Witness-preserving Amplification of QMA
|
|
- Josephine Ferguson
- 5 years ago
- Views:
Transcription
1 Witness-preserving Amplification of QMA Yassine Hamoudi December 30, 2015 Contents 1 Introduction QMA and the amplification problem Prior works Some facts about projectors Jordan s lemma Alternative formulation of QMA The witness-preserving amplification from [MW05] Description of the algorithm Analysis Comments and perspectives 8 5 Conclusion 9 References 9 1 Introduction One of the great questions of quantum computation is to understand how the quantum framework could speed-up classical algorithms. One way to tackle this problem is to define quantum analogues for the classical complexity classes (NP, BPP, IP,... ), and then study how they are related to each other. In this report, we will be interested in the quantum class QMA (Quantum Merlin Arthur), an analogue of NP and MA. This class characterizes the languages that can be recognize in polynomial time by quantum randomized circuits using quantum polynomial size witnesses. Among the famous results associated to MA and BPP is how to decrease the error probability of randomized circuits. Some attempts have been done to transpose this result in the quantum setting. Here we are going to present the amplification algorithm from [MW05] that achieves this task for QMA. In the following of this section, we will describe more formally the class QMA, the related amplification problem and some prior works. Then, some tools about projectors will be presented in section 2. This will enable us to detail in section 3 1
2 a simplified proof of the amplification scheme from [MW05]. Finally, a more recent result, from [NWZ09], will be compared to the last one in section QMA and the amplification problem A family of circuits {V x : x {0, 1} } is uniformly generated if there exists a polynomial time algorithm that given x (in binary) generates V x. This notion is more convenient than Turing machines to define quantum analogues of classical classes. We especially use it for MA and QMA. Definition 1. The (classical) class MA(a, b) consists of all languages L for which there exists a uniformly generated family of classical, randomized, poly-size circuits {V x : x {0, 1} }, and a polynomial m such that: 1. (Completeness) If x L then there exists a witness w of size m( x ) such that P(V x accepts w) a. 2. (Soundness) If x / L then for all witness w of size m( x ), we have P(V x accepts w) b. NP is obtained by removing the randomness in the previous definition, and BPP the witnesses. We also refer to MA(2/3, 1/3) as MA. Finally, QMA is defined using quantum circuits and witnesses: Definition 2. The (quantum) class QMA(a, b) consists of all languages L for which there exists a uniformly generated family of quantum, randomized, poly-size circuits {V x : x {0, 1} }, and two polynomials m, k such that V x has m( x ) input qubits, k( x ) ancillae (auxiliary qubits) and: 1. (Completeness) If x L then there exists a witness φ of size m( x ) such that P(V x accepts φ 0 k( x ) ) a. 2. (Soundness) If x / L then for all witness φ of size m( x ), we have P(V x accepts φ 0 k( x ) ) b. We will also call witnesses the tensor products ψ = φ 0 k( x ), and QMA will refer to QMA(2/3, 1/3). Finally, the quantum analogue BQP of BPP is obtained by removing the witnesses in the previous definition. Figure 1: A QMA verifier V. Note that the output qubit is on the first row. It is interesting to reduce the completeness and soundness errors in the definitions of MA and QMA, both for practical purposes and motivating the conventions MA = MA(2/3, 1/3) and QMA = QMA(2/3, 1/3). The following well-known theorem already provides an exponential decrease for MA: 2
3 Theorem 3. Let a, b and q three polynomials such that a(n) b(n) 1/q(n). Then, for all polynomial r and language L MA(a, b) we have L MA ( 1 2 r( x ), 2 r( x )). The proof is quite obvious. We just need to apply the original verifier polynomially many times, and then take the majority result. Standard Chernoff bound provides the new error term. The quantum version of this problem can be stated as follow: Problem 1. Given a polynomial r and a language L QMA(a, b) recognized by the family of circuits {V x : x {0, 1} }, is it possible to define a new family {V x : x {0, 1} } that recognizes L with completeness probability 1 2 r( x ), and soundness probability 2 r( x ) (i.e. L QMA ( 1 2 r( x ), 2 r( x )) )? Our goal in the following is to present algorithms for solving Problem 1. We will focus on a single quantum randomized poly-size circuit V with completeness probability a, and soundness probability b. The amplification will provide new circuits V that decrease the error of V. These results can then be applied to a whole family {V x : x {0, 1} }, thus solving the previous problem. 1.2 Prior works The complexity class QMA seems to be defined first in [Kit99] and [Wat00]. Various results concerning it have been proved in [KSV02], such as the completeness of the Local Hamiltonian Problem and the first amplification result solving Problem 1: Theorem 4 ([KSV02]). The amplification problem for ( circuit ) V (defined as in Problem 1) can be solved with a circuit V which uses O r copies of the original (a b) ( ) 2 witness, and is made of O r copies of V. (a b) 2 Proof. The idea is to mimic the classical amplification proof, by applying the original verifier several ( times ) and then output the majority result. This needs to run independently O r copies of V, and feed each of them with a new copy of the (a b) 2 original witness. In the case that x L, there exists a witness ψ such that P(V accepts ψ 0 k( x ) ) 2/3. We then give ψ ψ ψ as input to the new verifier V, which will accept with probability greater than 1 2 r( x ) (standard Chernoff bound). If x / L, the same reasoning applies to states of the form ψ ψ ψ, which will be rejected with probability greater than 1 2 r( x ).The only difficulty is to consider witnesses which are highly entangled states (i.e. not factorable into ψ ψ ψ ). This however is proved not to change the soundness probability. The main drawback of previous amplification is the need of multiple identical witnesses, which increases the size of the quantum states that are manipulated. This issue was solved later in two other amplification procedures for QMA. The first one, which will be described in section 3, makes use of only one copy of the original witness. The second one also lowers the number of copies of V needed in V. We will present it briefly in section 4. 3
