Algorithmic Cooling R.Laflamme

Total Page:16

File Type:pdf, Size:1020Kb

Algorithmic Cooling R.Laflamme Algorithmic cooling R.Laflamme In collaboration with: Guanru Feng Dawei Lu Nayeli Rodriguez-Briones Jonathan Baugh Jun Li Xinhua Peng Tal Mor Yossi Weinstein Improve and Benchmarking initial states Nuclear Overhauser Effect We have two spins, 13C H We irradiate one, the other one cools down!! Nuclear Overhauser Effect We have two spins, We irradiate one, the other one cools down!! When first proposed as a contributed paper at an APS meeting in April 1953, the proposal was met with much skepticism by a formidable array of physics talent. Included among these were notables such as: Felix Bloch (recipient of 1952 Physics Nobel Prize), Edward M. Purcell (recipient of Nobel Prize 1952 with Bloch and session chair), Isidor I. Rabi (recipient of Physics Nobel Prize, 1944) and Norman F. Ramsey (recipient of Physics Nobel Prize, 1989). Eventually everyone was won over. July 27, 1953 Dear Dr. Overhauser: You may recall that at the Washington Meeting of the Physical Society, when you presented your paper on nuclear alignment, Bloch, Rabi, Pearsall, and myself all said that we found it difficult to believe your conclusions and suspected that some fundamental fallacy would turn up in your argument. Subsequent to my coming to Brookhaven from Harvard for the summer, I have had occasion to see the manuscript of your paper. After considerable effort in trying to find the fallacy in your argument, I finally concluded that there was no fundamental fallacy to be found. Indeed, my feeling is that this provides a most intriguing and interesting technique for aligning nuclei. After considerable argument, I also succeeded in convincing Rabi and Bob Pound of the validity of your proposal and I have recently been told by Pound that he subsequently converted Pearsall shortly before Pound left for Europe. I hope that you will have complete success in overcoming the rather formidable experimental problems that still remain. I shall be very interested to hear of what success you have with the method. Sincerely, Norman F. Ramsey Threshold theorem A quantum computation can be as long as required with any desired accuracy using a reasonable amount of resources as long as the noise level is below a threshold value -6,-5,-4,-3,-2,... P < 10 Knill et al.; Science, 279, 342, 1998 Kitaev, Russ. Math Survey 1997 Aharonov & Ben Or, ACM press Preskill, PRSL, 454, 257, 1998 Significance: ... -imperfections and imprecisions are not fundamental objections to quantum computation -it gives criteria for scalability -its requirements are a guide for experimentalists -it is a benchmark to compare different technologies Ingredients for FTQEC ⌅ Parallel operations ⌅ Good quantum control ⌅ Ability to extract entropy ⌅ Knowledge of the noise No lost of qubits • Independent or quasi independent errors • Depolarising model • Memory and gate errors • ... • The need for nearly pure states For quantum error correction The initial state in NMR Benchmarking 1 βH 1 ⇢ = e− ( 1l βH + ...) Z ⇠ Z − Making a pseudo pure state (Cory et al 1996, Gershenfeld et al. 1997) C C C C C C R State State Prep. Meas. 1 1 β!n ( 1l βH) ( 1l ) Z − ! Z − 2n | ih | φ φ Encoding Gradient: Decoding Gradient:-2 Y:90 Fidelity Y:90 - X:90 -Y:90 - _ _ - i _ e i