QIP (complexity)

Last updated

In computational complexity theory, the class QIP (which stands for Quantum Interactive Polynomial time) is the quantum computing analogue of the classical complexity class IP, which is the set of problems solvable by an interactive proof system with a polynomial-time verifier and one computationally unbounded prover. Informally, IP is the set of languages for which a computationally unbounded prover can convince a polynomial-time verifier to accept when the input is in the language (with high probability) and cannot convince the verifier to accept when the input is not in the language (again, with high probability). In other words, the prover and verifier may interact for polynomially many rounds, and if the input is in the language the verifier should accept with probability greater than 2/3, and if the input is not in the language, the verifier should be reject with probability greater than 2/3. In IP, the verifier is like a BPP machine. In QIP, the communication between the prover and verifier is quantum, and the verifier can perform quantum computation. In this case the verifier is like a BQP machine.

By restricting the number of messages used in the protocol to at most k, we get the complexity class QIP(k). QIP and QIP(k) were introduced by John Watrous, [1] who along with Kitaev proved in a later paper [2] that QIP = QIP(3), which shows that 3 messages are sufficient to simulate a polynomial-round quantum interactive protocol. Since QIP(3) is already QIP, this leaves 4 possibly different classes: QIP(0), which is BQP, QIP(1), which is QMA, QIP(2) and QIP.

Kitaev and Watrous also showed that QIP is contained in EXP, the class of problems solvable by a deterministic Turing machine in exponential time. [2] QIP(2) was then shown to be contained in PSPACE, the set of problems solvable by a deterministic Turing machine in polynomial space. [3] Both results were subsumed by the 2009 result that QIP is contained in PSPACE, [4] which also proves that QIP = IP = PSPACE, since PSPACE is easily shown to be in QIP using the result IP = PSPACE.

Related Research Articles

In computational complexity theory, bounded-error probabilistic polynomial time (BPP) is the class of decision problems solvable by a probabilistic Turing machine in polynomial time with an error probability bounded away from 1/3 for all instances. BPP is one of the largest practical classes of problems, meaning most problems of interest in BPP have efficient probabilistic algorithms that can be run quickly on real modern machines. BPP also contains P, the class of problems solvable in polynomial time with a deterministic machine, since a deterministic machine is a special case of a probabilistic machine.

In computational complexity theory, bounded-error quantum polynomial time (BQP) is the class of decision problems solvable by a quantum computer in polynomial time, with an error probability of at most 1/3 for all instances. It is the quantum analogue to the complexity class BPP.

Computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm.

In computational complexity theory, PSPACE is the set of all decision problems that can be solved by a Turing machine using a polynomial amount of space.

In computational complexity theory, EXPSPACE is the set of all decision problems solvable by a deterministic Turing machine in exponential space, i.e., in space, where is a polynomial function of . Some authors restrict to be a linear function, but most authors instead call the resulting class ESPACE. If we use a nondeterministic machine instead, we get the class NEXPSPACE, which is equal to EXPSPACE by Savitch's theorem.

Interactive proof system

In computational complexity theory, an interactive proof system is an abstract machine that models computation as the exchange of messages between two parties: a prover and a verifier. The parties interact by exchanging messages in order to ascertain whether a given string belongs to a language or not. The prover possesses unlimited computational resources but cannot be trusted, while the verifier has bounded computation power but is assumed to be always honest. Messages are sent between the verifier and prover until the verifier has an answer to the problem and has "convinced" itself that it is correct.

Complexity class Set of problems in computational complexity theory

In computational complexity theory, a complexity class is a set of computational problems of related resource-based complexity. The two most commonly analyzed resources are time and memory.

In computational complexity theory, the complexity class PH is the union of all complexity classes in the polynomial hierarchy:

PP (complexity) Class of problems in computer science

In complexity theory, PP is the class of decision problems solvable by a probabilistic Turing machine in polynomial time, with an error probability of less than 1/2 for all instances. The abbreviation PP refers to probabilistic polynomial time. The complexity class was defined by Gill in 1977.

In computational complexity theory, an Arthur–Merlin protocol, introduced by Babai (1985), is an interactive proof system in which the verifier's coin tosses are constrained to be public. Goldwasser & Sipser (1986) proved that all (formal) languages with interactive proofs of arbitrary length with private coins also have interactive proofs with public coins.