4 2 Some facts about projectors A projector Π is a Hermitian matrix satisfying Π 2 = Π. In particular, the eigenvalues of Π are 0 or 1, and it projects on the eigenspace corresponding to the eigenvalue 1. Projectors are a convenient way of describing subspaces. Here we study the interaction of two projectors, and how it can be used to reformulate the QMA framework. 2.1 Jordan s lemma Given two projectors Π 1 and Π 2 defined over the Hilbert space H, the Jordan s lemma provides a nice way to decompose H. Lemma 5 ([Jor75]). For any two projectors Π 1 and Π 2, there exists an orthogonal decomposition of the Hilbert space H into: 1. one-dimensional subspaces T j, on which Π 1 and Π 2 are identities or zero-rank projectors. 2. two-dimensional subspaces S i invariant under Π 1 and Π 2, on which Π 1 and Π 2 are rank-one projectors (i.e. inside each S i there are two unit vectors v i and w i such that Π 1 projects on v i and Π 2 projects on w i ). Proof. An easy proof of this lemma can be found in [Reg06]. We are particularly interested in the two-dimensional subspaces S i (we will get rid of the T j in the following). According to Jordan s lemma, Π 1 is the projector v i v i inside S i, and Π 2 is the projector w i w i. If we set carefully the phases of our vectors, we can obtain two unit vectors v i and w i in S i, orthogonal to v i and w i respectively, such that the configuration of Figure 2 happens. Note that v i w i 0, θ i = arccos( v i w i ) [0, π/2] and p i = cos 2 θ i = v i w i 2. Figure 2: Π 1 and Π 2 inside the two-dimensional subspace S i. We especially obtain four useful relations, for each subspace S i : v i = p i w i + 1 p i w i (1) w i = p i v i + 1 p i v i (2) 4
5 v i = 1 p i w i p i w i (3) 2.2 Alternative formulation of QMA w i = 1 p i v i p i v i (4) We now consider a verifier circuit V acting on m qubits and k ancillae. We define the following related projectors: Π 0 = 0 0 I m+k 1 0 = 1 1 I m+k 1 Π 1 = I m 0 k 0 k 1 = I m+k Π 1 Π 2 = V 0 V {Π 0, 0 } describes the measurement of the first qubit, whereas the measurement associated to {Π 1, 1 } returns 1 if and only the k auxiliary qubits are equal to 0 (i.e. they are set to their initial state). We can use these projectors to define the acceptance probability of V. Lemma 6. The maximum acceptance probability of verifier V is the largest eigenvalue of Π 1 Π 2 Π 1. Proof. If state ψ has auxiliary qubits orthogonal to 0 k, then Π 1 ψ = 0, and ψ Π 1 Π 2 Π 1 ψ = 0. Thus, we only have to consider valid input states ψ = φ 0 k, where φ is a witness. For such a state, we have: ψ Π 1 Π 2 Π 1 ψ = ψ V 0 V ψ = 0 V ψ 2 These quantity is both the eigenvalue associated to ψ and the acceptance probability of φ. Thus, the maximum acceptance probability of V is the largest eigenvalue of Π 1 Π 2 Π 1. This result proves that QMA is in fact a largest eigenvalue problem. Note that the Local Hamiltonian Problem, which is complete for the class QMA ([KKR06]), consists in deciding extremal bounds on the smallest eigenvalue of a certain Hamiltonian. Furthermore, the Jordan s lemma allows us to characterize more precisely the maximum acceptance probability. Lemma 7. Using the notations introduced in the Jordan s lemma applied to the previous projectors Π 1 and Π 2, we have: Π 1 Π 2 Π 1 = p i v i v i i=1,2,... In particular, the maximum acceptance probability of V (or similarly its largest eigenvalue) is max i p i. 5
6 Proof. We will assume in the following that the acceptance probability of V for any input is strictly between 0 and 1 (if this is not the case, toss a three-sided dice before running V and either accept, reject, or run V depending on the result). In each one-dimensional subspace T j, if Π 1 is not zero then it is a rank-one projector. So there exists u i in Π 1 (i.e. a legal witness, with auxiliary qubits set to 0) such that Π 1 u i = u i. Then, depending on whether Π 2 is 0 or 1 in T j, we obtain either v i Π 1 Π 2 Π 1 v i = 0 or v i Π 1 Π 2 Π 1 v i = 1. Consequently, v i is accepted with probability 0 or 1, which contradicts the previous assumption. Thus, Π 1 must be zero in each T j, and Π 1 is spanned only by v 1, v 2,.... Moreover, in each S i we have Π 1 Π 2 Π 1 = v i v i w i w i v i v i = v i w i 2 v i v i = p i v i v i. Bringing all the S i together we obtain Π 1 Π 2 Π 1 = p i v i v i. i=1,2,... 3 The witness-preserving amplification from [MW05] C. Marriott and J. Watrous improved the first amplification result from Theorem 4, by removing the need of multiple copies of the original witness. Here is their main result we will detail in the following: Theorem 8 ([MW05]). The amplification problem for circuit V (defined as in Problem( 1) can ) be solved with a circuit V which uses only the original witness and O r copies of V. (a b) Description of the algorithm We consider a verifier V acting on m qubits and k ancillae. We define a new circuit V with same inputs as V, which repeats N times the following two steps: 1. apply V, measure if the output qubit is 1, apply V 2. measure if all auxiliary qubits are 0. We then write down the 2N measurement results and count the number of equal consecutive values. For instance, if the measurements are YYYNYNYYNN then it is 4. Finally, if the count is greater than N a+b 2 then V accepts, otherwise it rejects. Figure 3 illustrates the circuit V (the S box performs the previous counting). Figure 3: The amplified verifier V from [MW05]. 6
7 Thus, verifier V measures alternatively in {Π 2, I Π 2 } and {Π 1, 1 }. Its new completeness and soundness probabilities depend on the number N of steps performed. We set this parameter in the next section. 