protocols using convex optimization techniques. It is divided into the study of coin-flipping and oblivious transfer. We first examine a family of coin-flipping protocols. Almost all of the handful of explicitly described coin-flipping protocols are based on bit-commitment. To explore the possibility of finding explicit optimal or near-optimal protocols, we focus on a class which generalizes such protocols. We call these
BCCF-protocols, for bit-commitment based coin-flipping. We use the semidefinite programming (SDP) formulation of cheating strategies along the lines of Kitaev to analyze the structure of the protocols. \n \nIn the first part of the thesis, we show how these semidefinite programs can be used to simplify the analysis of the protocol. In particular, we show that a particular set of cheating strategies contains an optimal strategy. This reduces the problem to optimizing a linear combination of fidelity functions over a polytope which has several benefits. First, it allows one to model cheating probabilities using a simpler class of optimization problems known as second-order cone programs (SOCPs). Second, it helps with the construction of point games due to Kitaev as described in Mochon's work. \nPoint games were developed to give a new perspective for studying quantum protocols. In some sense, the notion of point games is dual to the notion of protocols. \n \nThere has been increased research activity in optimization concerning generalizing theory and algorithms for linear programming to much wider classes of optimization problems such as semidefinite programming. For example, semidefinite programming provides a tool for potentially improving results based on linear programming or investigating old problems that have eluded analysis by linear programming. In this sense, the history of semidefinite programming is very similar to the history of quantum computation. \nQuantum computing gives a generalized model of computation to tackle new and old problems, improving on and generalizing older classical techniques. Indeed, there are striking differences between linear programming and semidefinite programming as there are between classical and quantum computation. In this thesis, we strengthen this analogy by studying a family of classical coin-flipping protocols based on classical bit-commitment. Cheating strategies for these ``classical
BCCF-protocols'' can be formulated as linear programs (LPs) which are closely related to the semidefinite programs for the quantum version. In fact, we can construct point games for the classical protocols as well using the analysis for the quantum case. \n \nUsing point games, we prove that every classical
BCCF-protocol allows exactly one of the parties to entirely determine the outcome. Also, we rederive Kitaev's lower bound to show that only ``classical'' protocols can saturate Kitaev's analysis. Moreover, if the product of Alice and Bob's optimal cheating probabilities is
1/2, then at least one party can cheat with probability
1. \n \nThe second part concerns the design of an algorithm to search for
BCCF-protocols with small bias. Most coin-flipping protocols with more than three rounds have eluded direct analysis. To better understand the properties of optimal
BCCF-protocols with four or more rounds, we turn to computational experiments. We design a computational optimization approach to search for the best protocol based on the semidefinite programming formulations of cheating strategies. We create a protocol filter using cheating strategies, some of which build upon known strategies and others are based on convex optimization and linear algebra. The protocol filter efficiently eliminates candidate protocols with too high a bias. Using this protocol filter and symmetry arguments, we perform searches in a matter of days that would have otherwise taken millions of years. Our experiments checked
1016 four and six-round
BCCF-protocols and suggest that the optimal bias is
1/4. \n \nThe third part examines the relationship between oblivious transfer, bit-commitment, and coin-flipping. We consider oblivious transfer which succeeds with probability
1 when the two parties are honest and construct a simple protocol with security provably better than any classical protocol. We also derive a lower bound by constructing a bit-commitment protocol from an oblivious transfer protocol. Known lower bounds for bit-commitment then lead to a constant lower bound on the bias of oblivious transfer. Finally, we show that it is possible to use Kitaev's semidefinite programming formulation of cheating strategies to obtain optimal lower bounds on a ``forcing'' variant of oblivious transfer related to coin-flipping.
Abstract In many quantum tasks, there is an unknown quantum object that one wishes to learn. An online strategy for this task involves adaptively refining a hypothesis to reproduce such an object or its measurement statistics. A common evaluation metric for such a strategy is its regret, or roughly the accumulated errors in hypothesis statistics. We prove a sublinear regret bound for learning over general subsets of positive semidefinite matrices via the regularized-follow-the-leader algorithm and apply it to various settings where one wishes to learn quantum objects. For concrete applications, we present a sublinear regret bound for learning quantum states, effects, channels, interactive measurements, strategies, co-strategies, and the collection of inner products of pure states. Our bound applies to many other quantum objects with compact, convex representations. In proving our regret bound, we establish various matrix analysis results useful in quantum information theory. This includes a generalization of Pinsker’s inequality for arbitrary positive semidefinite operators with possibly different traces, which may be of independent interest and applicable to more general classes of divergences.
Oblivious transfer is a fundamental primitive in cryptography. While perfect information theoretic security is impossible, quantum oblivious transfer protocols can limit the dishonest players' cheating. Finding the optimal security parameters in such protocols is an important open question. In this paper we show that every 1-out-of-2 oblivious transfer protocol allows a dishonest party to cheat with probability bounded below by a constant strictly larger than 1/2. Alice's cheating is defined as her probability of guessing Bob's index, and Bob's cheating is defined as his probability of guessing both input bits of Alice. In our proof, we relate these cheating probabilities to the cheating probabilities of a coin flipping protocol and conclude by using Kitaev's coin flipping lower bound. Then, we present an oblivious transfer protocol with two messages and cheating probabilities at most 3/4. Last, we extend Kitaev's semidefinite programming formulation to more general primitives, where the security is against a dishonest player trying to force the outcome of the other player, and prove optimal lower and upper bounds for them.
Oblivious transfer is a fundamental primitive in cryptography. While perfect information theoretic security is impossible, quantum oblivious transfer protocols can limit the dishonest players' cheating. Finding the optimal security parameters in such protocols is an important open question. In this paper we show that every 1-out-of-2 oblivious transfer protocol allows a dishonest party to cheat with probability bounded below by a constant strictly larger than
1/2. Alice's cheating is defined as her probability of guessing Bob's index, and Bob's cheating is defined as his probability of guessing both input bits of Alice. In our proof, we relate these cheating probabilities to the cheating probabilities of a coin flipping protocol and conclude by using Kitaev's coin flipping lower bound. Then, we present an oblivious transfer protocol with two messages and cheating probabilities at most
3/4. Last, we extend Kitaev's semidefinite programming formulation to more general primitives, where the security is against a dishonest player trying to force the outcome of the other player, and prove optimal lower and upper bounds for them.