e _ _ 4 e _ - 4 - _ 4 _ i - _ i I Y 2 Y Y _ _ i 2 X _ 4 - Z _ _ 2 φ + _ _ e _ φ X X Z _ _ I I φ - 4 ( ( _ i ( = I ( X 2 X - _ X _ Z φ - X _ + 4 _ i Z ) ( + i I + Y I + i i + X ) X - ( ( Y I I Y I - - - - - ) Y Y Y Z ( ) ) Y Y I + + + + System Error Rate Reference i i Y Y I Ion Trap 0.000056 arxiv:1512.04600v1 (2015) - X Z ) ) ) Silicon (n) 0.0001 J. Phys.: Cond. Mat. 27 154205 (2015) Silicon (e−) 0.0005 J. Phys.: Cond. Mat. 27 154205 (2015) Superconducting 0.0008 Nature 508, 501 (2014) Ion Trap (single) 0.00002 PRA 83 030303(R) (2011) Neutral atoms 0.00014 NJP 12, 113007 (2010) Liquid-State NMR 0.00013 NJP 11 013034 (2009) Superconducting 0.011 PRL 102 090502 (2009) Ion Trap (crystal) 0.0008 QIC 9 920 (2009) Ion Trap (single) 0.00482 PRA 77 012307 (2008) ESR 0.007 Laflamme/Morton Algorithmic cooling polarization = ✏ = Tr[⇢Z] Sorenson, JMR, 1990 Schulman, Vazirani ACM 1999 Algorithmic cooling (PPA or partner pairing ) We have seen that we can cool a subset of spins by swapping states. For excample, with 3 spins, implementing a gate that swaps 011 100 will increase the order of the first spin at the expense of the last| i$ two.| Wei could concatenate this process to reach polarization of order 1. βH 1 ⇢ e− ( 1l β!(Z1 + Z2 + Z3)+...) ⇠ ⇠ 2n − 300 0 0 0 0 0 3000 0 0 0 0 010 0 0 0 0 0 0100 0 0 0 0 0001 0 0 0 0 01 00010 0 0 0 01 β! B000 10 0 0 0C β! B0001 0 0 0 0C ⇢d B − C ⇢d B C thermal ⇡ 8 B000 0 1 0 0 0C () pol ⇡ 8 B0000 10 0 0C B C B − C B000 0 0 10 0C B0000 0 10 0C B − C B − C |011> B000 0 0 0 10C B0000 0 0 10C B − C B − C |100> B000 0 0 0 0 3C B0000 0 0 0 3C B − C B − C @ A @ A d d 3 10 ⇢¯ = Tr 2,3⇢ β! pol pol ⇡ 4 0 1 ✓ − ◆ if want P~1 need ~ 1/ε^2 spins Algorithmic cooling (PPA or partner pairing ) We have seen that we can cool a subset of spins by swapping states. For excample, with 3 spins, implementing a gate that swaps 011 100 will | i$| i increase the order of the first spin at the expense of the last two. We could concatenate this process to reach polarization of order 1. βH 1 ⇢ e− ( 1l β!(Z1 + Z2 + Z3)+...) ⇠ ⇠ 2n − 300 0 0 0 0 0 3000 0 0 0 0 010 0 0 0 0 0 0100 0 0 0 0 0001 0 0 0 0 01 00010 0 0 0 01 β! B000 10 0 0 0C β! B0001 0 0 0 0C ⇢d B − C ⇢d B C thermal ⇡ 8 B000 0 1 0 0 0C () pol ⇡ 8 B0000 10 0 0C B C B − C B000 0 0 10 0C B0000 0 10 0C B − C B − C B000 0 0 0 10C B0000 0 0 10C B − C B − C B000 0 0 0 0 3C B0000 0 0 0 3C B − C B − C @ A @ A d d 3 10 ⇢¯ = Tr 2,3⇢ β! pol pol ⇡ 4 0 1 ✓ − ◆ if want P~1 need ~ 1/ε^2 spins Heat Bath Algorithmic Cooling 0 10 max 1 ∼ -1 ✏ 10 -2 10 Bath 3 qubits 4 qubits maximum polarization 5 qubits 6 qubits -3 10 -2 -1 0 1 2 Schulman, Mor, Weinstein 10 10 10 10 10 PRL 94, 120501 (2005). Scaled Heat-bath polarization ∼ n ✏b 2 n n Schulman/Mor/Weinstein ε<<1/2 => εmax~2 ε Moussa, Master thesis 2005 0 10 max 1 ∼ -1 HBAC ✏ 10 -2 10 3 qubits 4 qubits maximum polarization 5 qubits 6 qubits -3 10 -2 -1 0 1 2 10 10 10 10 10 Scaled Heat-bath polarization ∼ n ✏b 2 Moussa,Master thesis 2005 Schulman/Mor/Weinstein md md (1 + ✏b) (1 ✏b) ✏1l1 = − − . (1 + ✏ )md + (1 ✏ )md b − b if ✏ << m2n0; ✏ md✏ (= m2n0✏ ) N. Rodriguez Briones +RL, PRL 116, 170501, 2016 b 1l1 ⇡ b b 2 n0 if ✏b >> m2 ; ∆max =1 ✏1; ∆max 1l 1+✏b − ⇡ md ln 1 ✏ e − b +1 ⇣ ⌘ 0 10 max 1 ∼ -1 ✏ 10 -2 HBAC 10 3 qubits 4 qubits maximum polarization 5 qubits 6 qubits -3 10 -2 -1 0 1 2 10 10 10 10 10 