3.2 Analysis We first look at the computation of V on an input vector v i (using notations of section 2.2). According to Equation 1, we have v i = p i w i + 1 p i wi. However, the first measurement is {Π 2, I Π 2 }, where Π 2 = w i w i. Thus, we obtain w i (i.e. Y) with probability p i, and wi (i.e. N) with probability 1 p i. Then, depending on the state we obtained, measurement {Π 1, 1 } will provide v i or vi with probability p i or 1 p i. In fact, the whole process is a random walk on the Markov chain of Figure 4. Y, Y, Y, N, N, N, Y, Y, Y, N, N, N, Figure 4: Transition probabilities for V on input v i or v i. Starting at any point of this chain, the probability that the next two steps provide equal values (i.e. YY or NN) is p 2 i + p i(1 p i ) = p i. The probability of a given sequence of results is thus p j i (1 p i) N j, where j is the number of equal consecutive values in the sequence. Consequently, the probability of obtaining exactly j equal consecutive values is ( N j ) p j i (1 p i) N j, and V accepts v i with probability: N j=n a+b 2 ( ) N p j i j (1 p i) N j (5) Finally, we obtain the parameter N that makes amplification work, and concludes the proof of Theorem 8: Lemma ( ) 9 ([MW05]). If the two steps of previous algorithm are repeated N = O r times, then V achieves the amplified bounds of Problem 1. (a b) 2 Proof. First, let us assume that x L (i.e. there is a witness ψ such that P(V accepts ψ ) a). According to Lemma 6, this means that the largest eigenvalue of Π 1 Π 2 Π 1 is greater than a. However, the eigenvalues of Π 1 Π 2 Π 1 are exactly the p i s (Lemma 7). Thus, there exists i such that p i a. Using standard Chernoff bound on Equation 5, we conclude that V accepts v i with probability greater than 1 2 r. If x / L, all the p i s are less than b (according to Lemmas 6 and 7). The same reasoning as previously shows the v i s are accepted by V with probability less than 7
8 2 r. Finally, any witness ψ is a linear combination of the v i s, and it is easy to prove that it satisfies the new soundness property 4 Comments and perspectives Theorem 8 removes the multiple witnesses needed in Theorem 4, thus bringing the QMA amplification more practical. For instance, this leads to an analogue of the classical equality MA log = BPP, namely QMA log = BQP (where MA log and QMA log use logarithmic size witnesses only). This result, that easily stems from Theorem 8 (see [Reg06] for instance), could not be obtained with Theorem 4 since it requires to reduce the errors without polynomially increasing the size of the witness. A more recent amplification result, from [NWZ09], is also quadratically faster than Theorem 8: Theorem 10 ([NWZ09]). The amplification problem for circuit V (defined as in Problem ( ) 1) can be solved with a circuit V which uses only the original witness and O r a b copies of V. Proof. The algorithm builds upon [MW05], using the same projectors Π 1 and Π 2. It also considers the reflections about their supports: F 1 = 2Π 1 I F 2 = 2Π 2 I (6) Using the Jordan s lemma, the authors prove that in each two dimensional subspace S i, the product F 1 F 2 is a rotation by an angle ϕ i. Moreover, ϕ i is small if and only if the acceptance probability of original verifier V is high. Thus, the amplification becomes a phase estimation problem. Finally, the procedure is repeated several times to decrease the phase estimation error. There exist even stronger results in the classical case. For instance, it was proved in [ZF87] that the class MA 1, which requires perfect completeness (i.e. if x L then V x accepts a witness with probability 1), was in fact equal to MA. We do not know whether QMA 1 = QMA also. However, the work carried out in [NWZ09] provides QMA 1 = QMA ϕ for a subclass QMA ϕ of QMA. Finally, generalizing the definition of QMA, by allowing Arthur and Merlin to exchanged k messages (instead of only one witness), leads to the definition of QIP(k). In particular, BQP = QIP(0) and QMA = QIP(1). More generally, the quantum analogue QIP (Quantum Interactive Proofs) of IP allows polynomial many rounds between Arthur and Merlin. However, a striking result from [KW00] proves that QIP = QIP(3), which demonstrates that more than three rounds is useless. This is one of the only differences between quantum and classical classes, since IP = IP(3) is highly unlikely (unless AM = PSPACE). Moreover, it was shown recently that QIP = PSPACE ([JJUW11]). Since IP = PSPACE too ([Sha92]), it implies that classical interactive proofs can simulate quantum ones! The most misunderstood class remains QIP(2). 8
9 5 Conclusion We detailed in this report some results concerning the quantum analogue QMA of MA. We especially described three amplification theorems, from [Wat00], [MW05] and [NWZ09], the later two having the advantage of being witness-preserving. Other classical properties, such as complete problems or perfect completeness, have also been mentioned in the quantum setting. All of these results illustrate the growing similarities between classical and quantum computation. Some existing relationships have been summarized Figure 5. BQP QMA QIP(2) QIP = IP = PSPACE BPP MA P NP Figure 5: Some inclusions between classical and quantum classes. However, many problems regarding P and NP have not been solved, or even transposed, in the quantum setting yet (e.g. the PCP theorem). It is also not clear that QMA is the right analogue of NP. Thus, other classes such as QCMA (quantum verifier but classical witness) could carry the analogy further. References [JJUW11] Rahul Jain, Zhengfeng Ji, Sarvagya Upadhyay, and John Watrous. QIP = PSPACE. J. ACM, 58(6):30:1 30:27, December [Jor75] [Kit99] [KKR06] [KSV02] [KW00] Camille Jordan. Essai sur la géométrie à n dimensions. Bulletin de la Société Mathématique de France, 3: , Alexei Kitaev. Quantum NP. Talk at AQIP 99: Second Workshop on Algorithms in Quantum Information Processing, DePaul University, January Julia Kempe, Alexei Kitaev, and Oded Regev. The complexity of the local Hamiltonian problem. SIAM J. Comput., 35(5): , May A. Yu. Kitaev, A. H. Shen, and M. N. Vyalyi. Classical and Quantum Computation. American Mathematical Society, Boston, MA, USA, Alexei Kitaev and John Watrous. Parallelization, amplification, and exponential time simulation of quantum interactive proof systems. In Proceedings of the Thirty-second Annual ACM Symposium on Theory of Computing, STOC 00, pages , New York, NY, USA, ACM. 9
10 [MW05] Chris Marriott and John Watrous. Quantum Arthur-Merlin games. Comput. Complex., 14(2): , June [NWZ09] Daniel Nagaj, Pawel Wocjan, and Yong Zhang. Fast amplification of QMA. Quantum Info. Comput., 9(11): , November [Reg06] Oded Regev. Witness-preserving amplification of QMA. University Lecture, fall_2005/ln/qma.pdf. [Sha92] Adi Shamir. IP = PSPACE. J. ACM, 39(4): , October [Wat00] [ZF87] J. Watrous. Succinct quantum proofs for properties of finite groups. In Proceedings of the 41st Annual Symposium on Foundations of Computer Science, FOCS 00, pages 537, Washington, DC, USA, IEEE Computer Society. Stathis Zachos and Martin Furer. Probabilistic quantifiers vs. distrustful adversaries. In Proc. Of the Seventh Conference on Foundations of Software Technology and Theoretical Computer Science, pages , London, UK, UK, Springer-Verlag. 10
By allowing randomization in the verification process, we obtain a class known as MA.
