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An Effective State Space Dimension For A Quantu...

Travis Scholten
December 14, 2016

An Effective State Space Dimension For A Quantum System

A talk I gave to update my dissertation committee regarding my research.

Released under SAND2016-12569 PE

Travis Scholten

December 14, 2016
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  1. An Effective State Space Dimension For A Quantum System Travis

    L Scholten @Travis_Sch Center for Quantum Information and Control University of New Mexico Center for Computing Research Sandia National Labs, New Mexico Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. CCR Center for Computing Research
  2. Originally, this talk was going to be about this… arXiv:1609.04385

    (Go check it out on GitHub!) Behavior of the Maximum Likelihood in Quantum State Tomography Travis L Scholten and Robin Blume-Kohout Center for Computing Research (CCR), Sandia National Labs and University of New Mexico (Dated: September 22, 2016) Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. Model selection can be used to tailor the number of fit parameters to the data, but quantum tomography violates some common assumptions that underly canonical model selection techniques based on ratios of maximum likelihoods (loglikelihood ratio statistics), due to the nature of the state space boundaries. Here, we study the behavior of the maximum likelihood in di↵erent Hilbert space dimensions, and derive an expression for a complexity penalty – the expected value of the loglikelihood ratio statistic (roughly, the logarithm of the maximum likelihood) – that can be used to make an appropriate choice for d. antum information science, an experimentalist h to determine the quantum state ⇢0 that is pro- y a specific initialization procedure. This can be ng quantum state tomography [1]: many copies e produced; they are measured in diverse ways; lly the outcomes of those measurements (data) ted and analyzed to produce an estimate ˆ ⇢. This 60
  3. …but instead, it’s more about this. Hi Travis! I just

    read your new paper on the arXiv, which I think is pretty cool. Nice work! It also might have some wider application than “just” model selection: a mathematician would call your expectation value of gamma the Gaussian width (or statistical dimension) of something I would call the tangential cone at rho_0… These Gaussian widths have quite some applications in other fields, see e.g. https://arxiv.org/abs/1303.6672 who use them to explain many result in Compressed Sensing and related fields. 59
  4. Sets of density matrices are statistical models. Density matrices (set

    of quantum states) {M | M 2 B(H), Tr(M) = 1, M 0} Fixed POVM + Born rule gives probability distribution Prj(⇢) = Tr(Ej⇢) ⇢ 57
  5. Quantum state tomography maps data to density matrices. Thus, state

    tomography is the (statistical) “inverse problem”. Data set (from experiments, e.g., POVM counts) {(Ej, nj)} 56
  6. Data set (from experiments, e.g., POVM counts) Density matrices (set

    of quantum states) ˆ ⇢ {M | M 2 B(H), Tr(M) = 1, M 0} Quantum state tomography maps data to density matrices. Thus, state tomography is the (statistical) “inverse problem”. 55 {(Ej, nj)}
  7. © 2005 Nature Publishing Group method, and the spread of