In computational complexity theory, the complexity class NEXPTIME is the set of decision problems that can be solved by a non-deterministic Turing machine using time .

In computational complexity theory, P/poly is the complexity class of languages recognized by a polynomial-time Turing machine with a polynomial-bounded advice function. It is also equivalently defined as the class PSIZE of languages that have polynomial-size circuit families. This means that the machine that recognizes a language may use a different advice function or use a different circuit depending on the length of the input, and that the advice function or circuit will vary only on the size of the input.

IP (complexity)

In computational complexity theory, the class IP is the class of problems solvable by an interactive proof system. It is equal to the class PSPACE. The result was established in a series of papers: the first by Lund, Karloff, Fortnow, and Nisan showed that co-NP had multiple prover interactive proofs; and the second, by Shamir, employed their technique to establish that IP=PSPACE. The result is a famous example where the proof does not relativize.

In computational complexity theory, PostBQP is a complexity class consisting of all of the computational problems solvable in polynomial time on a quantum Turing machine with postselection and bounded error.

Circuit complexity model of computational complexity

In theoretical computer science, circuit complexity is a branch of computational complexity theory in which Boolean functions are classified according to the size or depth of the Boolean circuits that compute them. A related notion is the circuit complexity of a recursive language that is decided by a uniform family of circuits .

ACC<sup>0</sup>

ACC0, sometimes called ACC, is a class of computational models and problems defined in circuit complexity, a field of theoretical computer science. The class is defined by augmenting the class AC0 of constant-depth "alternating circuits" with the ability to count; the acronym ACC stands for "AC with counters". Specifically, a problem belongs to ACC0 if it can be solved by polynomial-size, constant-depth circuits of unbounded fan-in gates, including gates that count modulo a fixed integer. ACC0 corresponds to computation in any solvable monoid. The class is very well studied in theoretical computer science because of the algebraic connections and because it is one of the largest concrete computational models for which computational impossibility results, so-called circuit lower bounds, can be proved.

In computational complexity theory, QMA, which stands for Quantum Merlin Arthur, is the set of languages for which, when the answer is YES, there is a polynomial-size quantum proof that convinces a polynomial time quantum verifier of this fact with high probability. Moreover, when the answer is NO, every polynomial-size quantum state is rejected by the verifier with high probability.

Quantum complexity theory is the subfield of computational complexity theory that deals with complexity classes defined using quantum computers, a computational model based on quantum mechanics. It studies the hardness of computational problems in relation to these complexity classes, as well as the relationship between quantum complexity classes and classical complexity classes.

John Watrous (computer scientist)

John Harrison Watrous is a professor of computer science at the David R. Cheriton School of Computer Science at the University of Waterloo, a member of the Institute for Quantum Computing, an affiliate member of the Perimeter Institute for Theoretical Physics and a Fellow of the Canadian Institute for Advanced Research. He was a faculty member in the Department of Computer Science at the University of Calgary from 2002 to 2006 where he held a Canada Research Chair in quantum computing.

Quantum refereed game in quantum information processing is a class of games in the general theory of quantum games. It is played between two players, Alice and Bob, and arbitrated by a referee. The referee outputs the pay-off for the players after interacting with them for a fixed number of rounds, while exchanging quantum information.

References

  1. Watrous, John (2003), "PSPACE has constant-round quantum interactive proof systems", Theor. Comput. Sci., Essex, UK: Elsevier Science Publishers Ltd., 292 (3): 575–588, doi: 10.1016/S0304-3975(01)00375-9 , ISSN   0304-3975
  2. 1 2 Kitaev, Alexei; Watrous, John (2000), "Parallelization, amplification, and exponential time simulation of quantum interactive proof systems", STOC '00: Proceedings of the thirty-second annual ACM symposium on Theory of computing, ACM, pp. 608–617, ISBN   978-1-58113-184-0
  3. Jain, Rahul; Upadhyay, Sarvagya; Watrous, John (2009), "Two-Message Quantum Interactive Proofs Are in PSPACE", FOCS '09: Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science, IEEE Computer Society, pp. 534–543, ISBN   978-0-7695-3850-1
  4. Jain, Rahul; Ji, Zhengfeng; Upadhyay, Sarvagya; Watrous, John (2010), "QIP = PSPACE", STOC '10: Proceedings of the 42nd ACM symposium on Theory of computing, ACM, pp. 573–582, ISBN   978-1-4503-0050-6