Lecture 2 Tel Aviv University, Spring 2006 Quantum Computation Witness-preserving Amplification of QMA Lecturer: Oded Regev Scribe: N. Aharon In the previous class, we have defined the class QMA, which
More informationLecture 26: QIP and QMIP
Quantum Computation (CMU 15-859BB, Fall 2015) Lecture 26: QIP and QMIP December 9, 2015 Lecturer: John Wright Scribe: Kumail Jaffer 1 Introduction Recall QMA and QMA(2), the quantum analogues of MA and
More informationA Complete Characterization of Unitary Quantum Space
A Complete Characterization of Unitary Quantum Space Bill Fefferman (QuICS, University of Maryland/NIST) Joint with Cedric Lin (QuICS) Based on arxiv:1604.01384 1. Basics Quantum space complexity Main
More informationQUANTUM ARTHUR MERLIN GAMES
comput. complex. 14 (2005), 122 152 1016-3328/05/020122 31 DOI 10.1007/s00037-005-0194-x c Birkhäuser Verlag, Basel 2005 computational complexity QUANTUM ARTHUR MERLIN GAMES Chris Marriott and John Watrous
More informationQuantum NP - Cont. Classical and Quantum Computation A.Yu Kitaev, A. Shen, M. N. Vyalyi 2002
Quantum NP - Cont. Classical and Quantum Computation A.Yu Kitaev, A. Shen, M. N. Vyalyi 2002 1 QMA - the quantum analog to MA (and NP). Definition 1 QMA. The complexity class QMA is the class of all languages
More informationarxiv:cs/ v1 [cs.cc] 15 Jun 2005
Quantum Arthur-Merlin Games arxiv:cs/0506068v1 [cs.cc] 15 Jun 2005 Chris Marriott John Watrous Department of Computer Science University of Calgary 2500 University Drive NW Calgary, Alberta, Canada T2N
More informationOn the complexity of stoquastic Hamiltonians
On the complexity of stoquastic Hamiltonians Ian Kivlichan December 11, 2015 Abstract Stoquastic Hamiltonians, those for which all off-diagonal matrix elements in the standard basis are real and non-positive,
More informationGraph Non-Isomorphism Has a Succinct Quantum Certificate
Graph Non-Isomorphism Has a Succinct Quantum Certificate Tatsuaki Okamoto Keisuke Tanaka Summary This paper presents the first quantum computational characterization of the Graph Non-Isomorphism problem
More information. An introduction to Quantum Complexity. Peli Teloni
An introduction to Quantum Complexity Peli Teloni Advanced Topics on Algorithms and Complexity µπλ July 3, 2014 1 / 50 Outline 1 Motivation 2 Computational Model Quantum Circuits Quantum Turing Machine
More informationLecture 22: Quantum computational complexity
CPSC 519/619: Quantum Computation John Watrous, University of Calgary Lecture 22: Quantum computational complexity April 11, 2006 This will be the last lecture of the course I hope you have enjoyed the
More informationOn the power of a unique quantum witness
On the power of a unique quantum witness Rahul Jain Iordanis Kerenidis Miklos Santha Shengyu Zhang Abstract In a celebrated paper, Valiant and Vazirani [28] raised the question of whether the difficulty
More information6.896 Quantum Complexity Theory 30 October Lecture 17
6.896 Quantum Complexity Theory 30 October 2008 Lecturer: Scott Aaronson Lecture 17 Last time, on America s Most Wanted Complexity Classes: 1. QMA vs. QCMA; QMA(2). 2. IP: Class of languages L {0, 1} for
More informationarxiv:quant-ph/ v1 11 Oct 2002
Quantum NP - A Survey Dorit Aharonov and Tomer Naveh arxiv:quant-ph/00077 v Oct 00 Abstract We describe Kitaev s result from 999, in which he defines the complexity class QMA, the quantum analog of the
More informationDiscrete quantum random walks
Quantum Information and Computation: Report Edin Husić edin.husic@ens-lyon.fr Discrete quantum random walks Abstract In this report, we present the ideas behind the notion of quantum random walks. We further
More informationNotes on Complexity Theory Last updated: November, Lecture 10
Notes on Complexity Theory Last updated: November, 2015 Lecture 10 Notes by Jonathan Katz, lightly edited by Dov Gordon. 1 Randomized Time Complexity 1.1 How Large is BPP? We know that P ZPP = RP corp
More informationCS286.2 Lecture 8: A variant of QPCP for multiplayer entangled games
CS286.2 Lecture 8: A variant of QPCP for multiplayer entangled games Scribe: Zeyu Guo In the first lecture, we saw three equivalent variants of the classical PCP theorems in terms of CSP, proof checking,
More informationLecture 4: Postulates of quantum mechanics
Lecture 4: Postulates of quantum mechanics Rajat Mittal IIT Kanpur The postulates of quantum mechanics provide us the mathematical formalism over which the physical theory is developed. For people studying
More informationQMA(2) workshop Tutorial 1. Bill Fefferman (QuICS)
QMA(2) workshop Tutorial 1 Bill Fefferman (QuICS) Agenda I. Basics II. Known results III. Open questions/next tutorial overview I. Basics I.1 Classical Complexity Theory P Class of problems efficiently
More informationOn the power of quantum, one round, two prover interactive proof systems
On the power of quantum, one round, two prover interactive proof systems Alex Rapaport and Amnon Ta-Shma Department of Computer Science Tel-Aviv University Israel 69978. email: rapapo,amnon@post.tau.ac.il.