    the expectation values of the observables was extracted. For an investigation of the entanglement properties, we associate each particle k of a state r with a (possibly spatially separated) party Ak . We shall be interested in different aspects of entanglement between parties Ak , that is, the non-locality of the state r. A detailed entanglement analysis is achieved by investigating (1) the presence of genuine multipartite entanglement, (2) the distillability of multipartite entanglement and (3) entanglement in reduced states of two qubits. First, we consider whether the production of a single copy of the state requires non-local interactions of all parties. This leads to the notion of multipartite entanglement and biseparability. A pure multipartite state jwl is called biseparable if two groups G1 and G2 within the parties Ak can be found such that jwl is a product state with respect to the partition jwl ¼ jxl G1 ^jhl G2 ð2Þ otherwise it is multipartite entangled. A mixed state r is called biseparable if it can be produced by mixing pure biseparable states jwbs i l—which may be biseparable with respect to different bipartitions—with some probabilities pi , that is, the state can be written as r ¼ P i pi jwbs i lkwbs i j: If this is not the case, r is multipartite entangled. The generation of such a genuine multipartite entangled state requires interaction between all parties. In particular, a mixture of bipartite entangled states is not considered to be multipartite entangled. In order to show the presence of multipartite entangle- ment, we use the method of entanglement witnesses21–23. An entanglement witness for multipartite entanglement is an obser- vable with a positive expectation value on all biseparable states. Thus a negative expectation value proves the presence of multipartite entanglement. A typical witness for the states jWN l would be23: WN ¼ N 2 1 N l 2 jWN lkWN j ð3Þ This witness detects a state as entangled if the fidelity of the W state exceeds (N 2 1)/N. However, more sophisticated witnesses can be constructed, if there is more information available on the state under these advanced witnesses. The negative expectation values prove that in our experiment four-, five-, six-, seven- and eight-qubit entanglement has been produced. Second, we consider the question of whether one can use many copies of the state r to distil one pure multipartite entangled state jwl by local means; that is, whether entanglement contained in r is qualitatively equivalent to multiparty pure state entanglement. For this aim one determines whether there exists a number M such that the transformation M copies r^r^···^r |fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl} ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ! LOCC jwl ð4Þ is possible. Here, jwl is a multipartite entangled pure state (for Figure 1 | Absolute values, jrj, of the reconstructed density matrix of a jW8 l state as obtained from quantum state tomography. DDDDDDDD…SSSSSSSS label the entries of the density matrix r. Ideally, the blue coloured entries all have the same height of 0.125; the yellow coloured bars indicate noise. Numerical values of the density matrices for 4 # N # 8 can be found in Supplementary Information. In the upper right corner a string of eight trapped ions is shown. 644 ~ 656,100 experiments ~10 hours experimental runtime ~ weeks of data processing (MLE) For 8 ions: Scalable multiparticle entanglement of trapped ions H. Ha ¨ffner1,3, W. Ha ¨nsel1, C. F. Roos1,3, J. Benhelm1,3, D. Chek-al-kar1, M. Chwalla1, T. Ko ¨rber1,3, U. D. Rapol1,3, M. Riebe1, P. O. Schmidt1, C. Becher1†, O. Gu ¨hne3, W. Du ¨r2,3 & R. Blatt1,3 The generation, manipulation and fundamental understanding of entanglement lies at the very heart of quantum mechanics. Entangled particles are non-interacting but are described by a common wavefunction; consequently, individual particles are not independent of each other and their quantum properties are inextricably interwoven1–3. The intriguing features of entangle- ment become particularly evident if the particles can be individu- ally controlled and physically separated. However, both the experimental realization and characterization of entanglement become exceedingly difficult for systems with many particles. The main difficulty is to manipulate and detect the quantum state of individual particles as well as to control the interaction between them. So far, entanglement of four ions4 or five photons5 has been demonstrated experimentally. The creation of scalable multi- particle entanglement demands a non-exponential scaling of resources with particle number. Among the various kinds of entangled states, the ‘W state’6–8 plays an important role as its entanglement is maximally persistent and robust even under particle loss. Such states are central as a resource in quantum information processing9 and multiparty quantum communi- cation. Here we report the scalable and deterministic generation of four-, five-, six-, seven- and eight-particle entangled states of the W type with trapped ions. We obtain the maximum possible information on these states by performing full characterization via state tomography10, using individual control and detection of the ions. A detailed analysis proves that the entanglement is genuine. The availability of such multiparticle entangled states, together with full information in the form of their density matrices, creates a test-bed for theoretical studies of multiparticle entanglement. Independently, ‘Greenberger–Horne–Zeilinger’ entangled states11 with up to six ions have been created and t < 1.16 s) represent the qubits. Each ion qubit in the linear string is individually addressed by a series of tightly focused laser pulses on the jSl ; S1=2 ðmj ¼ 21=2Þ $ jDl ; D5=2 ðmj ¼ 21=2Þ quadrupole transition employing narrowband laser radiation near 729 nm. Doppler cooling on the fast S $ P transition (lifetime ,8 ns) and subsequent sideband cooling prepare the ion string in the ground state of the centre-of-mass vibrational mode18. Optical pumping initializes the ions’ electronic qubit states in the jSl state. After preparing an entangled state with a series of laser pulses, the quantum state is read out with a CCD camera using state selective fluorescence18. The W states are efficiently generated by sharing one motional quantum between the ions with partial swap operations (see Table 1)8. For an increasing number of ions, however, the initializa- tion of the quantum register becomes more and more difficult as technical imperfections—like incomplete optical pumping—add up for each ion. Therefore, for N ¼ 6,7,8, we first prepare the state j0;DD···Dl with N p pulses on the carrier transition18, where the 0 refers to the motional state of the centre-of-mass mode. Then, laser light resonant with the S $ P transition projects the ion string on the measurement basis. Absence of fluorescence indicates that all ions are prepared in jDl. Similarly, we test the motional state with a single p pulse on the blue sideband18. Absence of fluorescence during a subsequent detection period indicates ground state occupation. Success of both checks (total success rate $0.7) confirms that the desired initial state j0;DD···Dl is indeed prepared. We can then start with the actual entangling procedure (step (1) in Table 1) and create jWN l states (N # 8) in about 500–1,000 ms. Full information of the N-ion entangled state is obtained via quantum state reconstruction by expanding the density matrix in a basis of observables19 and measuring the corresponding expectation LETTERS Vol 438|1 December 2005|doi:10.1038/nature04279 53
  8. Gate set tomography requires many experiments. “long-sequence” GST: 2738 unique