Scaled Heat-bath polarization ∼ n ✏b 2 Moussa, Master thesis 2005 Schulman/Mor/Weinstein md md (1 + ✏b) (1 ✏b) ✏1l1 = − − . (1 + ✏ )md + (1 ✏ )md b − b For 2qubits: PPA, i.e. SORT + refresh qubits ✏ ✏ 1l1 ! b The deviation matrix |011> 300 0 0 0 0 0 010 0 0 0 0 0 |100> 0001 0 0 0 0 01 3 1 3 d β! B000 10 0 0 0C ⇢thermal 81l = ⇢thermal 8 B − C − ⇡ B000 0 1 0 0 0C B000 0 0 10 0C B C B000 0 0− 0 10C B − C B000 0 0 0 0 3C B − C @ A 200 0 000 0 ⇢2 d β! thermal ⇡ 4 0000 01 000 2 B C @ − A HBAC for 2 qubits ⇢˙ = i[H, ⇢]+ ⇢ − R ⇢ = γ↵ 2L↵⇢L† L† L↵⇢ ⇢L† L↵ R ↵ − ↵ − ↵ ↵ X ⇣ ⌘ Can get polarization enhancement for 2 qubits! Jun Li et al, PRA94, 012312, 2016 Approximation of Reachable Set for Coherently HBAC for 2 qubits Controlled Open Quantum Systems: Application to Quantum State Engineering, J. Li, D. Lu, Z. Luo, RL, X. Peng, and J. Du.l, PRA94, 012312, 2016 n 4n 1 1l − ⇢ = + rkBk 2Nn . Xk=0 P <0 Linblad => ~r˙ = ~r (~r r~ ) H − R − eq . P =0 2 Purity: P = Tr[⇢ ] P˙ = 2n+1~rT (~r r~ ) − R − eq 13C Experimental check H r ,r ,r ,r ,r ,r 0.0532, 0.0918, 0.0798, 0.0212, 0.0000, 0.0022 • { 1 2 3 4 5 6} ≈ { } r t=0=ZZ+4IZ r t=0=ZI+4Z 1/4 0 000 1/4 4 4 d ⎛ ZI ⎞ ⎡ (4r1 + 16r4)ε r1 r4 r5 ⎤ ⎛ ZI ⎞ 2 2 ⟨ ⟩ = − ⟨ ⟩ , dt ⎜ IZ ⎟ ⎢ (4r + 16r )ε r r r ⎥ ⎜ IZ ⎟ 0 0 ⎜ ⟨ ⟩ ⎟ ⎢ − 4 2 4 2 6 ⎥ ⎜ ⟨ ⟩ ⎟ ZI ⎜ ⎟ ⎢ ⎥ ⎜ ⎟ -2 IZ -2 ⎜ ZZ ⎟ ⎢ (4r5 + 16r6)ε r5 r6 r3 ⎥ ⎜ ZZ ⎟ ZZ ⎜ ⟨ ⟩ ⎟ ⎢ − ⎥ ⎜ ⟨ ⟩ ⎟ -4 -4 ⎝ (⎠a) ⎣ ⎦ ⎝ (b⎠ X \ A1 :(ϵ, 4ϵ, 0) 50 0 10 20 30 X 40\ 50 0 10 20 30 A2 : (0, 4.25ϵ, 0) s X \ s A3 : (1.87ϵ, 1.87ϵ, 1.87ϵ) A4 : (1.67ϵ, 1.67ϵ, 1.67ϵ) ê ê A5 : (1.53ϵ, 1.53ϵ, 1.53ϵ) Trajectory: NOE Trajectory: PPS 4ϵ Ry(90◦) Rx(90◦) S ( E A3 3 A4 x 2ϵ A5 P 0 A1 4ϵ A2 2ϵ 4ϵ 2ϵ 0 0 x1 x2 Nuclear Overhauser Effect (NOE) Solomon equations Cross-relaxation d Z1 h i = r ( Z1 Z1 ) r ( Z2 Z2 ), dt − 1 h ih i0 − 12 h ih i0 |11> Z1 Z1 + r12 Z2 0 r 0 Γ Γ2 Γ h i!h i 1 h i 1 1 |01> r (Γ Γ ) |10> 12 2 − 0 Γ = 0 Γ r1 (Γ0 +2Γ1 + Γ2) 1 Γ1 Solomon PR99, 559,1955 |00> ( ( Follow the deviation matrix ( Correlated ( 2 0 2 Flip qubit 2 Relaxation 0 2 2 0 -2 -2 ( -2 ( 0 ( -2 Γ1 Γ 2 |11> Γ1 X Γ2 ε 2 ε |00> This is a quantum information science version of the Nuclear Overhauser effect (NOE) Can do a little better… Γ1 Γ Γ 2 2 Γ1 X Γ1 X ( ( ( ( Qubit 2 Cross( 3 1 2 Flip Relaxation 1 3 3 … -1 -3 -3 ( -3 ( -1 ( -2 Polarization ε 3 ε Solid state NMR with Malonic acid Ensemble qubit Hamiltonian (13C) determined spectroscopically ó depends on crystal orientation C1(Hz) C2(Hz) Cm(Hz) T2*(ms) T1(s) C1 5693 237 828 2.4 162 C2 -- 1748 1020 2.6 326 Cm -- -- -3358 3.1 314 C1(kHz) C2(kHz) Cm(kHz) Hm1(kHz) Hm2(kHz) H1(kHz) H2(kHz) Hm1 1.9 1.7 3.5 -- -23 1.4 0.9 Hm2 -0.7 0.6 -46 -- -- 0.9 1.6 H1 2.3 0.2 0.4 -- -- -- 0.3 H2 0.2 1.4 0.5 -- -- -- -- Multiple Rounds of Algorithmic Cooling C2 C1 Cm Hm1/2 Hbath Refresh Thermal Contact Register Operation • By using heat-bath able to surpass 9 x 10 10 Shannon/Soresnsen bound of 1.5X C 1 C2 9 Cm heat-bath polarization 8 7 Polarization Boost 1.69X Boost 6 w.r.t.
Recommended publications
  • Heat Machines and Quantum Systems: Towards the Third Law