More informationSuccinct quantum proofs for properties of finite groups
Succinct quantum proofs for properties of finite groups John Watrous Department of Computer Science University of Calgary Calgary, Alberta, Canada Abstract In this paper we consider a quantum computational
More informationQuantum Computing Lecture 8. Quantum Automata and Complexity
Quantum Computing Lecture 8 Quantum Automata and Complexity Maris Ozols Computational models and complexity Shor s algorithm solves, in polynomial time, a problem for which no classical polynomial time
More informationPROBABILISTIC COMPUTATION. By Remanth Dabbati
PROBABILISTIC COMPUTATION By Remanth Dabbati INDEX Probabilistic Turing Machine Probabilistic Complexity Classes Probabilistic Algorithms PROBABILISTIC TURING MACHINE It is a turing machine with ability
More informationQuantum Computation. Michael A. Nielsen. University of Queensland
Quantum Computation Michael A. Nielsen University of Queensland Goals: 1. To eplain the quantum circuit model of computation. 2. To eplain Deutsch s algorithm. 3. To eplain an alternate model of quantum
More informationGeneralized Lowness and Highness and Probabilistic Complexity Classes
Generalized Lowness and Highness and Probabilistic Complexity Classes Andrew Klapper University of Manitoba Abstract We introduce generalized notions of low and high complexity classes and study their
More informationLecture 2: From Classical to Quantum Model of Computation
CS 880: Quantum Information Processing 9/7/10 Lecture : From Classical to Quantum Model of Computation Instructor: Dieter van Melkebeek Scribe: Tyson Williams Last class we introduced two models for deterministic
More informationThe detectability lemma and quantum gap amplification
The detectability lemma and quantum gap amplification Dorit Aharonov 1, Itai Arad 1, Zeph Landau 2 and Umesh Vazirani 2 1 School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel
More informationHamiltonian simulation and solving linear systems
Hamiltonian simulation and solving linear systems Robin Kothari Center for Theoretical Physics MIT Quantum Optimization Workshop Fields Institute October 28, 2014 Ask not what you can do for quantum computing
More informationA Note on the Karp-Lipton Collapse for the Exponential Hierarchy
A Note on the Karp-Lipton Collapse for the Exponential Hierarchy Chris Bourke Department of Computer Science & Engineering University of Nebraska Lincoln, NE 68503, USA Email: cbourke@cse.unl.edu January
More informationA Glimpse of Quantum Computation
A Glimpse of Quantum Computation Zhengfeng Ji (UTS:QSI) QCSS 2018, UTS 1. 1 Introduction What is quantum computation? Where does the power come from? Superposition Incompatible states can coexist Transformation
More informationRESTRICTED VERSIONS OF THE TWO-LOCAL HAMILTONIAN PROBLEM
The Pennsylvania State University The Graduate School Eberly College of Science RESTRICTED VERSIONS OF THE TWO-LOCAL HAMILTONIAN PROBLEM A Dissertation in Physics by Sandeep Narayanaswami c 2013 Sandeep
More informationOn the complexity of probabilistic trials for hidden satisfiability problems
On the complexity of probabilistic trials for hidden satisfiability problems Itai Arad 1, Adam Bouland 2, Daniel Grier 2, Miklos Santha 1,3, Aarthi Sundaram 1, and Shengyu Zhang 4 1 Center for Quantum
More informationLecture 23: Introduction to Quantum Complexity Theory 1 REVIEW: CLASSICAL COMPLEXITY THEORY
Quantum Computation (CMU 18-859BB, Fall 2015) Lecture 23: Introduction to Quantum Complexity Theory November 31, 2015 Lecturer: Ryan O Donnell Scribe: Will Griffin 1 REVIEW: CLASSICAL COMPLEXITY THEORY
More information6.896 Quantum Complexity Theory November 4th, Lecture 18
6.896 Quantum Complexity Theory November 4th, 2008 Lecturer: Scott Aaronson Lecture 18 1 Last Time: Quantum Interactive Proofs 1.1 IP = PSPACE QIP = QIP(3) EXP The first result is interesting, because
More information6.896 Quantum Complexity Theory Oct. 28, Lecture 16
6.896 Quantum Complexity Theory Oct. 28, 2008 Lecturer: Scott Aaronson Lecture 16 1 Recap Last time we introduced the complexity class QMA (quantum Merlin-Arthur), which is a quantum version for NP. In
More informationCryptographic Protocols Notes 2
ETH Zurich, Department of Computer Science SS 2018 Prof. Ueli Maurer Dr. Martin Hirt Chen-Da Liu Zhang Cryptographic Protocols Notes 2 Scribe: Sandro Coretti (modified by Chen-Da Liu Zhang) About the notes:
More informationCompute the Fourier transform on the first register to get x {0,1} n x 0.
CS 94 Recursive Fourier Sampling, Simon s Algorithm /5/009 Spring 009 Lecture 3 1 Review Recall that we can write any classical circuit x f(x) as a reversible circuit R f. We can view R f as a unitary
More information-bit integers are all in ThC. Th The following problems are complete for PSPACE NPSPACE ATIME QSAT, GEOGRAPHY, SUCCINCT REACH.