    experiments (L = 256) “long-sequence” GST: 1583 unique experiments (L = 4) memory-intensive ~hours processing (minutes w/ multi-core) (pyGSTi software) 52
  9. articles Wigner function reconstruction via inverse Radon transform Formally, all

    quadrature angles would need to be measured. Finite number of angles = reconstruction artifacts 51
  10. Unfortunately, state tomography tends to be resource-intensive. Number of measurement

    outcomes Experimental runtime Offline data processing 50
  11. Unfortunately, state tomography tends to be resource-intensive. Number of measurement

    outcomes Experimental runtime Offline data processing An issue: How many parameters in the density matrix we have to fit! 49
  12. ˆ ⇢ = ✓ ⇢00 ⇢01 ⇢10 ⇢11 ◆ ˆ

    ⇢ = 0 B B @ ⇢00 ⇢01 ⇢02 ⇢03 ⇢10 ⇢11 ⇢12 ⇢13 ⇢20 ⇢21 ⇢22 ⇢23 ⇢30 ⇢31 ⇢32 ⇢33 1 C C A ˆ ⇢ = 0 B B B B B B B B B B @ ⇢00 ⇢01 ⇢02 ⇢03 ⇢04 ⇢05 ⇢06 ⇢07 ⇢10 ⇢11 ⇢12 ⇢13 ⇢14 ⇢15 ⇢16 ⇢17 ⇢20 ⇢21 ⇢22 ⇢23 ⇢24 ⇢25 ⇢26 ⇢27 ⇢30 ⇢31 ⇢32 ⇢33 ⇢34 ⇢35 ⇢36 ⇢37 ⇢40 ⇢41 ⇢42 ⇢43 ⇢44 ⇢45 ⇢46 ⇢47 ⇢50 ⇢51 ⇢52 ⇢53 ⇢54 ⇢55 ⇢56 ⇢57 ⇢60 ⇢61 ⇢62 ⇢63 ⇢64 ⇢65 ⇢66 ⇢67 ⇢70 ⇢71 ⇢72 ⇢73 ⇢74 ⇢75 ⇢76 ⇢77 1 C C C C C C C C C C A For example, a general density matrix of a multi-qubit systems has an exponential number of parameters to be fit. p = 3 parameters p = 15 parameters p = 63 parameters p = 4N 1 parameters 48
  13. One structure commonly imposed is based on the rank of

    the estimate. ˆ ⇢ = | ih | ˆ ⇢ = a| ih | + (1 a)| ih | ˆ ⇢ = Pr j=1 pj | j ih j | p = 2(d 1) p = 4d 5 p = r(2d r) 1 Quantum compressed sensing: Can we find measurements such that minimizing our estimate’s rank yields one which is accurate? Structure Number of parameters 46
  14. Quantum State Tomography via Compressed Sensing David Gross,1 Yi-Kai Liu,2