    Heat Machines and Quantum Systems: Towards the Third Law Thesis submitted for the degree of "Doctor of Philosophy" by Yair Rezek Submitted to the senate of the Hebrew University of Jerusalem October 2011 Heat Machines and Quantum Systems: Towards the Third Law Thesis submitted for the degree of "Doctor of Philosophy" by Yair Rezek Submitted to the senate of the Hebrew University of Jerusalem October 2011 This work was carried out under the supervision of prof. Ronnie Kosloff Acknowledgments I thank professor Kosloff for his guidance, and his endless willingness to aid and teach. I am also grateful to doctor Tova Feldman, Amikam Levi, and professor Peter Salamon for many fruitful discussions. ...Democritus says that [the soul] is hot, a sort of fire; for while there are infinitely many shapes, i.e. atoms, he says that the spherical ones compose fire and the soul ... because such shapes can most easily penetrate through everything and move the others, being themselves in motion. - Aristotle, De Anima (4th century BCE), showing that Democritus (5th century BCE) already understood heat as molecular (atomic) motion. The theory of thermodynamics is today, ca. 100 years after its modern formulation, a basic concept of physics. Its application makes the description and implementation of all sorts of physical and technical processes feasi- ble. Nevertheless, the derivation of basic principles of thermodynamics like, e.g., the famous second law, from more fundamental theories remains an intensively disputed problem until these days. Almost all approaches in that direction are based on classical Newtonian physics. Most of them require additional postulates like the ergodic hypothesis, the ”a priori postulate”, or the ”Stosszahlansatz” which are themselves not part of the Newtonian theory.
    [Show full text]
  • Quantum Noise Can Enhance Algorithmic Cooling
    Quantum noise can enhance algorithmic cooling Zahra Farahmand,1 Reyhaneh Aghaei Saem,1 and Sadegh Raeisi1, ∗ 1Department of Physics, Sharif University of Technology, Tehran, Iran Heat-Bath Algorithmic Cooling techniques (HBAC) are techniques that are used to purify a target element in a quantum system. These methods compress and transfer entropy away from the target element into auxiliary elements of the system. The performance of Algorithmic Cooling has been investigated under ideal noiseless conditions. However, realistic implementations are imperfect and for practical purposes, noise should be taken into account. Here we analyze Heat-Bath Algorithmic Cooling techniques under realistic noise models. Surprisingly, we find that noise can in some cases enhance the performance and improve the cooling limit of Heat-Bath Algorithmic Cooling techniques. We numerically simulate the noisy algorithmic cooling for the two optimal strategies, the Partner Pairing, and the Two-sort algorithms. We find that for both of them, in the presence of the generalized amplitude damping noise, the process converges and the asymptotic purity can be higher than the noiseless process. This opens up new avenues for increasing the purity beyond the heat-bath algorithmic cooling. Most quantum applications require pure qubits. ments of the density matrix for compression. They However, it is not always easy to prepare pure quan- showed that sort is the optimal compression oper- tum states. For instance, for spin qubits, the states ation for HBAC. They also proved that even this are often close to maximally mixed states. optimal technique cannot always converge to a com- Heat-Bath Algorithmic Cooling techniques pletely pure state.