CMPSCI 601: Recall From Last Time Lecture 26 Theorem: All CFL s are in sac. Facts: ITADD, MULT, ITMULT and DIVISION on -bit integers are all in ThC. Th The following problems are complete for PSPACE NPSPACE
More informationarxiv: v1 [quant-ph] 12 May 2012
Multi-Prover Quantum Merlin-Arthur Proof Systems with Small Gap Attila Pereszlényi Centre for Quantum Technologies, National University of Singapore May, 0 arxiv:05.76v [quant-ph] May 0 Abstract This paper
More informationLecture 6: Random Walks versus Independent Sampling
Spectral Graph Theory and Applications WS 011/01 Lecture 6: Random Walks versus Independent Sampling Lecturer: Thomas Sauerwald & He Sun For many problems it is necessary to draw samples from some distribution
More informationCS286.2 Lecture 15: Tsirelson s characterization of XOR games
CS86. Lecture 5: Tsirelson s characterization of XOR games Scribe: Zeyu Guo We first recall the notion of quantum multi-player games: a quantum k-player game involves a verifier V and k players P,...,
More informationOn the complexity of approximating the diamond norm
On the complexity of approximating the diamond norm Avraham Ben-Aroya Amnon Ta-Shma Abstract The diamond norm is a norm defined over the space of quantum transformations. This norm has a natural operational
More informationBasics of quantum computing and some recent results. Tomoyuki Morimae YITP Kyoto University) min
Basics of quantum computing and some recent results Tomoyuki Morimae YITP Kyoto University) 50+10 min Outline 1. Basics of quantum computing circuit model, classically simulatable/unsimulatable, quantum
More informationDetecting pure entanglement is easy, so detecting mixed entanglement is hard
A quantum e-meter Detecting pure entanglement is easy, so detecting mixed entanglement is hard Aram Harrow (University of Washington) and Ashley Montanaro (University of Cambridge) arxiv:1001.0017 Waterloo
More informationReflections in Hilbert Space III: Eigen-decomposition of Szegedy s operator
Reflections in Hilbert Space III: Eigen-decomposition of Szegedy s operator James Daniel Whitfield March 30, 01 By three methods we may learn wisdom: First, by reflection, which is the noblest; second,
More informationIntroduction to Interactive Proofs & The Sumcheck Protocol
CS294: Probabilistically Checkable and Interactive Proofs January 19, 2017 Introduction to Interactive Proofs & The Sumcheck Protocol Instructor: Alessandro Chiesa & Igor Shinkar Scribe: Pratyush Mishra
More informationRandomized Complexity Classes; RP
Randomized Complexity Classes; RP Let N be a polynomial-time precise NTM that runs in time p(n) and has 2 nondeterministic choices at each step. N is a polynomial Monte Carlo Turing machine for a language
More informationThe computational difficulty of finding MPS ground states
The computational difficulty of finding MPS ground states Norbert Schuch 1, Ignacio Cirac 1, and Frank Verstraete 2 1 Max-Planck-Institute for Quantum Optics, Garching, Germany 2 University of Vienna,
More informationQuantum Information and the PCP Theorem
Quantum Information and the PCP Theorem arxiv:quant-ph/0504075v1 10 Apr 2005 Ran Raz Weizmann Institute ran.raz@weizmann.ac.il Abstract We show how to encode 2 n (classical) bits a 1,...,a 2 n by a single
More informationQuantum Computing Lecture 2. Review of Linear Algebra
Quantum Computing Lecture 2 Review of Linear Algebra Maris Ozols Linear algebra States of a quantum system form a vector space and their transformations are described by linear operators Vector spaces
More informationarxiv: v2 [quant-ph] 16 Nov 2008
The Power of Unentanglement arxiv:0804.080v [quant-ph] 16 Nov 008 Scott Aaronson MIT Salman Beigi MIT Andrew Drucker MIT Peter Shor MIT Abstract Bill Fefferman University of Chicago The class QMA(k), introduced
More informationLimits on the power of quantum statistical zero-knowledge
Limits on the power of quantum statistical zero-knowledge John Watrous Department of Computer Science University of Calgary Calgary, Alberta, Canada jwatrous@cpsc.ucalgary.ca January 16, 2003 Abstract
More informationQuantum Computational Complexity
Quantum Computational Complexity John Watrous Institute for Quantum Computing and School of Computer Science University of Waterloo, Waterloo, Ontario, Canada. arxiv:0804.3401v1 [quant-ph] 21 Apr 2008
More informationGeneral Properties of Quantum Zero-Knowledge Proofs
General Properties of Quantum Zero-Knowledge Proofs Hirotada Kobayashi Principles of Informatics Research Division, National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
More informationc 2006 Society for Industrial and Applied Mathematics
SIAM J. COMPUT. Vol. 35, No. 5, pp. 1070 1097 c 006 Society for Industrial and Applied Mathematics THE COMPLEXITY OF THE LOCAL HAMILTONIAN PROBLEM JULIA KEMPE, ALEXEI KITAEV, AND ODED REGEV Abstract. The
More informationThe Power of Unentanglement (Extended Abstract)
The Power of Unentanglement Extended Abstract) Scott Aaronson MIT Salman Beigi MIT Andrew Drucker MIT Bill Fefferman University of Chicago Peter Shor MIT Abstract The class QMAk), introduced by Kobayashi
More informationClassical Verification of Quantum Computations
2018 IEEE 59th Annual Symposium on Foundations of Computer Science Classical Verification of Quantum Computations Urmila Mahadev Department of Computer Science, UC Berkeley mahadev@berkeley.edu Abstract
More information6.896 Quantum Complexity Theory November 11, Lecture 20
6.896 Quantum Complexity Theory November, 2008 Lecturer: Scott Aaronson Lecture 20 Last Time In the previous lecture, we saw the result of Aaronson and Watrous that showed that in the presence of closed
More informationA Complete Characterization of Unitary Quantum Space
A Complete Characterization of Unitary Quantum Space Bill Fefferman 1 and Cedric Yen-Yu Lin 1 1 Joint Center for Quantum Information and Computer Science (QuICS), University of Maryland November 22, 2016
More informationLecture 26: Arthur-Merlin Games
CS 710: Complexity Theory 12/09/2011 Lecture 26: Arthur-Merlin Games Instructor: Dieter van Melkebeek Scribe: Chetan Rao and Aaron Gorenstein Last time we compared counting versus alternation and showed
More informationQuantum-Merlin-Arthur-complete problems for stoquastic Hamiltonians and Markov matrices
Quantum-Merlin-Arthur-complete problems for stoquastic Hamiltonians and Markov matrices The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters.