    Steven T. Flammia,3 Stephen Becker,4 and Jens Eisert5 1Institute for Theoretical Physics, Leibniz University Hannover, 30167 Hannover, Germany 2Institute for Quantum Information, California Institute of Technology, Pasadena, California, USA 3Perimeter Institute for Theoretical Physics, Waterloo, Ontario, N2L 2Y5 Canada 4Applied and Computational Mathematics, California Institute of Technology, Pasadena, California, USA 5Institute of Physics und Astronomy, University of Potsdam, 14476 Potsdam, Germany (Received 21 October 2009; published 4 October 2010) We establish methods for quantum state tomography based on compressed sensing. These methods are specialized for quantum states that are fairly pure, and they offer a significant performance improvement on large quantum systems. In particular, they are able to reconstruct an unknown density matrix of dimension d and rank r using Oðrdlog2dÞ measurement settings, compared to standard methods that require d2 settings. Our methods have several features that make them amenable to experimental implementation: they require only simple Pauli measurements, use fast convex optimization, are stable against noise, and can be applied to states that are only approximately low rank. The acquired data can be used to certify that the state is indeed close to pure, so no a priori assumptions are needed. DOI: 10.1103/PhysRevLett.105.150401 PACS numbers: 03.65.Wj, 03.67.Àa The tasks of reconstructing the quantum states and processes produced by physical systems—known respec- tively as quantum state and process tomography [1]—are of increasing importance in physics and especially in quantum information science. Tomography has been used to characterize the quantum state of trapped ions [2] and an optical entangling gate [3] among many other implemen- tations. But a fundamental difficulty in performing tomo- minimum-rank matrix subject to linear constraints is NP- hard in general [8]. In addition to a reduction in experimental complexity, one might hope that a postprocessing algorithm taking as input only OðrdÞ ( d2 numbers could be tuned to run considerably faster than standard methods. Since the out- put of the procedure is a low-rank approximation to the density operator and only requires OðrdÞ numbers be PRL 105, 150401 (2010) P H Y S I C A L R E V I E W L E T T E R S week ending 8 OCTOBER 2010 Measure expectation values of tensor products of qubit Paulis Solve a convex optimization problem Provable bounds on sufficient number of outcomes for unique recovery Ej = ⌦n k=1 k k 2 {I, X, Y, Z} m cdr0 log 2 d 45 min ||M||tr s.t. Tr(M) = 1 Tr(EjM) = hEj i s.t. Tr(M) = 1 Tr(EjM) = hE
  15. Can we say anything about the resource requirements of maximum

    likelihood estimation when positivity is accounted for?
  16. ˆ ⇢MLE = argmax ⇢ L ( ⇢ ) =

    argmax ⇢ Pr( { ( Ej, nj) } | ⇢ ) ˆ ⇢MLE Maximum likelihood inference returns the state which maximizes the probability of our data. Z Hradil, 1998 43 {(Ej, nj)}
  17. To study the resource requirements of MLE, we make three

    assumptions. Local Asymptotic Normality: likelihood is Gaussian L ( ⇢ ) = Pr(Data |⇢ ) ! Pr(ˆ ⇢|⇢ ) / Exp  1 2 Tr[( ⇢ ˆ ⇢ ) I 1 ( ⇢ ˆ ⇢ )] 42
  18. To study the resource requirements of MLE, we make three

    assumptions. Local Asymptotic Normality: likelihood is Gaussian L ( ⇢ ) = Pr(Data |⇢ ) ! Pr(ˆ ⇢|⇢ ) / Exp  1 2 Tr[( ⇢ ˆ ⇢ ) I 1 ( ⇢ ˆ ⇢ )] 41
  19. Local Asymptotic Normality: likelihood is Gaussian L ( ⇢ )

    = Pr(Data |⇢ ) ! Pr(ˆ ⇢|⇢ ) / Exp  1 2 Tr[( ⇢ ˆ ⇢ ) I 1 ( ⇢ ˆ ⇢ )] To study the resource requirements of MLE, we make three assumptions. Fisher Information proportional to Hilbert-Schmidt metric I = ✏2I = ) Pr(ˆ ⇢|⇢ ) / Exp  1 2 ✏2 Tr[( ⇢ ˆ ⇢ ) 2 ] 40
  20. Local Asymptotic Normality: likelihood is Gaussian Fisher Information proportional to

    Hilbert-Schmidt metric L ( ⇢ ) = Pr(Data |⇢ ) ! Pr(ˆ ⇢|⇢ ) / Exp  1 2 Tr[( ⇢ ˆ ⇢ ) I 1 ( ⇢ ˆ ⇢ )] Impose positivity on the maximum likelihood estimates ˆ ⇢MLE = argmin ⇢ Tr[(⇢ ˆ ⇢)2] To study the resource requirements of MLE, we make three assumptions. I = ✏2I = ) Pr(ˆ ⇢|⇢ ) / Exp  1 2 ✏2 Tr[( ⇢ ˆ ⇢ ) 2 ] 39
  21. Under these assumptions, we’ll attempt to determine the average number

    of parameters in the estimate. hp(ˆ ⇢MLE)i =? Quantity to study ˆ ⇢MLE = argmin ⇢ Tr[(⇢ ˆ ⇢)2] ˆ ⇢ ⇠ N(⇢0, ✏2I) MLE, given our assumptions Distribution of unconstrained MLE (Computing exact distribution difficult) 38
  22. Can we say anything about the average number of parameters

    in maximum likelihood estimation when positivity is accounted for? Yes
  23. To answer this question, we’ll have to think about cones.