    [Show full text]
  • Experimental Heat-Bath Cooling of Spins*
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector Eur. Phys. J. Plus (2014) 129: 266 THE EUROPEAN DOI 10.1140/epjp/i2014-14266-0 PHYSICAL JOURNAL PLUS Regular Article Experimental heat-bath cooling of spins G. Brassard1,2,3, Y. Elias4, J.M. Fernandez5, H. Gilboa6,J.A.Jones7,T.Mor4, Y. Weinstein4,a, and L. Xiao7 1 D´epartement IRO, Universit´edeMontr´eal, Montr´eal (QC), H3C 3J7, Canada 2 The Canadian Institute for Advanced Research, Toronto, Canada 3 Institute for Theoretical Studies at ETH, Z¨urich, Switzerland 4 Department of Computer Science, Technion, Haifa 32000, Israel 5 D´epartement de g´enie informatique, Ecole´ Polytechnique de Montr´eal, Montr´eal (QC), H3C 3A7, Canada 6 Department of Chemistry, Technion, Haifa 32000, Israel 7 Centre for Quantum Computation, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX13PU, UK Received: 3 May 2014 Published online: 16 December 2014 c The Author(s) 2014. This article is published with open access at Springerlink.com Abstract. Algorithmic cooling (AC) is a method to purify quantum systems, such as ensembles of nuclear spins, or cold atoms in an optical lattice. When applied to spins, AC produces ensembles of highly po- larized spins, which enhance the signal strength in nuclear magnetic resonance (NMR). According to this cooling approach, spin-half nuclei in a constant magnetic field are considered as bits, or more precisely quantum bits, in a known probability distribution. Algorithmic steps on these bits are then translated into specially designed NMR pulse sequences using common NMR quantum computation tools.
    [Show full text]
  • Algorithmic Quantum Heat Engines
    PHYSICAL REVIEW E 100, 012109 (2019) Algorithmic quantum heat engines Emre Köse,1 Selçuk Çakmak,2 Azmi Gençten,3 Iannis K. Kominis,4 and Özgür E. Müstecaplıoglu˘ 1,* 1Department of Physics, Koç University, 34450 Sariyer, Istanbul,˙ Turkey 2Department of Software Engineering, Samsun University, 55420 Samsun, Turkey 3Department of Physics, Ondokuz Mayıs University, 55139 Samsun, Turkey 4Department of Physics, University of Crete, 70013 Heraklion, Greece (Received 23 January 2019; revised manuscript received 14 June 2019; published 8 July 2019) We suggest alternative quantum Otto engines, using heat bath algorithmic cooling with a partner pairing algorithm instead of isochoric cooling and using quantum SWAP operations instead of quantum adiabatic processes. Liquid state nuclear magnetic resonance systems in a single entropy sink are treated as working fluids. The extractable work and thermal efficiency are analyzed in detail for four-stroke and two-stroke types of alternative quantum Otto engines. The role of the heat bath algorithmic cooling in these cycles is to use a single entropy sink instead of two so that a single incoherent energy resource can be harvested and processed using an algorithmic quantum heat engine. Our results indicate a path to programmable quantum heat engines as analogs of quantum computers beyond traditional heat engine cycles. We find that for our NMR system example implementation of quantum algorithmic heat engine stages yields more power due to increased cycle speeds. DOI: 10.1103/PhysRevE.100.012109 I. INTRODUCTION ization is much slower, reducing the power output of the NMR machines. With the advances of miniaturized information and energy Here, we propose to replace adiabatic steps by SWAP op- devices, the question of whether using a quantum system erations while replacing the cooling step by an algorithmic to harvest a classical resource can have an advantage over cooling [32–45].