More informationQALGO workshop, Riga. 1 / 26. Quantum algorithms for linear algebra.
QALGO workshop, Riga. 1 / 26 Quantum algorithms for linear algebra., Center for Quantum Technologies and Nanyang Technological University, Singapore. September 22, 2015 QALGO workshop, Riga. 2 / 26 Overview
More informationreport: RMT and boson computers
18.338 report: RMT and boson computers John Napp May 11, 2016 Abstract In 2011, Aaronson and Arkhipov [1] proposed a simple model of quantum computation based on the statistics of noninteracting bosons.
More information3 Symmetry Protected Topological Phase
Physics 3b Lecture 16 Caltech, 05/30/18 3 Symmetry Protected Topological Phase 3.1 Breakdown of noninteracting SPT phases with interaction Building on our previous discussion of the Majorana chain and
More informationA New Lower Bound Technique for Quantum Circuits without Ancillæ
A New Lower Bound Technique for Quantum Circuits without Ancillæ Debajyoti Bera Abstract We present a technique to derive depth lower bounds for quantum circuits. The technique is based on the observation
More informationComplexity-Theoretic Foundations of Quantum Supremacy Experiments
Complexity-Theoretic Foundations of Quantum Supremacy Experiments Scott Aaronson, Lijie Chen UT Austin, Tsinghua University MIT July 7, 2017 Scott Aaronson, Lijie Chen (UT Austin, Tsinghua University Complexity-Theoretic
More informationarxiv: v2 [quant-ph] 1 Sep 2012
QMA variants with polynomially many provers Sevag Gharibian Jamie Sikora Sarvagya Upadhyay arxiv:1108.0617v2 [quant-ph] 1 Sep 2012 September 3, 2012 Abstract We study three variants of multi-prover quantum
More informationLecture 2: November 9
Semidefinite programming and computational aspects of entanglement IHP Fall 017 Lecturer: Aram Harrow Lecture : November 9 Scribe: Anand (Notes available at http://webmitedu/aram/www/teaching/sdphtml)
More informationCSC 2429 Approaches to the P versus NP Question. Lecture #12: April 2, 2014
CSC 2429 Approaches to the P versus NP Question Lecture #12: April 2, 2014 Lecturer: David Liu (Guest) Scribe Notes by: David Liu 1 Introduction The primary goal of complexity theory is to study the power
More informationQUANTUM FINITE AUTOMATA. Andris Ambainis 1 1 Faculty of Computing, University of Latvia,
QUANTUM FINITE AUTOMATA Andris Ambainis 1 1 Faculty of Computing, University of Latvia, Raiņa bulv. 19, Rīga, LV-1586, Latvia. Email: ambainis@lu.lv Abstract Quantum finite automata (QFAs) are quantum
More informationLecture 26. Daniel Apon
Lecture 26 Daniel Apon 1 From IPPSPACE to NPPCP(log, 1): NEXP has multi-prover interactive protocols If you ve read the notes on the history of the PCP theorem referenced in Lecture 19 [3], you will already
More informationPr[X = s Y = t] = Pr[X = s] Pr[Y = t]
Homework 4 By: John Steinberger Problem 1. Recall that a real n n matrix A is positive semidefinite if A is symmetric and x T Ax 0 for all x R n. Assume A is a real n n matrix. Show TFAE 1 : (a) A is positive
More informationInteractive Proofs 1
CS294: Probabilistically Checkable and Interactive Proofs January 24, 2017 Interactive Proofs 1 Instructor: Alessandro Chiesa & Igor Shinkar Scribe: Mariel Supina 1 Pspace IP The first proof that Pspace
More informationTsuyoshi Ito (McGill U) Hirotada Kobayashi (NII & JST) Keiji Matsumoto (NII & JST)
Tsuyoshi Ito (McGill U) Hirotada Kobayashi (NII & JST) Keiji Matsumoto (NII & JST) arxiv:0810.0693 QIP 2009, January 12 16, 2009 Interactive proof [Babai 1985] [Goldwasser, Micali, Rackoff 1989] erifier:
More informationUpper Bounds for Quantum Interactive Proofs with Competing Provers
Upper Bounds for Quantum Interactive Proofs with Competing Provers Gus Gutoski Institute for Quantum Information Science and Department of Computer Science University of Calgary Calgary, Alberta, Canada
More informationQuantum Information and the PCP Theorem
Quantum Information and the PCP Theorem Ran Raz Weizmann Institute ran.raz@weizmann.ac.il Abstract Our main result is that the membership x SAT (for x of length n) can be proved by a logarithmic-size quantum
More informationQuantum Metropolis Sampling. K. Temme, K. Vollbrecht, T. Osborne, D. Poulin, F. Verstraete arxiv:
Quantum Metropolis Sampling K. Temme, K. Vollbrecht, T. Osborne, D. Poulin, F. Verstraete arxiv: 09110365 Quantum Simulation Principal use of quantum computers: quantum simulation Quantum Chemistry Determination
More information6.841/18.405J: Advanced Complexity Wednesday, April 2, Lecture Lecture 14
6.841/18.405J: Advanced Complexity Wednesday, April 2, 2003 Lecture Lecture 14 Instructor: Madhu Sudan In this lecture we cover IP = PSPACE Interactive proof for straightline programs. Straightline program
More informationα x x 0 α x x f(x) α x x α x ( 1) f(x) x f(x) x f(x) α x = α x x 2
Quadratic speedup for unstructured search - Grover s Al- CS 94- gorithm /8/07 Spring 007 Lecture 11 01 Unstructured Search Here s the problem: You are given an efficient boolean function f : {1,,} {0,1},
More informationImpossibility of Succinct Quantum Proofs for Collision- Freeness
Impossibility of Succinct Quantum Proofs for Collision- Freeness The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation As Published
More informationReport on Conceptual Foundations and Foils for QIP at the Perimeter Institute, May 9 May
Report on Conceptual Foundations and Foils for QIP at the Perimeter Institute, May 9 May 13 2011 Vlad Gheorghiu Department of Physics Carnegie Mellon University Pittsburgh, PA 15213, U.S.A. May 19, 2011