    C = { x 2 Rd | ⌧ x 2 C 8 ⌧ > 0} 36
  24. To study cones in quantum state space, we define them

    relative to some true state. ⇢0 C = { | 9 r > 0 s.t. (⇢0 + r ) 0} Think about them as real cones (write in Hilbert-Schmidt basis) 35
  25. If we zoom in on the quantum state space, we

    find such cones. ⇢0 ⇢0 ⇢0 ⇢0 Half-space Full-space C = { | 9 r > 0 s.t. (⇢0 + r ) 0} 34
  26. Classically, the statistical dimension tells us what kind of real

    vector space a cone “looks like”. C = { x 2 Rd | ⌧ x 2 C 8 ⌧ > 0} R (C) ⇡ C is an L -dimensional subspace = ) ( C ) = L 33
  27. Classically, the statistical dimension tells us what kind of real

    vector space a cone “looks like”. R (C) ⇡ not necessarily integer-valued! 32
  28. R (C) ⇡ Yes, and no ⇢0 Does the statistical

    dimension of cones in quantum state space tell us about the average number of parameters for MLE? 31 ? ⇡ Rhp(ˆ ⇢MLE)i
  29. To compute the statistical dimension of a (classical) cone, we

    start with a Gaussian distribution about its vertex… g ⇠ N(0, Id) C 30
  30. ⇧C( g ) ⌘ argmin x 2C || x g

    ||2 “metric projection” …and project the distribution back to the cone. g (closest point on the cone) ⇧C(g) C 29
  31. The statistical dimension is the expected value of the 2-norm

    of the metric projection. C (C) = h||⇧C(g)||2 2 i ⇧C( g ) ⌘ argmin x 2C || x g ||2 g ⇠ N(0, Id) McCoy/Tropp: arXiv 1308.5265 Amelunxen, et al: arXiv 1303.6672 28 where
  32. ˆ ⇢ ⇢0 To compute the statistical dimension of a

    cone around some true state, we start with a Gaussian distribution about its vertex… (It’s the distribution of unconstrained MLEs under local asymptotic normality…) ˆ ⇢ ⇠ N(⇢0, ✏2I) 27
  33. …and project the distribution back to the cone. ⇧C(ˆ ⇢)

    = argmin ⇢ Tr[(⇢ ˆ ⇢)2] ⇧C(ˆ ⇢) (It’s the maximum likelihood estimate assuming local asymptotic normality, and imposing positivity…) ˆ ⇢ 26
  34. The statistical dimension of a cone in state space is

    the average Hilbert-Schmidt distance between the true state and the MLE. ⇢0 (C) = hTr[(⇧C(ˆ ⇢) ⇢0)2]i/✏2 ⇧C(ˆ ⇢) = argmin ⇢ Tr[(⇢ ˆ ⇢)2] ˆ ⇢ ⇠ N(⇢0, ✏2I) 25 where
  35. Behavior of the Maximum Likelihood in Quantum State Tomography Travis

    L Scholten and Robin Blume-Kohout Center for Computing Research (CCR), Sandia National Labs and University of New Mexico (Dated: September 22, 2016) (C) = hTr[(⇧C(ˆ ⇢) ⇢0)2]i/✏2 Although we hadn’t set out to, we computed the statistical dimension of these cones. (C) = hTr[(ˆ ⇢MLE ⇢0)2]i/✏2 Assumptions of the paper = h i A statistic which depends on the maximum of the likelihood 23
  36. Using its definition, we realized the statistical dimension has two

    pieces - an “L”, and a “kite”. (C) = 1 ✏2 hTr[(⇧C(ˆ ⇢) ⇢0)2]i = d X j,k=1 ⌧ ||(⇧C(ˆ ⇢) ⇢0)jk ||2 ✏2 22
  37. 1. The “kite” comprises all diagonal elements and all elements