    [Show full text]
  • Algorithmic Cooling in Liquid State NMR (Pdf)
    Algorithmic Cooling in Liquid-state NMR Yuval Elias / Gilles Brassard Group NTRQ Meeting November 2015 Château Bromont Algorithmic Cooling in Liquid-state NMR Yuval Elias / Gilles Brassard Group NTRQ Meeting November 2015 Château Bromont Bonne-Fête Anniversaire Gilles! 15 15 15 15 http://menoummm.centerblog.net/6123670-Bonne-Fete-Anniversaire- https://s3.amazonaws.com/uploads.startups.fm/wp-content/uploads/ 2013/08/erwin-schrodinger.jpg Overview Novel Spin Cooling from Quantum Computing Spin cooling: enhancing polarization in NMR Polarization compression: generalized polarization transfer Open systems: heat-bath cooling, algorithmic cooling Heat-bath Cooling Experiments Model system: 13C-labeled trichloroethylene (TCE) 13C-labeled glutamate and glycine (Glu, Gly) Algorithmic Cooling Experiments AC in solid-state NMR AC in liquid-state NMR - building blocks AC in liquid-state NMR – process 1 (cooling C1) AC in liquid-state NMR – processes 2,3 (cooling C2, C3) Future prospects – Carbon-based Brain Spectroscopy Outline The advent of NMR quantum computing in the late 1990s led to renewed interest in entropy manipulations for spin cooling. Spin Cooling – Enhancing Polarization in NMR Polarization bias : excess polarization of spins aligned with field 1 2 0 EEEE T 0 hB 0 1 tanh2kT 2 kT 2 kT 0 2 Effective spin temperature: for any bias (also non-thermal) 11.7T (500 MHz), 25°C ~ 10-5 Spin-cooling: transient increase in polarization, beyond thermal Abragam and Goldman, Nuclear Magnetism: Order and Disorder (1982) Polarization Compression Shannon’s bound on total entropy Shannon, Bell Syst. Tech. J. (1948) H( X ) p log 1 i 2 pi Coin flip: H(fair) = 1; H(¼,¾) = 0.811; H(heads) = 0 Fair biased coin: flip twice, use first toss if different J.
    [Show full text]
  • Algorithmic Cooling of Spins: a Practicable Method for Increasing Polarization
    Algorithmic Cooling of Spins: A Practicable Method for Increasing Polarization Jos´eM. Fernandez1, Seth Lloyd2, Tal Mor3, and Vwani Roychowdhury4 1. D´epartement de g´enie informatique, Ecole´ Polytechnique de Montr´eal, Montr´eal, Canada 2. Department of Mechanical Engineering, MIT, Cambridge, USA 3. Computer Science Department, Technion, Haifa, Israel. 4. Electrical Engineering Department, UCLA, Los Angeles, CA, USA. 21 January 2004 An efficient technique to generate ensembles of spins that are highly polarized by external magnetic fields is the Holy Grail in Nuclear Magnetic Resonance (NMR) spectroscopy. Since spin-half nuclei have steady-state polarization biases that increase inversely with temperature, spins exhibiting high polarization biases are con- sidered cool, even when their environment is warm. Existing spin- cooling techniques [1, 2, 3, 4] are highly limited in their efficiency and usefulness. Algorithmic cooling [5] is a promising new spin- cooling approach that employs data compression methods in open systems. It reduces the entropy of spins on long molecules to a arXiv:quant-ph/0401135v2 23 Jan 2004 point far beyond Shannon’s bound on reversible entropy manipula- tions (an information-theoretic version of the 2nd Law of Thermo- dynamics), thus increasing their polarization. Here we present an efficient and experimentally feasible algorithmic cooling technique that cools spins to very low temperatures even on short molecules. This practicable algorithmic cooling could lead to breakthroughs in high-sensitivity NMR spectroscopy in the near future, and to the development of scalable NMR quantum computers in the far future. Moreover, while the cooling algorithm itself is classical, it uses quantum gates in its implementation, thus representing the first short-term application of quantum computing devices.