More informationCSC 5170: Theory of Computational Complexity Lecture 9 The Chinese University of Hong Kong 15 March 2010
CSC 5170: Theory of Computational Complexity Lecture 9 The Chinese University of Hong Kong 15 March 2010 We now embark on a study of computational classes that are more general than NP. As these classes
More informationNotes for Lecture 2. Statement of the PCP Theorem and Constraint Satisfaction
U.C. Berkeley Handout N2 CS294: PCP and Hardness of Approximation January 23, 2006 Professor Luca Trevisan Scribe: Luca Trevisan Notes for Lecture 2 These notes are based on my survey paper [5]. L.T. Statement
More informationLecture 12 Combinatorial Planning in High Dimensions
CS 460/560 Introduction to Computational Robotics Fall 017, Rutgers University Lecture 1 Combinatorial Planning in High Dimensions Instructor: Jingjin Yu Outline Review of combinatorial motion planning
More information6-1 Computational Complexity
6-1 Computational Complexity 6. Computational Complexity Computational models Turing Machines Time complexity Non-determinism, witnesses, and short proofs. Complexity classes: P, NP, conp Polynomial-time
More informationOn preparing ground states of gapped Hamiltonians: An efficient Quantum Lovász Local Lemma
On preparing ground states of gapped Hamiltonians: An efficient Quantum Lovász Local Lemma András Gilyén QuSoft, CWI, Amsterdam, Netherlands Joint work with: Or Sattath Hebrew University and MIT 1 / 20
More informationQuantum Information and Quantum Many-body Systems
Quantum Information and Quantum Many-body Systems Lecture 1 Norbert Schuch California Institute of Technology Institute for Quantum Information Quantum Information and Quantum Many-Body Systems Aim: Understand
More information4 Matrix product states
Physics 3b Lecture 5 Caltech, 05//7 4 Matrix product states Matrix product state (MPS) is a highly useful tool in the study of interacting quantum systems in one dimension, both analytically and numerically.
More informationTheory Component of the Quantum Computing Roadmap
3.2.4 Quantum simulation Quantum simulation represents, along with Shor s and Grover s algorithms, one of the three main experimental applications of quantum computers. Of the three, quantum simulation
More informationAlgorithm for Quantum Simulation
Applied Mathematics & Information Sciences 3(2) (2009), 117 122 An International Journal c 2009 Dixie W Publishing Corporation, U. S. A. Algorithm for Quantum Simulation Barry C. Sanders Institute for
More informationAlmost transparent short proofs for NP R
Brandenburgische Technische Universität, Cottbus, Germany From Dynamics to Complexity: A conference celebrating the work of Mike Shub Toronto, May 10, 2012 Supported by DFG under GZ:ME 1424/7-1 Outline
More informationThe computational power of many-body systems
The computational power of many-body systems by Zak Webb A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Physics and
More informationLecture 3: Interactive Proofs and Zero-Knowledge
CS 355 Topics in Cryptography April 9, 2018 Lecture 3: Interactive Proofs and Zero-Knowledge Instructors: Henry Corrigan-Gibbs, Sam Kim, David J. Wu So far in the class, we have only covered basic cryptographic
More informationSimulating classical circuits with quantum circuits. The complexity class Reversible-P. Universal gate set for reversible circuits P = Reversible-P
Quantum Circuits Based on notes by John Watrous and also Sco8 Aaronson: www.sco8aaronson.com/democritus/ John Preskill: www.theory.caltech.edu/people/preskill/ph229 Outline Simulating classical circuits
More informationCSCI 1590 Intro to Computational Complexity
CSCI 1590 Intro to Computational Complexity Interactive Proofs John E. Savage Brown University April 20, 2009 John E. Savage (Brown University) CSCI 1590 Intro to Computational Complexity April 20, 2009
More informationQuantum interactive proofs with short messages
Quantum interactive proofs with short messages SalmanBeigi Peter W. Shor John Watrous Institutefor Quantum Information California Institute of Technology Department of Mathematics Massachusetts Institute
More information1 Randomized complexity
80240233: Complexity of Computation Lecture 6 ITCS, Tsinghua Univesity, Fall 2007 23 October 2007 Instructor: Elad Verbin Notes by: Zhang Zhiqiang and Yu Wei 1 Randomized complexity So far our notion of
More informationLecture 17: Interactive Proof Systems
Computational Complexity Theory, Fall 2010 November 5 Lecture 17: Interactive Proof Systems Lecturer: Kristoffer Arnsfelt Hansen Scribe: Søren Valentin Haagerup 1 Interactive Proof Systems Definition 1.
More informationComputational Complexity
p. 1/24 Computational Complexity The most sharp distinction in the theory of computation is between computable and noncomputable functions; that is, between possible and impossible. From the example of
More informationOracle Separations for Quantum Statistical Zero-Knowledge
Oracle Separations for Quantum Statistical Zero-Knowledge Sanketh Menda and John Watrous,2 Institute for Quantum Computing and School of Computer Science University of Waterloo, Canada 2 Canadian Institute
More informationarxiv: v1 [quant-ph] 1 Sep 2009
Adaptive versus non-adaptive strategies for quantum channel discrimination Aram W. Harrow Avinatan Hassidim Debbie W. Leung John Watrous arxiv:0909.056v [quant-ph] Sep 009 Department of Mathematics, University
More information