    on the kernel (null space) of ⇢0 , 2. The “L” comprises all o↵-diagonal elements on the support of ⇢0 and between the support and the kernel, and observe that h i = h i L + h i kite . The rationale for this division is simple: small fluctuations on the “L” do not change the zero eigenvalues of ˆ ⇢ to 1st order, whereas those on the “kite” do. “Kite” “L” “L” Matrix Elements of ˆ 1 0.98 0.12 0.12 0.12 0.11 0.11 0.3 1 1 0.12 0.12 0.11 0.12 0.33 0.11 1 1 0.12 0.12 0.12 0.34 0.12 0.11 1 1 0.12 0.12 0.29 0.12 0.11 0.12 0.99 0.99 0.13 0.38 0.12 0.12 0.12 0.12 0.94 1 0.35 0.13 0.12 0.12 0.12 0.12 1 2.6 1 0.99 1 1 1 0.98 2.7 1 0.94 0.99 1 1 1 1 h jk i O(✏)) egorie 1. 2. The distrib matri conve by Pr The e collisi surpri semic italize of  tics. j is Large The statistical dimension can be written in terms of two parts - an “L”, and a “kite”. (C) = 1 ✏2 hTr[(⇧C(ˆ ⇢) ⇢0)2]i = d X j,k=1 ⌧ ||(⇧C(ˆ ⇢) ⇢0)jk ||2 ✏2 21
  38. 1. The “kite” comprises all diagonal elements and all elements

    on the kernel (null space) of ⇢0 , 2. The “L” comprises all o↵-diagonal elements on the support of ⇢0 and between the support and the kernel, and observe that h i = h i L + h i kite . The rationale for this division is simple: small fluctuations on the “L” do not change the zero eigenvalues of ˆ ⇢ to 1st order, whereas those on the “kite” do. “Kite” “L” “L” Matrix Elements of ˆ 1 0.98 0.12 0.12 0.12 0.11 0.11 0.3 1 1 0.12 0.12 0.11 0.12 0.33 0.11 1 1 0.12 0.12 0.12 0.34 0.12 0.11 1 1 0.12 0.12 0.29 0.12 0.11 0.12 0.99 0.99 0.13 0.38 0.12 0.12 0.12 0.12 0.94 1 0.35 0.13 0.12 0.12 0.12 0.12 1 2.6 1 0.99 1 1 1 0.98 2.7 1 0.94 0.99 1 1 1 1 h jk i O(✏)) egorie 1. 2. The distrib matri conve by Pr The e collisi surpri semic italize of  tics. j is Large The statistical dimension can be written in terms of two parts - an “L”, and a “kite”. (C) = 1 ✏2 hTr[(⇧C(ˆ ⇢) ⇢0)2]i = (C)L + (C)Kite 20
  39. For qubits, we can compute the statistical dimension exactly. ⇢0

    ⇢0 ⇢0 ⇢0 (C) = 2.5 (C) = 3 What about higher-dimensional state spaces? 19
  40. h (⇢0 , Md)i ⇡ 2rd r2 + rq2 +

    N(N + q2) ⇡ ✓ ⇡ 2 sin 1 ✓ q 2 p N ◆◆ q(q2 + 26N) 24⇡ p 4N q2 (10) where q is given in Equation (8), N = d r, and r = Rank(⇢0 ). Equation (10) is our main result. To test its validity, we compare it to numerical simulations for d = 2, . . . , 30 and r = 1, . . . , 10, in Figure 3. The prediction of the Wilks Theorem is wildly incorrect for r ⌧ d. In contrast, an er M m d, an pa Behavior of the Maximum Likelihood in Quantum State Tomography Travis L Scholten and Robin Blume-Kohout Center for Computing Research (CCR), Sandia National Labs and University of New Mexico (Dated: September 22, 2016) Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. Model selection can be used to tailor the number of fit parameters to the data, but quantum tomography violates some common assumptions that underly canonical model selection techniques based on ratios of maximum likelihoods (loglikelihood ratio statistics), due to the nature of the state space boundaries. Here, we study the behavior of the maximum likelihood in di↵erent Hilbert space dimensions, and derive an expression for a complexity penalty – the expected value of the loglikelihood ratio statistic (roughly, the logarithm of the maximum likelihood) – that can be used to make an appropriate choice for d. ntum information science, an experimentalist to determine the quantum state ⇢0 that is pro- a specific initialization procedure. This can be g quantum state tomography [1]: many copies produced; they are measured in diverse ways; y the outcomes of those measurements (data) ed and analyzed to produce an estimate ˆ ⇢. This - - . s , - o , e d l n e o e l n s = many purposes) by their order statistics even when the elements of the sample are independent, and level avoid- ance makes the approximation even better. We make one further approximation, by assuming (as an ansatz) that N 1, and thus that the distribution of the j is e↵ectively continuous and identical to Pr(). (See Ap- pendix I for a more detailed discussion of this series of approximations.) To proceed with truncation, we observe that the j are symmetrically distributed around  = 0, so half of them are negative. Therefore, with high probabil- ity, Tr [Trunc(ˆ ⇢)] > 1, and so we will need to subtract q1l from ˆ ⇢ before truncating. The appropriate q solves Tr [Trunc(ˆ ⇢ q1l)] = 1. This equation can be solved us- ing the ansatz established so far, and some series expan- sions (see Appendix I) yield the solution: q ⇡ 2 p N (240r⇡)2/5 4 N1/10 + (240r⇡)4/5 80 N 3/10. (8) Now that we know how much to subtract o↵ in the truncation process, we can compute h i kite . Defining + q2 ✓ ⇡ 2 sin 1 ✓ q 2 p N ◆◆ p 4N q2 (10) (8), N = d r, and r = and H5 . We used reject erodyne datasets with u MLEs over each of the merical optimization [4 d, we averaged (⇢0 , M an empirical average log pair. In the large-dimensional limit, we derived an expression for the statistical dimension. (C) r = Rank(⇢0) …how did we do? 18
  41. 2 4 6 8 10 12 14 16 18 20