    [Show full text]
  • Heat Bath Algorithmic Cooling with Spins: Review and Prospects
    Heat Bath Algorithmic Cooling with Spins: Review and Prospects Daniel K. Park1, 2, Nayeli A. Rodriguez-Briones1, 2, Guanru Feng1, 2, Robabeh R. Darabad1, 2, Jonathan Baugh1, 2, 3 and Raymond Laflamme1, 2, 4, 5 1Institute for Quantum Computing, 2Department of Physics and Astronomy, and 3Department of Chemistry, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 4Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada N2J 2W9 5Canadian Institute for Advanced Research, Toronto, Ontario, Canada M5G 1Z8 1. Introduction Quantum Information Processing (QIP) has the potential to perform exponentially faster computation and revolutionize current technology by harnessing the systems governed by the laws of quantum mechanics. To reach this goal we need to be able to control imperfections and imprecision occurring when theoretical ideas are implemented in the physical world. Quantum error correction is a theory that aims to bridge this. The first steps towards developing experimental quantum error correction have been taken but there is still more to be done. Application of multiple rounds of Quantum Error Correction (QEC) is an essential milestone towards the construction of scalable QIP devices, but experimental realizations of it are still in their infancy. The requirements for multiple round QEC are high control fidelity and the ability to extract entropy from ancilla qubits. Nuclear Magnetic Resonance (NMR) based quantum devices have demonstrated high control fidelity with up to 12 qubits. Hence, these devices are excellent test beds that can be explored in the lab today for the ideas of quantum control and QEC. More details on NMR QIP can be found in [1,2].
    [Show full text]
  • Note to Users
    NOTE TO USERS This reproduction is the best copy available. Techniques for Quantum Computing State Generation, Discrete Logarithms in Elliptic Curve Groups, Reliable Global Control Schemes and Algorithmic Cooling by Phillip R. Kaye A thesis presented to the University of Waterloo in ful¯llment of the thesis requirement for the degree of Doctor of Philosophy in Computer Science Waterloo, Ontario, Canada, 2007 °c Phillip R. Kaye, 2007 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required ¯nal revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. Phillip R. Kaye ii Abstract This thesis is about techniques for quantum computing. A common theme throughout this work is the examination of how quantum algorithms and protocols might be imple- mented in practice. I explore this question at the level of algorithmic details and computer architecture, and not at the level of speci¯c physical systems for performing quantum computation. The ¯rst problem I consider is the generation of quantum states. Many results in quantum information theory require the generation of speci¯c quantum states, such as Bell states. Some states can be e±ciently created using standard quantum computational primitives such as preparing a qubit in the state j0i and applying a sequence of quantum gates (from a ¯nite set). For example, a Bell state can be prepared from the state j0ij0i using a Hadamard gate and a controlled-not gate. However, many states cannot be e±ciently created.
    [Show full text]
  • Introduction to Quantum Algorithms for Physics and Chemistry
    Introduction to Quantum Algorithms for Physics and Chemistry The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Yung, M.-H., Whitfield, J. D., Boixo, S., Tempel, D. G. and Aspuru- Guzik, A. (2014) Introduction to Quantum Algorithms for Physics and Chemistry, in Quantum Information and Computation for Chemistry: Advances in Chemical Physics Volume 154 (ed S. Kais), John Wiley & Sons, Inc., Hoboken, New Jersey. doi: 10.1002/9781118742631.ch03 Published Version 10.1002/9781118742631.ch03 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:11084801 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP Introduction to Quantum Algorithms for Physics and Chemistry Man-Hong Yung1, James D. Whitfield2;3, Sergio Boixo1;4, David G. Tempel5, and Alan Aspuru-Guzik1 March 8, 2012 Abstract An enormous number of model chemistries are used in computational chemistry to solve or approximately solve the Schr¨odingerequation; each with their own drawbacks. One key limitation is that the hardware used in computational chemistry is based on classical physics, and is often not well suited for simulating models in quantum physics. In this review, we focus on applications of quantum computation to chemical physics problems. We describe the algorithms that have been proposed for the electronic- structure problem, the simulation of chemical dynamics, thermal state preparation, density functional theory and adiabatic quantum simulation.