    d (Hilbert Space Dimension) 0 50 100 150 200 250 300 350 400 d (C) d2 1 2(d 1) From a naive parameter counting argument, we know what bounds our theory should respect. 17
  42. 2 4 6 8 10 12 14 16 18 20

    d (Hilbert Space Dimension) 0 50 100 150 200 250 300 350 400 d (C) d2 1 2(d 1) Rank( r0) =1 (various colors) Rank( r0) = 2...9 Rank( r0) =10 Our theory (generally) stays within the bounds, although discrepancies exist. (Theory breaks down when rank of truth comparable to dimension) 16
  43. 2 4 6 8 10 12 14 16 18 20

    d (Hilbert Space Dimension) 0 50 100 150 200 250 300 350 400 d (C) d2 1 2(d 1) Rank( r0) =1 (various colors) Rank( r0) = 2...9 Rank( r0) =10 And our theory does a good job of capturing numerical results. 15
  44. However, the statistical dimension quantifies geometrical, not topological, properties. Law

    of total expectation: Cannot calculate analytically; we simulate Dimension of manifold of rank-r states in a d-dimensional state space (C) = P r (C|r)Pr(Rank(ˆ ⇢MLE) = r) (C|r) ? = r(2d r) 1 13
  45. (C) = P r (C|r)Pr(Rank(ˆ ⇢MLE) = r) (C|r) ?

    = r(2d r) 1 0 50 100 150 200 (C) 0 50 100 150 200 Theory The Statistical Dimension is Not Topological 12
  46. 0 50 100 150 200 (C|r) - Numerics 0 50

    100 150 200 (C|r) - Theory Theory for (C|r) doesn’t work! (C|r) ? = r(2d r) 1 Why? Because state space boundaries are curved! 11
  47. This problem is not unique to state space, and helps

    us understand how to proceed. ( C ) = ( C| vertex)Pr(vertex) + ( C| surface)Pr(surface) + ( C| interior)Pr(interior) 10
  48. This problem is not unique to state space, and helps

    us understand how to proceed. ( C ) = ( C| vertex)Pr(vertex) + ( C| surface)Pr(surface) + ( C| interior)Pr(interior) 9
  49. This problem is not unique to state space, and helps

    us understand how to proceed. ( C ) = ( C| vertex)Pr(vertex) + ( C| surface)Pr(surface) + ( C| interior)Pr(interior) 8
  50. This problem is not unique to state space, and helps

    us understand how to proceed. (C|surface) = v1+2v2 v1+v2 Probability metric projection ends up on k-dimensional face of a polyhedral approximation ( C ) = ( C| vertex)Pr(vertex) + ( C| surface)Pr(surface) + ( C| interior)Pr(interior) 7
  51. There are several avenues we can pursue for future work.

    (C|r) = P k k ⇤ v0 k (C) Expression for “conditional” statistical dimensions? 0 mprises all o↵-diagonal elements on the ⇢0 and between the support and the h i = h i L + h i kite . The rationale for mple: small fluctuations on the “L” do ro eigenvalues of ˆ ⇢ to 1st order, whereas e” do. “Kite” “L” “L” Matrix Elements of ˆ nk-2 state is reconstructed in d = 8 dimen- 1. r elements corresponding to and given by pj = ⇢jj + jj 2. N ⌘ d r elements that a which we denote by  = {j The j are random variables, distributed. Instead, ker is prop matrix. For N 1, the distributio converges to a Wigner semicircle by Pr() = 2 ⇡R2 p R2 2 for ||  The eigenvalues are not independe collisions (“level avoidance” [29]) surprisingly regular array over the semicircle. Since our goal is to com italize on this behavior by replacin of  with a typical sample ¯  giv tics. These are the average valu j is the average value of the j Large random samples are usually many purposes) by their order st Examine different regimes for size of kite relative to entire matrix. 5
  52. Relate statistical dimension to number of POVM outcomes Study how

    anisotropy in Fisher information affects statistical dimension (C) $ |{Ej }| ? I 6/ I =) (C) =? There are several avenues we can pursue for future work. 4
  53. The geometry of state space affects resource requirements for maximum

    likelihood tomography because it affects the average number of parameters. (We’re still figuring out how, but the statistical dimension provides a good place to start!)
  54. 2 4 6 8 10 12 14 16 18 20

    d (Hilbert Space Dimension) 0 50 100 150 200 250 300 350 400 d (C) d2 1 2(d 1) Rank( r0) =1 (various colors) Rank( r0) = 2...9 Rank( r0) =10
  55. Let’s build intuition about this projection process. Where do these

    vectors get projected to? ⇧C( g ) ⌘ argmin x 2C || x g ||2 30
  56. Let’s build intuition about this projection process. Where do these

    vectors get projected to? ⇧C( g ) ⌘ argmin x 2C || x g ||2 29
  57. Numerical results indicate there’s interesting behavior to be explained… 2

    4 6 8 10 12 14 16 Hilbert Space Dimension d 0 2 4 6 8 10 12 hRank(ˆ ⇢MLE )i Average Rank of Maximum Likelihood Estimates 9 8 7 6 5 4 3 2 1
  58. …and maybe some concentration results? 2 4 6 8 10

    12 14 16 Hilbert Space Dimension d 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Rank(ˆ ⇢MLE ) Standard Deviation of Distribution 9 8 7 6 5 4 3 2 1 Concentration of statistical dimension -> concentration of ranks?
  59. Quantum State Tomography via Compressed Sensing David Gross,1 Yi-Kai Liu,2

    Steven T. Flammia,3 Stephen Becker,4 and Jens Eisert5 1Institute for Theoretical Physics, Leibniz University Hannover, 30167 Hannover, Germany 2Institute for Quantum Information, California Institute of Technology, Pasadena, California, USA 3Perimeter Institute for Theoretical Physics, Waterloo, Ontario, N2L 2Y5 Canada 4Applied and Computational Mathematics, California Institute of Technology, Pasadena, California, USA 5Institute of Physics und Astronomy, University of Potsdam, 14476 Potsdam, Germany (Received 21 October 2009; published 4 October 2010) We establish methods for quantum state tomography based on compressed sensing. These methods are specialized for quantum states that are fairly pure, and they offer a significant performance improvement on large quantum systems. In particular, they are able to reconstruct an unknown density matrix of dimension d and rank r using Oðrdlog2dÞ measurement settings, compared to standard methods that require d2 settings. Our methods have several features that make them amenable to experimental implementation: they require only simple Pauli measurements, use fast convex optimization, are stable against noise, and can be applied to states that are only approximately low rank. The acquired data can be used to certify that the state is indeed close to pure, so no a priori assumptions are needed. DOI: 10.1103/PhysRevLett.105.150401 PACS numbers: 03.65.Wj, 03.67.Àa The tasks of reconstructing the quantum states and processes produced by physical systems—known respec- tively as quantum state and process tomography [1]—are of increasing importance in physics and especially in quantum information science. Tomography has been used to characterize the quantum state of trapped ions [2] and an optical entangling gate [3] among many other implemen- tations. But a fundamental difficulty in performing tomo- minimum-rank matrix subject to linear constraints is NP- hard in general [8]. In addition to a reduction in experimental complexity, one might hope that a postprocessing algorithm taking as input only OðrdÞ ( d2 numbers could be tuned to run considerably faster than standard methods. Since the out- put of the procedure is a low-rank approximation to the density operator and only requires OðrdÞ numbers be PRL 105, 150401 (2010) P H Y S I C A L R E V I E W L E T T E R S week ending 8 OCTOBER 2010 Measure expectation values of tensor products of qubit Paulis Solve a convex optimization problem Provable bounds on sufficient number of outcomes 48