    [Show full text]
  • Experimental Quantum Error Correction
    Experimental Quantum Error Correction Raymond Laflamme Institute for Quantum Computing www.iqc.ca lafl[email protected] Help from : Martin Laforest, Osama Moussa other colleagues at IQC Plan The need for quantum error correction and the accuracy threshold theorem Characterising noise Characterising and demonstrating control • Implementing error correcting codes, noiseless subspaces and subsystems • Implementing encoded gates Extracting entropy, algorithmic cooling Conclusion The death of QComputersÊ(1995)Ê Threshold theorem A quantum computation can be as long as required with any desired accuracy as long as the noise level is below a threshold value -6,-5,-4,...,-1? P < 10 Knill et al.; Science, 279, 342, 1998 Kitaev, Russ. Math Survey 1997 Aharonov & Ben Or, ACM press Significance: Preskill, PRSL, 454, 257, 1998 -imperfections and imprecisions are not fundamental objections to quantum computation -it gives criteria for scalability -its requirements are a guide for experimentalists -it is a benchmark to compare different technologies 16 Ingredients for FTQEC Knowledge of the noise Good quantum control Ability to extract entropy Parallel operations Characterising noise in q. systems Process tomography: X † X ρf = AkρiAk = χklPkρiPl k kl For one qubit, 12 parameters are required as de- scribed by the evolution of the Bloch sphere: For n qubits, we need to provide 42n−4n numbers to do so. 6 Coarse graining • We are not interested in all the elements that describe the full noise superopeartor but only a coarse graining of them. • If we are interested in implementing quantum error corrrection, we can ask what is the probbaility to get one, or two, or k qubit error, independent of the location and independent of teh type of error σx,y,z.
    [Show full text]
  • Algorithmic Cooling and Scalable NMR Quantum Computers
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CERN Document Server Algorithmic Cooling and Scalable NMR Quantum Computers∗ 1 1;2 1 P. Oscar Boykin ,TalMor ∗, Vwani Roychowdhury , Farrokh Vatan1;3, and Rutger Vrijen4 1. Electrical Engineering Department, UCLA, Los Angeles, CA 90095, USA. 2. Electrical Engineering Department, College of Judea and Samaria, Ariel, Israel. 3. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive Pasadena, CA 91109 4. Sun Microsystems Laboratories, Mountain View, CA, USA * To whom correspondence should be addressed. Email: [email protected] Abstract We present here algorithmic cooling (via polarization-heat-bath)—a powerful method for obtain- ing a large number of highly polarized spins in liquid nuclear-spin systems at finite temperature. Given that spin-half states represent (quantum) bits, algorithmic cooling cleans dirty bits beyond the Shan- non’s bound on data compression, by employing a set of rapidly thermal-relaxing bits. Such auxiliary bits could be implemented using spins that rapidly get into thermal equilibrium with the environment, e.g., electron spins. Cooling spins to a very low temperature without cooling the environment could lead to a break- through in nuclear magnetic resonance experiments, and our “spin-refrigerating” method suggests that this is possible. The scaling of NMR ensemble computers is probably the main obstacle to building useful quantum computing devices, and our spin-refrigerating method suggests that this problem can be resolved. ∗This work was sponsored in part by the Defense Advanced Research Projects Agency (DARPA) project MDA972–99–1– 0017 [note that the content of this paper does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred], and in part by the U.S.
    [Show full text]
  • Theoretical Methods (*) and Experimental Implementation (**) of Heat-Bath Algorithmic Cooling
    Theoretical Methods (*) and Experimental Implementation (**) of Heat-Bath Algorithmic Cooling (*) José M. Fernandez 5, Seth Lloyd 2, Tal Mor 3, Vwani Roychowdhury 4 (**) Gilles Brassard 1, José M. Fernandez 5, Raymond Laflamme 6, Tal Mor 3, Yossi Weinstein 3 1. Université de Montréal 4. UCLA 2. MIT 5. École Polytechnique de Montréal (work done while at U de M) 3. Technion 6. Perimeter/Waterloo Abstract Algorithmic Cooling is a generic term for techniques leading to the purification of qubit registers whose initial state is not pure. This term encompasses polarisation transfer techniques of NMR (pre-dating both QC and NMR-based QC) and also in-place quantum compression schemes such as those of Schulman and Vazirani. The entropy conservation law imposes, however, severe restrictions on the efficiency of such techniques, often referred to as the Shannon Bound. In addition, the necessity that all transformations applied be unitary (i.e. inner-product preserving), imposes an even tighter bound, referred to as the Sørensen Bound. A new kind of algorithmic cooling which bypasses these bounds by pumping out excess entropy into the environment (i.e. heat bath) has been proposed in 1999 by Boykin, Mor, Roychowdhury, Vatan, and Vrijen and has been referred to as "non-adiabatic" or "heat- bath" algorithmic cooling. This poster presents an improved non-adiabatic cooling algorithm, more efficient in terms of the number of required qubits, which achieves an exponential increase in polarisation with a linear number of qubits. Furthermore, we report on the first proof-of-concept non-adiabatic cooling experiment ever, performed in April 2002 at the Université de Montréal on a 3-spin molecule.
    [Show full text]